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tensorvijay/19015_a6_18cn711.ipynb

Created September 23, 2023 16:29
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19015_a6_18cn711.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"mount_file_id": "12eMvBRdUrNcgc5TvO0H775hFqJPUzOfa",
"authorship_tag": "ABX9TyNqfGZwwe1U0/7DRf3Ap7J8",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/tensorvijay/08b68a0c7229faa9a6acacbb7cfef82c/19015_a6_18cn711.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7WqICYHHaTds",
"outputId": "6460f2e6-783f-44cb-af7d-49f0894c0e2d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"!unzip '/content/drive/My Drive/Thirdsem/DLforA/Assign_6/COVID19_dataset.zip'"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: /content/drive/My Drive/Thirdsem/DLforA/Assign_6/COVID19_dataset.zip\n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (1290).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (134).png \n",
" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (1340).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (14).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (17).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (811).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (813).png \n",
" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (814).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (83).png \n",
" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (830).png \n",
" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (831).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (836).png \n",
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" inflating: COVID-19 Radiography Database/Viral Pneumonia/Viral Pneumonia (84).png \n",
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],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FjBEtwlUctn8"
},
"source": [
"import glob as glob\n",
"from keras.layers import Conv2D, MaxPooling2D , Dropout, concatenate,UpSampling2D\n",
"from keras.optimizers import Adam\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import glob as glob\n",
"import PIL.Image as im\n",
"from skimage.transform import resize\n",
"from skimage.transform import rotate\n",
"from skimage.color import rgb2gray\n",
"from skimage.color import gray2rgb\n",
"import random as rand\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"import keras\n",
"from keras.preprocessing.image import load_img\n",
"from keras.preprocessing.image import img_to_array\n",
"from keras.applications.vgg16 import preprocess_input\n",
"from keras.applications.vgg16 import decode_predictions\n",
"from keras.applications.vgg16 import VGG16\n",
"from keras.models import Model\n",
"from pickle import dump\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.decomposition import PCA"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qQvAJSnYdCLM"
},
"source": [
"def readImage(address):\n",
" x = im.open(address)\n",
" x = np.array(x)\n",
" x = resize(x,(128,128))\n",
" x = gray2rgb(x)\n",
" return(x)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XB50jr3BdWwD"
},
"source": [
"COV=glob.glob('/content/COVID-19 Radiography Database/COVID-19/*.png')\n",
"NOR=glob.glob('/content/COVID-19 Radiography Database/NORMAL/*.png')\n",
"VIR=glob.glob('/content/COVID-19 Radiography Database/Viral Pneumonia/*.png')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LVRPmYV5f0xV"
},
"source": [
"A=readImage('/content/COVID-19 Radiography Database/COVID-19/COVID-19 (10).png')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "F_8hq-DjgDMG",
"outputId": "ad516de7-f18f-4df0-8117-81f4bfc76138",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"A.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(128, 128, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jGLs861JgOux"
},
"source": [
"x_train=[]\n",
"for i in COV :\n",
" x_train.append(kerasimg(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2qisK7bcjY-Z"
},
"source": [
"Loading the images into the respective files, after that load the images into an image data generator and"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uI5uUoNhXPnu"
},
"source": [
"for i in VIR :\n",
" x_train.append(kerasimg(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rysqLESlXkh1",
"outputId": "0310f1d9-94b8-4b1d-895a-bfe0fa7046b3",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(x_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1564"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x9BfOxNjXZ7U"
},
"source": [
"After loading the Viral images let us extract the features using VGG 16 Pre trained weights"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9hPI5uKs3mAA"
},
"source": [
"def kerasimg(ad):\n",
" image = load_img(ad, target_size=(224, 224,3))\n",
" image = img_to_array(image)\n",
" image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n",
" image = preprocess_input(image)\n",
" return image\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1-UmjiGs4F5K",
"outputId": "1c9a991a-955f-44d7-feb3-5b410865a44f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"model = VGG16()\n",
"# remove the output layer\n",
"model = Model(inputs=model.inputs, outputs=model.layers[-2].output)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
"553467904/553467096 [==============================] - 26s 0us/step\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Qq9jNtbk4gtA",
"outputId": "bae30dad-ada5-4c8e-9321-ddf2ff504acd",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(x_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"219"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XFMhPci74PXS"
},
"source": [
"abnorm_feat=[]\n",
"for i in x_train :\n",
" abnorm_feat.append(model.predict(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UwRK_KS14btK",
"outputId": "d5841400-26ad-4d25-822a-7d9c613cff71",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(abnorm_feat)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1564"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "eH-_4KGM4qqt"
},
"source": [
"abnorm_fa=np.asarray(abnorm_feat)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YcoEeK_9aX-1"
},
"source": [
"abnorm_fa=abnorm_fa.reshape(1564,4096)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "AUpTPBXhaouF",
"outputId": "51a063f5-b5db-4f8a-b432-7d7c6d13abf8",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"abnorm_fa.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1564, 4096)"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1K_Xlo8bZq73"
},
"source": [
"pca=PCA(n_components=1521)\n",
"dimen=pca.fit_transform(abnorm_fa)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OXoNJni4bA0w",
"outputId": "c64622b7-7acf-46b4-8bbe-55b4f7367472",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"dimen.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1564, 1521)"
]
},
"metadata": {
"tags": []
},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aEho3YbQcZHL",
"outputId": "2c86c9e7-b4cc-4d53-9a23-459a64577c4c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"kmeans=KMeans(n_clusters=2)\n",
"kmeans.fit(dimen)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7_ZXFdFJcb8t"
},
"source": [
"lab=kmeans.labels_"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "cmAv2uzqdIZ6"
},
"source": [
"clust1=dimen[lab==0] #abnormal 1\n",
"clust2=dimen[lab==1] #abnormal 2"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "N17A2g1cdksd"
},
"source": [
"Divide the data set into two cluster , this becomes the abnormal 1 and abnormal 2"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D8pCb3g1dhRG",
"outputId": "51ed40f3-50b5-49c1-d979-b26ea9c40664",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"clust2.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(911, 1521)"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AbkRa-n7d8ub"
},
"source": [
"Now reshape the array of clusters into to parts abnormal 1 and abnormal 2 as given in the paper"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nSqP9XZ9dtC2"
},
"source": [
"clust1_=clust1.reshape(653,39,39)\n",
"clust2_=clust2.reshape(911,39,39)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZgRixMCGfOwT"
},
"source": [
"example image of the features"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CZNEx6axfPUR",
"outputId": "105ca1b5-348b-4350-e5ac-ecc4dd297882",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
}
},
"source": [
"plt.imshow(clust2_[100])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f089d690940>"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "moMRcMGbezVO"
},
"source": [
"As you can see dimensionality reduction does not help much the features are not evidently visible, so instead of reducing the dimensionality let us try plotting the original image"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EWZQag3SeyLB",
"outputId": "c693f14e-6881-451c-9d30-340e1e3b0272",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"image=abnorm_fa[0].reshape(64,64)\n",
"plt.imshow(image)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f089d703908>"
]
},
"metadata": {
"tags": []
},
"execution_count": 67
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-JkIu5OWmblQ"
},
"source": [
"The features in the above image are better , than the one after dimensionality reduction.As a reuslt we will apply K means on the feature space of the orinignal image and not implement the dimensionality reducion. This is possibly becasue the size of our data set is very small and hence the number of principal components becomes extremely small"
]
},
{
"cell_type": "code",
"metadata": {
"id": "OMbCxytym9yq"
},
"source": [
"kmeans_=KMeans(n_clusters=2)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PncFYbqJ5A6i",
"outputId": "ed84d0af-02c5-4edf-c8a0-dcc4ae044421",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"\n",
"kmeans_.fit(abnorm_fa)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3Dytc_kB5PoT"
},
"source": [
"labels=kmeans_.labels_"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gkkh9y_PngiD"
},
"source": [
"clus1=abnorm_fa[labels==0]\n",
"clus2=abnorm_fa[labels==1]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sMjIptw9nrCw",
"outputId": "324b3b34-50cd-43f1-f310-7c74aff53e80",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(clus2)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"653"
]
},
"metadata": {
"tags": []
},
"execution_count": 74
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gaJ8wjVeoKlN"
},
"source": [
"THe number of clusters for both the cases are the same"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UZq_RjoroJhp"
},
"source": [
"clus1_=clus1.reshape(911,64,64)\n",
"clus2_=clus2.reshape(653,64,64)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XGqiVLCnohin"
},
"source": [
"x_train=[]\n",
"for i in clus1_ :\n",
" x_train.append(gray2rgb(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "05uyjDGLpSa2",
"outputId": "50d5a47a-f0bf-4689-e015-551214fe67ab",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 304
}
},
"source": [
"plt.imshow(clus1_col[901])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f08a7d5e978>"
]
},
"metadata": {
"tags": []
},
"execution_count": 81
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5ZHXwUaLxS8-",
"outputId": "21d650cc-5878-4797-947c-fcd7d43a84b7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(clus1_col)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"911"
]
},
"metadata": {
"tags": []
},
"execution_count": 110
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vLoevL9CxmXI",
"outputId": "9b5d97e9-b9cc-497d-b7d1-9e5b5f8ff0a8",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(clus2_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"653"
]
},
"metadata": {
"tags": []
},
"execution_count": 111
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ixVc6U31p6a5"
},
"source": [
"\n",
"for i in clus2_ :\n",
" x_train.append(gray2rgb(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "J9e4_7D9qEp0",
"outputId": "455d9e29-aa7f-4282-c9ce-5d7210deafcf",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(x_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1564"
]
},
"metadata": {
"tags": []
},
"execution_count": 124
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZzX4Sdcnrx83",
"outputId": "13bbf36f-d694-4893-90d4-c6cded85aa0f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 304
}
},
"source": [
"plt.imshow(clus2_col[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f089c4c0710>"
]
},
"metadata": {
"tags": []
},
"execution_count": 89
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ta2GcyVfppKX"
},
"source": [
"Let us try the normal case now"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a7u3mvMtqDCq"
},
"source": [
"x_nor=[]\n",
"for i in NOR :\n",
" x_nor.append(kerasimg(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7SEyj_V-q2gH"
},
"source": [
"norm_feat=[]\n",
"for i in x_nor :\n",
" norm_feat.append(model.predict(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iG6-NcCatJgG"
},
"source": [
"norm_feat=np.asarray(norm_feat)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KXtViKgTtXzp",
"outputId": "a62179f6-3346-425d-a4e2-65fe01959a53",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(norm_feat)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1341"
]
},
"metadata": {
"tags": []
},
"execution_count": 98
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7v67LstutT42"
},
"source": [
"norm_feat=np.asarray(norm_feat).reshape(1341,4096)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GxvxZTRTsTXT",
"outputId": "d057159f-bed8-4a09-bab1-caf863f3da0c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"kmeans1=KMeans(n_clusters=2)\n",
"kmeans1.fit(norm_feat)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 102
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pZvNG-UNwOHH"
},
"source": [
"labels1=kmeans1.labels_"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NT2-1KdywN4B"
},
"source": [
"nclus1=norm_feat[labels1==0]\n",
"nclus2=norm_feat[labels1==1]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5iIXQOqBx1ZQ",
"outputId": "887a51a8-d4f1-4cf0-9e0c-1b38091c597b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(nclus1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"659"
]
},
"metadata": {
"tags": []
},
"execution_count": 113
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "14z1qWjEx3jz",
"outputId": "78ee492a-4aa9-460c-e25c-2db3118a0c61",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(nclus2)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"682"
]
},
"metadata": {
"tags": []
},
"execution_count": 114
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "P3G5lmy6zFTi"
},
"source": [
"nclus1_=nclus1.reshape(659,64,64)\n",
"nclus2_=nclus2.reshape(682,64,64)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xnAG8EV3wo2R"
},
"source": [
"\n",
"for i in nclus1_ :\n",
" x_train.append(gray2rgb(i))\n",
"for i in nclus2_ :\n",
" x_train.append(gray2rgb(i))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Oh63Yxn6xMbm"
},
"source": [
"y_train1=np.repeat(0,len(clus1_))\n",
"y_train=np.repeat()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Pm8IlT9byT2L"
},
"source": [
"y_train=np.concatenate((np.repeat(0,len(clus1_)),np.repeat(1,len(clus2_)),np.repeat(2,len(nclus1)),np.repeat(3,len(nclus2))))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bMdxwuzd0KSZ",
"outputId": "423d0a18-7d73-416b-a169-1d700f4d509f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(x_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2905"
]
},
"metadata": {
"tags": []
},
"execution_count": 126
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UPlsl-LS0XXX"
},
"source": [
"world of the CNN"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UnDMEbcN0M4t",
"outputId": "1b1ccc8e-17ff-4bc7-8a53-860bce58a6f8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 304
}
},
"source": [
"plt.imshow(x_train[2341])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f089cd622b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 128
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ljz1rGrgzl-O",
"outputId": "46e1064b-304a-41ac-92d6-7e15c62cdca4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2905"
]
},
"metadata": {
"tags": []
},
"execution_count": 121
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qD4nH_J61Ypz"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"from keras.preprocessing.image import ImageDataGenerator\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FR6CecdTABsE"
},
"source": [
"from keras.callbacks import ModelCheckpoint"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yndBWncx0Z_O"
},
"source": [
"np.savez_compressed('/content/drive/My Drive/Thirdsem/DLforA/Assign_6/train.npz',x_train,y_train)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "y5JFjRsx28ga"
},
"source": [
"X=[]\n",
"for i in x_train:\n",
" X.append(resize(i,(80,80)))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tqgOcJ804iN1",
"outputId": "a750ef4e-314c-4497-e02a-3ce870510596",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"X[20].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(80, 80, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 141
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "m0-_XyFJ4phP"
},
"source": [
"from keras.utils.np_utils import to_categorical"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sQYT9QVq4unc"
},
"source": [
"Y=to_categorical(y_train,num_classes=4)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DvDcCVEG40_z",
"outputId": "b4824e30-02fc-401a-d6df-bb6f2a921118",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(Y)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2905"
]
},
"metadata": {
"tags": []
},
"execution_count": 144
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n5LqQoH202Kx"
},
"source": [
"X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.33, random_state=42)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PHRs7oMj5D-1"
},
"source": [
"_,X_val,_,Y_val=train_test_split(X_train,Y_train,test_size=0.33,random_state=42)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Kf9wa6Rs6zGB"
},
"source": [
"X_train=np.asarray(X_train)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wn-Cu6sG7Cft"
},
"source": [
"X_val=np.asarray(X_val)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jzVAxVIA6NrK",
"outputId": "0a877e00-f8e1-40c0-dec5-84cdac75bd15",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"Y_val.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(643, 4)"
]
},
"metadata": {
"tags": []
},
"execution_count": 156
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "k0xtNf6y1_ZH"
},
"source": [
"aug = ImageDataGenerator(\n",
"\t\trotation_range=20,\n",
"\t\tzoom_range=0.15,\n",
"\t\twidth_shift_range=0.2,\n",
"\t\theight_shift_range=0.2,\n",
"\t\tshear_range=0.15,\n",
"\t\thorizontal_flip=True,\n",
"\t\tfill_mode=\"nearest\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hTeyh1pH2FVg"
},
"source": [
"filepath=\"/content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-{epoch:02d}-{accuracy:.2f}.hdf5\"\n",
"checkpoint = ModelCheckpoint(filepath, monitor='accuracy', verbose=1, save_best_only=True, mode='max')\n",
"callbacks_list = [checkpoint]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uvoQ9nzl2gGT",
"outputId": "cc1eb780-2da0-463f-885f-09f10e217607",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"base_model = keras.applications.InceptionResNetV2(\n",
" weights='imagenet', # Load weights pre-trained on ImageNet.\n",
" input_shape=(80, 80, 3),\n",
" include_top=False) # Do not include the ImageNet classifier at the top."
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
"219062272/219055592 [==============================] - 5s 0us/step\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RQUG33db5-ft"
},
"source": [
"from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten\n",
"from keras.optimizers import SGD"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qNqj7adp5z4-"
},
"source": [
"model=keras.Sequential()\n",
"model.add(base_model)\n",
"model.add(Flatten())\n",
"model.add(Dense(4,activation='sigmoid'))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "s8xu9qHp6Bgc",
"outputId": "3b4129bf-e444-47f8-c61a-ba58c02aab1a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"model.summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"inception_resnet_v2 (Functio (None, 1, 1, 1536) 54336736 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 1536) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 4) 6148 \n",
"=================================================================\n",
"Total params: 54,342,884\n",
"Trainable params: 53,684,932\n",
"Non-trainable params: 657,952\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_9VidvuG6HU1"
},
"source": [
"opt = SGD(lr=0.001, momentum=0.9)\n",
"model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LHgSTzSW6cc2",
"outputId": "7777dbe7-447c-45df-aa4f-f2f3f55971b6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"H = model.fit(\n",
"\tx=aug.flow(X_train, Y_train, batch_size=32),\n",
"\tsteps_per_epoch=len(X_train)/32,\n",
"\tvalidation_data=(X_val,Y_val),\n",
"\tcallbacks=callbacks_list,\n",
"\tepochs=1000)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3611 - accuracy: 0.6650\n",
"Epoch 00001: accuracy improved from 0.66033 to 0.66495, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-01-0.66.hdf5\n",
"61/60 [==============================] - 24s 395ms/step - loss: 0.3611 - accuracy: 0.6650 - val_loss: 0.4797 - val_accuracy: 0.5350\n",
"Epoch 2/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3371 - accuracy: 0.7020\n",
"Epoch 00002: accuracy improved from 0.66495 to 0.70195, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-02-0.70.hdf5\n",
"61/60 [==============================] - 22s 360ms/step - loss: 0.3371 - accuracy: 0.7020 - val_loss: 0.5290 - val_accuracy: 0.4914\n",
"Epoch 3/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3256 - accuracy: 0.7184\n",
"Epoch 00003: accuracy improved from 0.70195 to 0.71840, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-03-0.72.hdf5\n",
"61/60 [==============================] - 22s 368ms/step - loss: 0.3256 - accuracy: 0.7184 - val_loss: 0.4615 - val_accuracy: 0.5552\n",
"Epoch 4/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3261 - accuracy: 0.7122\n",
"Epoch 00004: accuracy did not improve from 0.71840\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.3261 - accuracy: 0.7122 - val_loss: 0.4565 - val_accuracy: 0.5816\n",
"Epoch 5/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3246 - accuracy: 0.7174\n",
"Epoch 00005: accuracy did not improve from 0.71840\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.3246 - accuracy: 0.7174 - val_loss: 0.3687 - val_accuracy: 0.6594\n",
"Epoch 6/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3124 - accuracy: 0.7220\n",
"Epoch 00006: accuracy improved from 0.71840 to 0.72199, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-06-0.72.hdf5\n",
"61/60 [==============================] - 22s 358ms/step - loss: 0.3124 - accuracy: 0.7220 - val_loss: 0.4947 - val_accuracy: 0.5614\n",
"Epoch 7/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3081 - accuracy: 0.7390\n",
"Epoch 00007: accuracy improved from 0.72199 to 0.73895, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-07-0.74.hdf5\n",
"61/60 [==============================] - 22s 360ms/step - loss: 0.3081 - accuracy: 0.7390 - val_loss: 0.4849 - val_accuracy: 0.5645\n",
"Epoch 8/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.3089 - accuracy: 0.7256\n",
"Epoch 00008: accuracy did not improve from 0.73895\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.3089 - accuracy: 0.7256 - val_loss: 0.3339 - val_accuracy: 0.7045\n",
"Epoch 9/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2979 - accuracy: 0.7487\n",
"Epoch 00009: accuracy improved from 0.73895 to 0.74872, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-09-0.75.hdf5\n",
"61/60 [==============================] - 22s 358ms/step - loss: 0.2979 - accuracy: 0.7487 - val_loss: 0.3621 - val_accuracy: 0.6734\n",
"Epoch 10/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2976 - accuracy: 0.7451\n",
"Epoch 00010: accuracy did not improve from 0.74872\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2976 - accuracy: 0.7451 - val_loss: 0.4385 - val_accuracy: 0.5925\n",
"Epoch 11/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2870 - accuracy: 0.7585\n",
"Epoch 00011: accuracy improved from 0.74872 to 0.75848, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-11-0.76.hdf5\n",
"61/60 [==============================] - 22s 359ms/step - loss: 0.2870 - accuracy: 0.7585 - val_loss: 0.4629 - val_accuracy: 0.5972\n",
"Epoch 12/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2880 - accuracy: 0.7518\n",
"Epoch 00012: accuracy did not improve from 0.75848\n",
"61/60 [==============================] - 17s 283ms/step - loss: 0.2880 - accuracy: 0.7518 - val_loss: 0.3946 - val_accuracy: 0.6485\n",
"Epoch 13/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2784 - accuracy: 0.7677\n",
"Epoch 00013: accuracy improved from 0.75848 to 0.76773, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-13-0.77.hdf5\n",
"61/60 [==============================] - 22s 369ms/step - loss: 0.2784 - accuracy: 0.7677 - val_loss: 0.3884 - val_accuracy: 0.6516\n",
"Epoch 14/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2769 - accuracy: 0.7626\n",
"Epoch 00014: accuracy did not improve from 0.76773\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2769 - accuracy: 0.7626 - val_loss: 0.2838 - val_accuracy: 0.7667\n",
"Epoch 15/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2841 - accuracy: 0.7641\n",
"Epoch 00015: accuracy did not improve from 0.76773\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.2841 - accuracy: 0.7641 - val_loss: 0.3517 - val_accuracy: 0.6874\n",
"Epoch 16/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2811 - accuracy: 0.7605\n",
"Epoch 00016: accuracy did not improve from 0.76773\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.2811 - accuracy: 0.7605 - val_loss: 0.3410 - val_accuracy: 0.7061\n",
"Epoch 17/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2767 - accuracy: 0.7605\n",
"Epoch 00017: accuracy did not improve from 0.76773\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.2767 - accuracy: 0.7605 - val_loss: 0.3093 - val_accuracy: 0.7201\n",
"Epoch 18/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2753 - accuracy: 0.7682\n",
"Epoch 00018: accuracy improved from 0.76773 to 0.76824, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-18-0.77.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.2753 - accuracy: 0.7682 - val_loss: 0.3586 - val_accuracy: 0.6687\n",
"Epoch 19/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2711 - accuracy: 0.7816\n",
"Epoch 00019: accuracy improved from 0.76824 to 0.78160, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-19-0.78.hdf5\n",
"61/60 [==============================] - 22s 360ms/step - loss: 0.2711 - accuracy: 0.7816 - val_loss: 0.3536 - val_accuracy: 0.6936\n",
"Epoch 20/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2717 - accuracy: 0.7713\n",
"Epoch 00020: accuracy did not improve from 0.78160\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.2717 - accuracy: 0.7713 - val_loss: 0.3121 - val_accuracy: 0.7232\n",
"Epoch 21/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2589 - accuracy: 0.7837\n",
"Epoch 00021: accuracy improved from 0.78160 to 0.78366, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-21-0.78.hdf5\n",
"61/60 [==============================] - 22s 365ms/step - loss: 0.2589 - accuracy: 0.7837 - val_loss: 0.2885 - val_accuracy: 0.7543\n",
"Epoch 22/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2518 - accuracy: 0.7914\n",
"Epoch 00022: accuracy improved from 0.78366 to 0.79137, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-22-0.79.hdf5\n",
"61/60 [==============================] - 22s 359ms/step - loss: 0.2518 - accuracy: 0.7914 - val_loss: 0.2739 - val_accuracy: 0.7807\n",
"Epoch 23/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2642 - accuracy: 0.7765\n",
"Epoch 00023: accuracy did not improve from 0.79137\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2642 - accuracy: 0.7765 - val_loss: 0.3308 - val_accuracy: 0.7076\n",
"Epoch 24/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2577 - accuracy: 0.7811\n",
"Epoch 00024: accuracy did not improve from 0.79137\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2577 - accuracy: 0.7811 - val_loss: 0.2437 - val_accuracy: 0.7947\n",
"Epoch 25/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2617 - accuracy: 0.7801\n",
"Epoch 00025: accuracy did not improve from 0.79137\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.2617 - accuracy: 0.7801 - val_loss: 0.4226 - val_accuracy: 0.6563\n",
"Epoch 26/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2482 - accuracy: 0.7924\n",
"Epoch 00026: accuracy improved from 0.79137 to 0.79239, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-26-0.79.hdf5\n",
"61/60 [==============================] - 22s 361ms/step - loss: 0.2482 - accuracy: 0.7924 - val_loss: 0.3469 - val_accuracy: 0.7030\n",
"Epoch 27/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2488 - accuracy: 0.7914\n",
"Epoch 00027: accuracy did not improve from 0.79239\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2488 - accuracy: 0.7914 - val_loss: 0.4226 - val_accuracy: 0.6299\n",
"Epoch 28/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2470 - accuracy: 0.8022\n",
"Epoch 00028: accuracy improved from 0.79239 to 0.80216, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-28-0.80.hdf5\n",
"61/60 [==============================] - 22s 359ms/step - loss: 0.2470 - accuracy: 0.8022 - val_loss: 0.2739 - val_accuracy: 0.7652\n",
"Epoch 29/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2417 - accuracy: 0.7965\n",
"Epoch 00029: accuracy did not improve from 0.80216\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2417 - accuracy: 0.7965 - val_loss: 0.3396 - val_accuracy: 0.6936\n",
"Epoch 30/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2456 - accuracy: 0.7991\n",
"Epoch 00030: accuracy did not improve from 0.80216\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2456 - accuracy: 0.7991 - val_loss: 0.3385 - val_accuracy: 0.6998\n",
"Epoch 31/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2406 - accuracy: 0.8032\n",
"Epoch 00031: accuracy improved from 0.80216 to 0.80319, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-31-0.80.hdf5\n",
"61/60 [==============================] - 22s 367ms/step - loss: 0.2406 - accuracy: 0.8032 - val_loss: 0.3565 - val_accuracy: 0.7030\n",
"Epoch 32/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2379 - accuracy: 0.7991\n",
"Epoch 00032: accuracy did not improve from 0.80319\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2379 - accuracy: 0.7991 - val_loss: 0.3285 - val_accuracy: 0.7325\n",
"Epoch 33/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2390 - accuracy: 0.8052\n",
"Epoch 00033: accuracy improved from 0.80319 to 0.80524, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-33-0.81.hdf5\n",
"61/60 [==============================] - 22s 362ms/step - loss: 0.2390 - accuracy: 0.8052 - val_loss: 0.3423 - val_accuracy: 0.7092\n",
"Epoch 34/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2421 - accuracy: 0.7945\n",
"Epoch 00034: accuracy did not improve from 0.80524\n",
"61/60 [==============================] - 19s 318ms/step - loss: 0.2421 - accuracy: 0.7945 - val_loss: 0.2852 - val_accuracy: 0.7496\n",
"Epoch 35/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2358 - accuracy: 0.8088\n",
"Epoch 00035: accuracy improved from 0.80524 to 0.80884, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-35-0.81.hdf5\n",
"61/60 [==============================] - 22s 360ms/step - loss: 0.2358 - accuracy: 0.8088 - val_loss: 0.2636 - val_accuracy: 0.7729\n",
"Epoch 36/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2364 - accuracy: 0.8016\n",
"Epoch 00036: accuracy did not improve from 0.80884\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.2364 - accuracy: 0.8016 - val_loss: 0.3421 - val_accuracy: 0.6983\n",
"Epoch 37/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2359 - accuracy: 0.8140\n",
"Epoch 00037: accuracy improved from 0.80884 to 0.81398, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-37-0.81.hdf5\n",
"61/60 [==============================] - 22s 360ms/step - loss: 0.2359 - accuracy: 0.8140 - val_loss: 0.2950 - val_accuracy: 0.7512\n",
"Epoch 38/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2295 - accuracy: 0.8027\n",
"Epoch 00038: accuracy did not improve from 0.81398\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2295 - accuracy: 0.8027 - val_loss: 0.3427 - val_accuracy: 0.7092\n",
"Epoch 39/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.8068\n",
"Epoch 00039: accuracy did not improve from 0.81398\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2291 - accuracy: 0.8068 - val_loss: 0.4065 - val_accuracy: 0.6423\n",
"Epoch 40/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2310 - accuracy: 0.8114\n",
"Epoch 00040: accuracy did not improve from 0.81398\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2310 - accuracy: 0.8114 - val_loss: 0.2660 - val_accuracy: 0.7838\n",
"Epoch 41/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2301 - accuracy: 0.8114\n",
"Epoch 00041: accuracy did not improve from 0.81398\n",
"61/60 [==============================] - 17s 283ms/step - loss: 0.2301 - accuracy: 0.8114 - val_loss: 0.3441 - val_accuracy: 0.7014\n",
"Epoch 42/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2192 - accuracy: 0.8237\n",
"Epoch 00042: accuracy improved from 0.81398 to 0.82374, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-42-0.82.hdf5\n",
"61/60 [==============================] - 22s 359ms/step - loss: 0.2192 - accuracy: 0.8237 - val_loss: 0.3219 - val_accuracy: 0.7294\n",
"Epoch 43/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2356 - accuracy: 0.7934\n",
"Epoch 00043: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.2356 - accuracy: 0.7934 - val_loss: 0.3189 - val_accuracy: 0.7325\n",
"Epoch 44/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2228 - accuracy: 0.8201\n",
"Epoch 00044: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2228 - accuracy: 0.8201 - val_loss: 0.3206 - val_accuracy: 0.7325\n",
"Epoch 45/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2220 - accuracy: 0.8114\n",
"Epoch 00045: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2220 - accuracy: 0.8114 - val_loss: 0.2607 - val_accuracy: 0.7714\n",
"Epoch 46/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2258 - accuracy: 0.8135\n",
"Epoch 00046: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2258 - accuracy: 0.8135 - val_loss: 0.2649 - val_accuracy: 0.7621\n",
"Epoch 47/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2229 - accuracy: 0.8145\n",
"Epoch 00047: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2229 - accuracy: 0.8145 - val_loss: 0.3586 - val_accuracy: 0.6827\n",
"Epoch 48/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2212 - accuracy: 0.8124\n",
"Epoch 00048: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.2212 - accuracy: 0.8124 - val_loss: 0.2150 - val_accuracy: 0.8134\n",
"Epoch 49/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2201 - accuracy: 0.8160\n",
"Epoch 00049: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2201 - accuracy: 0.8160 - val_loss: 0.2775 - val_accuracy: 0.7558\n",
"Epoch 50/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2114 - accuracy: 0.8207\n",
"Epoch 00050: accuracy did not improve from 0.82374\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2114 - accuracy: 0.8207 - val_loss: 0.2684 - val_accuracy: 0.7698\n",
"Epoch 51/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2191 - accuracy: 0.8248\n",
"Epoch 00051: accuracy improved from 0.82374 to 0.82477, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-51-0.82.hdf5\n",
"61/60 [==============================] - 22s 359ms/step - loss: 0.2191 - accuracy: 0.8248 - val_loss: 0.3063 - val_accuracy: 0.7170\n",
"Epoch 52/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2197 - accuracy: 0.8222\n",
"Epoch 00052: accuracy did not improve from 0.82477\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.2197 - accuracy: 0.8222 - val_loss: 0.2830 - val_accuracy: 0.7558\n",
"Epoch 53/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2103 - accuracy: 0.8397\n",
"Epoch 00053: accuracy improved from 0.82477 to 0.83967, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-53-0.84.hdf5\n",
"61/60 [==============================] - 23s 376ms/step - loss: 0.2103 - accuracy: 0.8397 - val_loss: 0.2494 - val_accuracy: 0.7807\n",
"Epoch 54/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2230 - accuracy: 0.8129\n",
"Epoch 00054: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2230 - accuracy: 0.8129 - val_loss: 0.2515 - val_accuracy: 0.7745\n",
"Epoch 55/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2162 - accuracy: 0.8186\n",
"Epoch 00055: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.2162 - accuracy: 0.8186 - val_loss: 0.2680 - val_accuracy: 0.7574\n",
"Epoch 56/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2242 - accuracy: 0.8181\n",
"Epoch 00056: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2242 - accuracy: 0.8181 - val_loss: 0.2362 - val_accuracy: 0.7963\n",
"Epoch 57/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2024 - accuracy: 0.8392\n",
"Epoch 00057: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.2024 - accuracy: 0.8392 - val_loss: 0.2849 - val_accuracy: 0.7496\n",
"Epoch 58/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2148 - accuracy: 0.8330\n",
"Epoch 00058: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2148 - accuracy: 0.8330 - val_loss: 0.2838 - val_accuracy: 0.7527\n",
"Epoch 59/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2060 - accuracy: 0.8325\n",
"Epoch 00059: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.2060 - accuracy: 0.8325 - val_loss: 0.3018 - val_accuracy: 0.7481\n",
"Epoch 60/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2178 - accuracy: 0.8253\n",
"Epoch 00060: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2178 - accuracy: 0.8253 - val_loss: 0.3062 - val_accuracy: 0.7465\n",
"Epoch 61/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2058 - accuracy: 0.8299\n",
"Epoch 00061: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2058 - accuracy: 0.8299 - val_loss: 0.3479 - val_accuracy: 0.7014\n",
"Epoch 62/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2025 - accuracy: 0.8386\n",
"Epoch 00062: accuracy did not improve from 0.83967\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.2025 - accuracy: 0.8386 - val_loss: 0.2793 - val_accuracy: 0.7652\n",
"Epoch 63/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1976 - accuracy: 0.8402\n",
"Epoch 00063: accuracy improved from 0.83967 to 0.84018, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-63-0.84.hdf5\n",
"61/60 [==============================] - 22s 366ms/step - loss: 0.1976 - accuracy: 0.8402 - val_loss: 0.2471 - val_accuracy: 0.7932\n",
"Epoch 64/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2123 - accuracy: 0.8273\n",
"Epoch 00064: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.2123 - accuracy: 0.8273 - val_loss: 0.2530 - val_accuracy: 0.7885\n",
"Epoch 65/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1967 - accuracy: 0.8402\n",
"Epoch 00065: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.1967 - accuracy: 0.8402 - val_loss: 0.2050 - val_accuracy: 0.8336\n",
"Epoch 66/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2095 - accuracy: 0.8314\n",
"Epoch 00066: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2095 - accuracy: 0.8314 - val_loss: 0.2474 - val_accuracy: 0.8025\n",
"Epoch 67/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2078 - accuracy: 0.8191\n",
"Epoch 00067: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2078 - accuracy: 0.8191 - val_loss: 0.2514 - val_accuracy: 0.7838\n",
"Epoch 68/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1997 - accuracy: 0.8356\n",
"Epoch 00068: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.1997 - accuracy: 0.8356 - val_loss: 0.3149 - val_accuracy: 0.7309\n",
"Epoch 69/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2123 - accuracy: 0.8232\n",
"Epoch 00069: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.2123 - accuracy: 0.8232 - val_loss: 0.2120 - val_accuracy: 0.8383\n",
"Epoch 70/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2128 - accuracy: 0.8273\n",
"Epoch 00070: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2128 - accuracy: 0.8273 - val_loss: 0.2657 - val_accuracy: 0.7698\n",
"Epoch 71/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2091 - accuracy: 0.8243\n",
"Epoch 00071: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2091 - accuracy: 0.8243 - val_loss: 0.3277 - val_accuracy: 0.7123\n",
"Epoch 72/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2025 - accuracy: 0.8366\n",
"Epoch 00072: accuracy did not improve from 0.84018\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.2025 - accuracy: 0.8366 - val_loss: 0.2698 - val_accuracy: 0.7714\n",
"Epoch 73/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2045 - accuracy: 0.8458\n",
"Epoch 00073: accuracy improved from 0.84018 to 0.84584, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-73-0.85.hdf5\n",
"61/60 [==============================] - 22s 361ms/step - loss: 0.2045 - accuracy: 0.8458 - val_loss: 0.2441 - val_accuracy: 0.7900\n",
"Epoch 74/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2009 - accuracy: 0.8366\n",
"Epoch 00074: accuracy did not improve from 0.84584\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.2009 - accuracy: 0.8366 - val_loss: 0.2496 - val_accuracy: 0.7869\n",
"Epoch 75/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2132 - accuracy: 0.8196\n",
"Epoch 00075: accuracy did not improve from 0.84584\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.2132 - accuracy: 0.8196 - val_loss: 0.2699 - val_accuracy: 0.7683\n",
"Epoch 76/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1958 - accuracy: 0.8458\n",
"Epoch 00076: accuracy did not improve from 0.84584\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1958 - accuracy: 0.8458 - val_loss: 0.2233 - val_accuracy: 0.7994\n",
"Epoch 77/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1931 - accuracy: 0.8438\n",
"Epoch 00077: accuracy did not improve from 0.84584\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1931 - accuracy: 0.8438 - val_loss: 0.2748 - val_accuracy: 0.7558\n",
"Epoch 78/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1928 - accuracy: 0.8479\n",
"Epoch 00078: accuracy improved from 0.84584 to 0.84789, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-78-0.85.hdf5\n",
"61/60 [==============================] - 78s 1s/step - loss: 0.1928 - accuracy: 0.8479 - val_loss: 0.2866 - val_accuracy: 0.7574\n",
"Epoch 79/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1970 - accuracy: 0.8273\n",
"Epoch 00079: accuracy did not improve from 0.84789\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1970 - accuracy: 0.8273 - val_loss: 0.2627 - val_accuracy: 0.7916\n",
"Epoch 80/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2038 - accuracy: 0.8340\n",
"Epoch 00080: accuracy did not improve from 0.84789\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2038 - accuracy: 0.8340 - val_loss: 0.2804 - val_accuracy: 0.7776\n",
"Epoch 81/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1939 - accuracy: 0.8407\n",
"Epoch 00081: accuracy did not improve from 0.84789\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1939 - accuracy: 0.8407 - val_loss: 0.2725 - val_accuracy: 0.7652\n",
"Epoch 82/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1943 - accuracy: 0.8499\n",
"Epoch 00082: accuracy improved from 0.84789 to 0.84995, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-82-0.85.hdf5\n",
"61/60 [==============================] - 22s 361ms/step - loss: 0.1943 - accuracy: 0.8499 - val_loss: 0.2775 - val_accuracy: 0.7838\n",
"Epoch 83/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.8304\n",
"Epoch 00083: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1983 - accuracy: 0.8304 - val_loss: 0.2092 - val_accuracy: 0.8367\n",
"Epoch 84/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1939 - accuracy: 0.8392\n",
"Epoch 00084: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.1939 - accuracy: 0.8392 - val_loss: 0.2129 - val_accuracy: 0.8351\n",
"Epoch 85/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1964 - accuracy: 0.8407\n",
"Epoch 00085: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.1964 - accuracy: 0.8407 - val_loss: 0.2418 - val_accuracy: 0.8009\n",
"Epoch 86/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1918 - accuracy: 0.8494\n",
"Epoch 00086: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.1918 - accuracy: 0.8494 - val_loss: 0.2822 - val_accuracy: 0.7683\n",
"Epoch 87/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.2024 - accuracy: 0.8314\n",
"Epoch 00087: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.2024 - accuracy: 0.8314 - val_loss: 0.2274 - val_accuracy: 0.8103\n",
"Epoch 88/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1872 - accuracy: 0.8422\n",
"Epoch 00088: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1872 - accuracy: 0.8422 - val_loss: 0.2524 - val_accuracy: 0.8009\n",
"Epoch 89/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1977 - accuracy: 0.8392\n",
"Epoch 00089: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1977 - accuracy: 0.8392 - val_loss: 0.2366 - val_accuracy: 0.8180\n",
"Epoch 90/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1870 - accuracy: 0.8469\n",
"Epoch 00090: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1870 - accuracy: 0.8469 - val_loss: 0.2073 - val_accuracy: 0.8305\n",
"Epoch 91/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1925 - accuracy: 0.8458\n",
"Epoch 00091: accuracy did not improve from 0.84995\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1925 - accuracy: 0.8458 - val_loss: 0.2186 - val_accuracy: 0.8258\n",
"Epoch 92/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1850 - accuracy: 0.8535\n",
"Epoch 00092: accuracy improved from 0.84995 to 0.85355, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-92-0.85.hdf5\n",
"61/60 [==============================] - 22s 368ms/step - loss: 0.1850 - accuracy: 0.8535 - val_loss: 0.2660 - val_accuracy: 0.7792\n",
"Epoch 93/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1949 - accuracy: 0.8433\n",
"Epoch 00093: accuracy did not improve from 0.85355\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1949 - accuracy: 0.8433 - val_loss: 0.2595 - val_accuracy: 0.7792\n",
"Epoch 94/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1896 - accuracy: 0.8479\n",
"Epoch 00094: accuracy did not improve from 0.85355\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1896 - accuracy: 0.8479 - val_loss: 0.2512 - val_accuracy: 0.7869\n",
"Epoch 95/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1863 - accuracy: 0.8535\n",
"Epoch 00095: accuracy did not improve from 0.85355\n",
"61/60 [==============================] - 18s 300ms/step - loss: 0.1863 - accuracy: 0.8535 - val_loss: 0.2376 - val_accuracy: 0.8040\n",
"Epoch 96/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1863 - accuracy: 0.8474\n",
"Epoch 00096: accuracy did not improve from 0.85355\n",
"61/60 [==============================] - 18s 297ms/step - loss: 0.1863 - accuracy: 0.8474 - val_loss: 0.2557 - val_accuracy: 0.7885\n",
"Epoch 97/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1931 - accuracy: 0.8530\n",
"Epoch 00097: accuracy did not improve from 0.85355\n",
"61/60 [==============================] - 18s 299ms/step - loss: 0.1931 - accuracy: 0.8530 - val_loss: 0.2244 - val_accuracy: 0.8180\n",
"Epoch 98/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1838 - accuracy: 0.8551\n",
"Epoch 00098: accuracy improved from 0.85355 to 0.85509, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-98-0.86.hdf5\n",
"61/60 [==============================] - 37s 609ms/step - loss: 0.1838 - accuracy: 0.8551 - val_loss: 0.2648 - val_accuracy: 0.7947\n",
"Epoch 99/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1849 - accuracy: 0.8494\n",
"Epoch 00099: accuracy did not improve from 0.85509\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1849 - accuracy: 0.8494 - val_loss: 0.2398 - val_accuracy: 0.8103\n",
"Epoch 100/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1874 - accuracy: 0.8525\n",
"Epoch 00100: accuracy did not improve from 0.85509\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1874 - accuracy: 0.8525 - val_loss: 0.2294 - val_accuracy: 0.8196\n",
"Epoch 101/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1779 - accuracy: 0.8613\n",
"Epoch 00101: accuracy improved from 0.85509 to 0.86125, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-101-0.86.hdf5\n",
"61/60 [==============================] - 22s 362ms/step - loss: 0.1779 - accuracy: 0.8613 - val_loss: 0.2351 - val_accuracy: 0.8118\n",
"Epoch 102/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1767 - accuracy: 0.8556\n",
"Epoch 00102: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1767 - accuracy: 0.8556 - val_loss: 0.2129 - val_accuracy: 0.8289\n",
"Epoch 103/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1701 - accuracy: 0.8551\n",
"Epoch 00103: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1701 - accuracy: 0.8551 - val_loss: 0.2024 - val_accuracy: 0.8305\n",
"Epoch 104/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1818 - accuracy: 0.8494\n",
"Epoch 00104: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1818 - accuracy: 0.8494 - val_loss: 0.2146 - val_accuracy: 0.8227\n",
"Epoch 105/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1835 - accuracy: 0.8443\n",
"Epoch 00105: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1835 - accuracy: 0.8443 - val_loss: 0.2159 - val_accuracy: 0.8196\n",
"Epoch 106/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1840 - accuracy: 0.8541\n",
"Epoch 00106: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.1840 - accuracy: 0.8541 - val_loss: 0.2410 - val_accuracy: 0.8040\n",
"Epoch 107/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1906 - accuracy: 0.8458\n",
"Epoch 00107: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1906 - accuracy: 0.8458 - val_loss: 0.2099 - val_accuracy: 0.8243\n",
"Epoch 108/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1776 - accuracy: 0.8566\n",
"Epoch 00108: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1776 - accuracy: 0.8566 - val_loss: 0.2637 - val_accuracy: 0.7729\n",
"Epoch 109/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1918 - accuracy: 0.8299\n",
"Epoch 00109: accuracy did not improve from 0.86125\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1918 - accuracy: 0.8299 - val_loss: 0.2497 - val_accuracy: 0.7900\n",
"Epoch 110/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1692 - accuracy: 0.8618\n",
"Epoch 00110: accuracy improved from 0.86125 to 0.86177, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-110-0.86.hdf5\n",
"61/60 [==============================] - 22s 361ms/step - loss: 0.1692 - accuracy: 0.8618 - val_loss: 0.1932 - val_accuracy: 0.8538\n",
"Epoch 111/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1927 - accuracy: 0.8469\n",
"Epoch 00111: accuracy did not improve from 0.86177\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1927 - accuracy: 0.8469 - val_loss: 0.2517 - val_accuracy: 0.7916\n",
"Epoch 112/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1770 - accuracy: 0.8607\n",
"Epoch 00112: accuracy did not improve from 0.86177\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1770 - accuracy: 0.8607 - val_loss: 0.2429 - val_accuracy: 0.7978\n",
"Epoch 113/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1857 - accuracy: 0.8489\n",
"Epoch 00113: accuracy did not improve from 0.86177\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1857 - accuracy: 0.8489 - val_loss: 0.2360 - val_accuracy: 0.8056\n",
"Epoch 114/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1806 - accuracy: 0.8407\n",
"Epoch 00114: accuracy did not improve from 0.86177\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.1806 - accuracy: 0.8407 - val_loss: 0.2323 - val_accuracy: 0.8134\n",
"Epoch 115/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1672 - accuracy: 0.8571\n",
"Epoch 00115: accuracy did not improve from 0.86177\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1672 - accuracy: 0.8571 - val_loss: 0.2259 - val_accuracy: 0.8149\n",
"Epoch 116/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1756 - accuracy: 0.8654\n",
"Epoch 00116: accuracy improved from 0.86177 to 0.86536, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-116-0.87.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1756 - accuracy: 0.8654 - val_loss: 0.2407 - val_accuracy: 0.8149\n",
"Epoch 117/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1803 - accuracy: 0.8525\n",
"Epoch 00117: accuracy did not improve from 0.86536\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1803 - accuracy: 0.8525 - val_loss: 0.2547 - val_accuracy: 0.7854\n",
"Epoch 118/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1769 - accuracy: 0.8556\n",
"Epoch 00118: accuracy did not improve from 0.86536\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1769 - accuracy: 0.8556 - val_loss: 0.2148 - val_accuracy: 0.8243\n",
"Epoch 119/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1731 - accuracy: 0.8499\n",
"Epoch 00119: accuracy did not improve from 0.86536\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1731 - accuracy: 0.8499 - val_loss: 0.2409 - val_accuracy: 0.7932\n",
"Epoch 120/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1801 - accuracy: 0.8561\n",
"Epoch 00120: accuracy did not improve from 0.86536\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1801 - accuracy: 0.8561 - val_loss: 0.2909 - val_accuracy: 0.7418\n",
"Epoch 121/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1668 - accuracy: 0.8669\n",
"Epoch 00121: accuracy improved from 0.86536 to 0.86691, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-121-0.87.hdf5\n",
"61/60 [==============================] - 22s 362ms/step - loss: 0.1668 - accuracy: 0.8669 - val_loss: 0.2717 - val_accuracy: 0.7792\n",
"Epoch 122/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1682 - accuracy: 0.8607\n",
"Epoch 00122: accuracy did not improve from 0.86691\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1682 - accuracy: 0.8607 - val_loss: 0.1989 - val_accuracy: 0.8367\n",
"Epoch 123/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1785 - accuracy: 0.8561\n",
"Epoch 00123: accuracy did not improve from 0.86691\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.1785 - accuracy: 0.8561 - val_loss: 0.1896 - val_accuracy: 0.8445\n",
"Epoch 124/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1633 - accuracy: 0.8679\n",
"Epoch 00124: accuracy improved from 0.86691 to 0.86793, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-124-0.87.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1633 - accuracy: 0.8679 - val_loss: 0.2028 - val_accuracy: 0.8383\n",
"Epoch 125/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.8710\n",
"Epoch 00125: accuracy improved from 0.86793 to 0.87102, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-125-0.87.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1677 - accuracy: 0.8710 - val_loss: 0.2230 - val_accuracy: 0.8258\n",
"Epoch 126/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1767 - accuracy: 0.8494\n",
"Epoch 00126: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1767 - accuracy: 0.8494 - val_loss: 0.2731 - val_accuracy: 0.7838\n",
"Epoch 127/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1665 - accuracy: 0.8643\n",
"Epoch 00127: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1665 - accuracy: 0.8643 - val_loss: 0.3131 - val_accuracy: 0.7512\n",
"Epoch 128/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1615 - accuracy: 0.8654\n",
"Epoch 00128: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1615 - accuracy: 0.8654 - val_loss: 0.2482 - val_accuracy: 0.7978\n",
"Epoch 129/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1801 - accuracy: 0.8458\n",
"Epoch 00129: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1801 - accuracy: 0.8458 - val_loss: 0.2320 - val_accuracy: 0.8149\n",
"Epoch 130/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1626 - accuracy: 0.8679\n",
"Epoch 00130: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1626 - accuracy: 0.8679 - val_loss: 0.2068 - val_accuracy: 0.8336\n",
"Epoch 131/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1675 - accuracy: 0.8602\n",
"Epoch 00131: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1675 - accuracy: 0.8602 - val_loss: 0.2081 - val_accuracy: 0.8243\n",
"Epoch 132/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1674 - accuracy: 0.8710\n",
"Epoch 00132: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1674 - accuracy: 0.8710 - val_loss: 0.2030 - val_accuracy: 0.8320\n",
"Epoch 133/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1736 - accuracy: 0.8633\n",
"Epoch 00133: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1736 - accuracy: 0.8633 - val_loss: 0.2211 - val_accuracy: 0.8165\n",
"Epoch 134/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1735 - accuracy: 0.8618\n",
"Epoch 00134: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 20s 321ms/step - loss: 0.1735 - accuracy: 0.8618 - val_loss: 0.2816 - val_accuracy: 0.7621\n",
"Epoch 135/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1697 - accuracy: 0.8571\n",
"Epoch 00135: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 19s 307ms/step - loss: 0.1697 - accuracy: 0.8571 - val_loss: 0.2173 - val_accuracy: 0.8258\n",
"Epoch 136/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1717 - accuracy: 0.8582\n",
"Epoch 00136: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 297ms/step - loss: 0.1717 - accuracy: 0.8582 - val_loss: 0.2381 - val_accuracy: 0.8087\n",
"Epoch 137/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1667 - accuracy: 0.8684\n",
"Epoch 00137: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1667 - accuracy: 0.8684 - val_loss: 0.2049 - val_accuracy: 0.8367\n",
"Epoch 138/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1670 - accuracy: 0.8633\n",
"Epoch 00138: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1670 - accuracy: 0.8633 - val_loss: 0.1938 - val_accuracy: 0.8491\n",
"Epoch 139/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1779 - accuracy: 0.8592\n",
"Epoch 00139: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1779 - accuracy: 0.8592 - val_loss: 0.2111 - val_accuracy: 0.8398\n",
"Epoch 140/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1674 - accuracy: 0.8577\n",
"Epoch 00140: accuracy did not improve from 0.87102\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1674 - accuracy: 0.8577 - val_loss: 0.2092 - val_accuracy: 0.8460\n",
"Epoch 141/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1558 - accuracy: 0.8777\n",
"Epoch 00141: accuracy improved from 0.87102 to 0.87770, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-141-0.88.hdf5\n",
"61/60 [==============================] - 22s 365ms/step - loss: 0.1558 - accuracy: 0.8777 - val_loss: 0.2664 - val_accuracy: 0.7869\n",
"Epoch 142/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1620 - accuracy: 0.8726\n",
"Epoch 00142: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1620 - accuracy: 0.8726 - val_loss: 0.3194 - val_accuracy: 0.7481\n",
"Epoch 143/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1678 - accuracy: 0.8587\n",
"Epoch 00143: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1678 - accuracy: 0.8587 - val_loss: 0.2545 - val_accuracy: 0.7916\n",
"Epoch 144/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1652 - accuracy: 0.8628\n",
"Epoch 00144: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1652 - accuracy: 0.8628 - val_loss: 0.2286 - val_accuracy: 0.8165\n",
"Epoch 145/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1694 - accuracy: 0.8654\n",
"Epoch 00145: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1694 - accuracy: 0.8654 - val_loss: 0.2268 - val_accuracy: 0.7994\n",
"Epoch 146/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1653 - accuracy: 0.8674\n",
"Epoch 00146: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1653 - accuracy: 0.8674 - val_loss: 0.2015 - val_accuracy: 0.8305\n",
"Epoch 147/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1714 - accuracy: 0.8659\n",
"Epoch 00147: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1714 - accuracy: 0.8659 - val_loss: 0.1965 - val_accuracy: 0.8414\n",
"Epoch 148/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1663 - accuracy: 0.8654\n",
"Epoch 00148: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1663 - accuracy: 0.8654 - val_loss: 0.1991 - val_accuracy: 0.8320\n",
"Epoch 149/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1637 - accuracy: 0.8726\n",
"Epoch 00149: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1637 - accuracy: 0.8726 - val_loss: 0.1898 - val_accuracy: 0.8491\n",
"Epoch 150/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1710 - accuracy: 0.8664\n",
"Epoch 00150: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1710 - accuracy: 0.8664 - val_loss: 0.2074 - val_accuracy: 0.8258\n",
"Epoch 151/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.8607\n",
"Epoch 00151: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 20s 320ms/step - loss: 0.1639 - accuracy: 0.8607 - val_loss: 0.2414 - val_accuracy: 0.7932\n",
"Epoch 152/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1598 - accuracy: 0.8746\n",
"Epoch 00152: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 19s 315ms/step - loss: 0.1598 - accuracy: 0.8746 - val_loss: 0.1802 - val_accuracy: 0.8476\n",
"Epoch 153/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1617 - accuracy: 0.8726\n",
"Epoch 00153: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1617 - accuracy: 0.8726 - val_loss: 0.2605 - val_accuracy: 0.7932\n",
"Epoch 154/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1648 - accuracy: 0.8695\n",
"Epoch 00154: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1648 - accuracy: 0.8695 - val_loss: 0.2417 - val_accuracy: 0.7978\n",
"Epoch 155/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1605 - accuracy: 0.8679\n",
"Epoch 00155: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1605 - accuracy: 0.8679 - val_loss: 0.2504 - val_accuracy: 0.7916\n",
"Epoch 156/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1697 - accuracy: 0.8510\n",
"Epoch 00156: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1697 - accuracy: 0.8510 - val_loss: 0.2012 - val_accuracy: 0.8320\n",
"Epoch 157/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1586 - accuracy: 0.8741\n",
"Epoch 00157: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1586 - accuracy: 0.8741 - val_loss: 0.1687 - val_accuracy: 0.8663\n",
"Epoch 158/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1649 - accuracy: 0.8695\n",
"Epoch 00158: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1649 - accuracy: 0.8695 - val_loss: 0.1997 - val_accuracy: 0.8320\n",
"Epoch 159/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1567 - accuracy: 0.8710\n",
"Epoch 00159: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1567 - accuracy: 0.8710 - val_loss: 0.1894 - val_accuracy: 0.8336\n",
"Epoch 160/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1632 - accuracy: 0.8720\n",
"Epoch 00160: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1632 - accuracy: 0.8720 - val_loss: 0.2638 - val_accuracy: 0.7776\n",
"Epoch 161/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1624 - accuracy: 0.8746\n",
"Epoch 00161: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1624 - accuracy: 0.8746 - val_loss: 0.2049 - val_accuracy: 0.8289\n",
"Epoch 162/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1624 - accuracy: 0.8756\n",
"Epoch 00162: accuracy did not improve from 0.87770\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1624 - accuracy: 0.8756 - val_loss: 0.2202 - val_accuracy: 0.8149\n",
"Epoch 163/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1539 - accuracy: 0.8839\n",
"Epoch 00163: accuracy improved from 0.87770 to 0.88386, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-163-0.88.hdf5\n",
"61/60 [==============================] - 22s 361ms/step - loss: 0.1539 - accuracy: 0.8839 - val_loss: 0.2177 - val_accuracy: 0.8212\n",
"Epoch 164/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1720 - accuracy: 0.8618\n",
"Epoch 00164: accuracy did not improve from 0.88386\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1720 - accuracy: 0.8618 - val_loss: 0.1850 - val_accuracy: 0.8476\n",
"Epoch 165/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1684 - accuracy: 0.8623\n",
"Epoch 00165: accuracy did not improve from 0.88386\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1684 - accuracy: 0.8623 - val_loss: 0.1807 - val_accuracy: 0.8523\n",
"Epoch 166/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1610 - accuracy: 0.8731\n",
"Epoch 00166: accuracy did not improve from 0.88386\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1610 - accuracy: 0.8731 - val_loss: 0.2289 - val_accuracy: 0.8134\n",
"Epoch 167/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1706 - accuracy: 0.8613\n",
"Epoch 00167: accuracy did not improve from 0.88386\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1706 - accuracy: 0.8613 - val_loss: 0.2405 - val_accuracy: 0.8056\n",
"Epoch 168/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1497 - accuracy: 0.8859\n",
"Epoch 00168: accuracy improved from 0.88386 to 0.88592, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-168-0.89.hdf5\n",
"61/60 [==============================] - 28s 456ms/step - loss: 0.1497 - accuracy: 0.8859 - val_loss: 0.2176 - val_accuracy: 0.8227\n",
"Epoch 169/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1618 - accuracy: 0.8777\n",
"Epoch 00169: accuracy did not improve from 0.88592\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1618 - accuracy: 0.8777 - val_loss: 0.2633 - val_accuracy: 0.7885\n",
"Epoch 170/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1568 - accuracy: 0.8772\n",
"Epoch 00170: accuracy did not improve from 0.88592\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1568 - accuracy: 0.8772 - val_loss: 0.2227 - val_accuracy: 0.8180\n",
"Epoch 171/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1559 - accuracy: 0.8710\n",
"Epoch 00171: accuracy did not improve from 0.88592\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1559 - accuracy: 0.8710 - val_loss: 0.2303 - val_accuracy: 0.8118\n",
"Epoch 172/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.8772\n",
"Epoch 00172: accuracy did not improve from 0.88592\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1562 - accuracy: 0.8772 - val_loss: 0.2124 - val_accuracy: 0.8243\n",
"Epoch 173/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.8839\n",
"Epoch 00173: accuracy did not improve from 0.88592\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1471 - accuracy: 0.8839 - val_loss: 0.2063 - val_accuracy: 0.8305\n",
"Epoch 174/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1492 - accuracy: 0.8869\n",
"Epoch 00174: accuracy improved from 0.88592 to 0.88695, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-174-0.89.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1492 - accuracy: 0.8869 - val_loss: 0.2193 - val_accuracy: 0.8103\n",
"Epoch 175/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1598 - accuracy: 0.8756\n",
"Epoch 00175: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1598 - accuracy: 0.8756 - val_loss: 0.1872 - val_accuracy: 0.8414\n",
"Epoch 176/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1516 - accuracy: 0.8808\n",
"Epoch 00176: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1516 - accuracy: 0.8808 - val_loss: 0.1876 - val_accuracy: 0.8429\n",
"Epoch 177/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1599 - accuracy: 0.8731\n",
"Epoch 00177: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1599 - accuracy: 0.8731 - val_loss: 0.2068 - val_accuracy: 0.8243\n",
"Epoch 178/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1697 - accuracy: 0.8710\n",
"Epoch 00178: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1697 - accuracy: 0.8710 - val_loss: 0.1922 - val_accuracy: 0.8398\n",
"Epoch 179/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1574 - accuracy: 0.8756\n",
"Epoch 00179: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1574 - accuracy: 0.8756 - val_loss: 0.1931 - val_accuracy: 0.8383\n",
"Epoch 180/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1469 - accuracy: 0.8834\n",
"Epoch 00180: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1469 - accuracy: 0.8834 - val_loss: 0.1757 - val_accuracy: 0.8476\n",
"Epoch 181/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1532 - accuracy: 0.8762\n",
"Epoch 00181: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1532 - accuracy: 0.8762 - val_loss: 0.1913 - val_accuracy: 0.8336\n",
"Epoch 182/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1650 - accuracy: 0.8674\n",
"Epoch 00182: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1650 - accuracy: 0.8674 - val_loss: 0.2043 - val_accuracy: 0.8336\n",
"Epoch 183/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1606 - accuracy: 0.8710\n",
"Epoch 00183: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1606 - accuracy: 0.8710 - val_loss: 0.1776 - val_accuracy: 0.8507\n",
"Epoch 184/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.8684\n",
"Epoch 00184: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1562 - accuracy: 0.8684 - val_loss: 0.1892 - val_accuracy: 0.8445\n",
"Epoch 185/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1512 - accuracy: 0.8798\n",
"Epoch 00185: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 19s 311ms/step - loss: 0.1512 - accuracy: 0.8798 - val_loss: 0.1830 - val_accuracy: 0.8554\n",
"Epoch 186/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1574 - accuracy: 0.8772\n",
"Epoch 00186: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 19s 316ms/step - loss: 0.1574 - accuracy: 0.8772 - val_loss: 0.1820 - val_accuracy: 0.8523\n",
"Epoch 187/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1579 - accuracy: 0.8700\n",
"Epoch 00187: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1579 - accuracy: 0.8700 - val_loss: 0.1886 - val_accuracy: 0.8523\n",
"Epoch 188/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1497 - accuracy: 0.8782\n",
"Epoch 00188: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1497 - accuracy: 0.8782 - val_loss: 0.1858 - val_accuracy: 0.8476\n",
"Epoch 189/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1546 - accuracy: 0.8762\n",
"Epoch 00189: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1546 - accuracy: 0.8762 - val_loss: 0.2199 - val_accuracy: 0.8103\n",
"Epoch 190/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1539 - accuracy: 0.8803\n",
"Epoch 00190: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1539 - accuracy: 0.8803 - val_loss: 0.2007 - val_accuracy: 0.8367\n",
"Epoch 191/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1603 - accuracy: 0.8741\n",
"Epoch 00191: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1603 - accuracy: 0.8741 - val_loss: 0.1901 - val_accuracy: 0.8476\n",
"Epoch 192/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1491 - accuracy: 0.8808\n",
"Epoch 00192: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1491 - accuracy: 0.8808 - val_loss: 0.2138 - val_accuracy: 0.8320\n",
"Epoch 193/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1475 - accuracy: 0.8782\n",
"Epoch 00193: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1475 - accuracy: 0.8782 - val_loss: 0.1926 - val_accuracy: 0.8367\n",
"Epoch 194/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1541 - accuracy: 0.8844\n",
"Epoch 00194: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1541 - accuracy: 0.8844 - val_loss: 0.2588 - val_accuracy: 0.7838\n",
"Epoch 195/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1549 - accuracy: 0.8726\n",
"Epoch 00195: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1549 - accuracy: 0.8726 - val_loss: 0.2138 - val_accuracy: 0.8274\n",
"Epoch 196/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1499 - accuracy: 0.8787\n",
"Epoch 00196: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1499 - accuracy: 0.8787 - val_loss: 0.2000 - val_accuracy: 0.8445\n",
"Epoch 197/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1515 - accuracy: 0.8839\n",
"Epoch 00197: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1515 - accuracy: 0.8839 - val_loss: 0.1817 - val_accuracy: 0.8445\n",
"Epoch 198/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1595 - accuracy: 0.8792\n",
"Epoch 00198: accuracy did not improve from 0.88695\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1595 - accuracy: 0.8792 - val_loss: 0.1873 - val_accuracy: 0.8383\n",
"Epoch 199/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1432 - accuracy: 0.8890\n",
"Epoch 00199: accuracy improved from 0.88695 to 0.88900, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-199-0.89.hdf5\n",
"61/60 [==============================] - 22s 363ms/step - loss: 0.1432 - accuracy: 0.8890 - val_loss: 0.1898 - val_accuracy: 0.8476\n",
"Epoch 200/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1469 - accuracy: 0.8756\n",
"Epoch 00200: accuracy did not improve from 0.88900\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1469 - accuracy: 0.8756 - val_loss: 0.2180 - val_accuracy: 0.8274\n",
"Epoch 201/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1432 - accuracy: 0.8905\n",
"Epoch 00201: accuracy improved from 0.88900 to 0.89054, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-201-0.89.hdf5\n",
"61/60 [==============================] - 28s 463ms/step - loss: 0.1432 - accuracy: 0.8905 - val_loss: 0.1966 - val_accuracy: 0.8398\n",
"Epoch 202/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1448 - accuracy: 0.8880\n",
"Epoch 00202: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 20s 329ms/step - loss: 0.1448 - accuracy: 0.8880 - val_loss: 0.2333 - val_accuracy: 0.8040\n",
"Epoch 203/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1539 - accuracy: 0.8813\n",
"Epoch 00203: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1539 - accuracy: 0.8813 - val_loss: 0.2647 - val_accuracy: 0.7776\n",
"Epoch 204/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1499 - accuracy: 0.8834\n",
"Epoch 00204: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1499 - accuracy: 0.8834 - val_loss: 0.2341 - val_accuracy: 0.7932\n",
"Epoch 205/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1524 - accuracy: 0.8839\n",
"Epoch 00205: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1524 - accuracy: 0.8839 - val_loss: 0.2087 - val_accuracy: 0.8212\n",
"Epoch 206/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1513 - accuracy: 0.8720\n",
"Epoch 00206: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1513 - accuracy: 0.8720 - val_loss: 0.2231 - val_accuracy: 0.8134\n",
"Epoch 207/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1553 - accuracy: 0.8720\n",
"Epoch 00207: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.1553 - accuracy: 0.8720 - val_loss: 0.1954 - val_accuracy: 0.8351\n",
"Epoch 208/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1484 - accuracy: 0.8859\n",
"Epoch 00208: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1484 - accuracy: 0.8859 - val_loss: 0.2213 - val_accuracy: 0.8149\n",
"Epoch 209/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1491 - accuracy: 0.8854\n",
"Epoch 00209: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1491 - accuracy: 0.8854 - val_loss: 0.2352 - val_accuracy: 0.7994\n",
"Epoch 210/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1513 - accuracy: 0.8823\n",
"Epoch 00210: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1513 - accuracy: 0.8823 - val_loss: 0.2439 - val_accuracy: 0.7932\n",
"Epoch 211/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1479 - accuracy: 0.8818\n",
"Epoch 00211: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1479 - accuracy: 0.8818 - val_loss: 0.2096 - val_accuracy: 0.8227\n",
"Epoch 212/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1487 - accuracy: 0.8756\n",
"Epoch 00212: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1487 - accuracy: 0.8756 - val_loss: 0.2289 - val_accuracy: 0.8134\n",
"Epoch 213/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1452 - accuracy: 0.8834\n",
"Epoch 00213: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1452 - accuracy: 0.8834 - val_loss: 0.2121 - val_accuracy: 0.8180\n",
"Epoch 214/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1476 - accuracy: 0.8905\n",
"Epoch 00214: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 17s 287ms/step - loss: 0.1476 - accuracy: 0.8905 - val_loss: 0.2255 - val_accuracy: 0.8134\n",
"Epoch 215/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1519 - accuracy: 0.8787\n",
"Epoch 00215: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1519 - accuracy: 0.8787 - val_loss: 0.2013 - val_accuracy: 0.8258\n",
"Epoch 216/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1444 - accuracy: 0.8787\n",
"Epoch 00216: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1444 - accuracy: 0.8787 - val_loss: 0.2311 - val_accuracy: 0.8040\n",
"Epoch 217/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1472 - accuracy: 0.8859\n",
"Epoch 00217: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1472 - accuracy: 0.8859 - val_loss: 0.2004 - val_accuracy: 0.8429\n",
"Epoch 218/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1465 - accuracy: 0.8864\n",
"Epoch 00218: accuracy did not improve from 0.89054\n",
"61/60 [==============================] - 20s 331ms/step - loss: 0.1465 - accuracy: 0.8864 - val_loss: 0.2009 - val_accuracy: 0.8336\n",
"Epoch 219/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1387 - accuracy: 0.8916\n",
"Epoch 00219: accuracy improved from 0.89054 to 0.89157, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-219-0.89.hdf5\n",
"61/60 [==============================] - 22s 367ms/step - loss: 0.1387 - accuracy: 0.8916 - val_loss: 0.1924 - val_accuracy: 0.8429\n",
"Epoch 220/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1391 - accuracy: 0.8849\n",
"Epoch 00220: accuracy did not improve from 0.89157\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1391 - accuracy: 0.8849 - val_loss: 0.1931 - val_accuracy: 0.8414\n",
"Epoch 221/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1419 - accuracy: 0.8885\n",
"Epoch 00221: accuracy did not improve from 0.89157\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1419 - accuracy: 0.8885 - val_loss: 0.2089 - val_accuracy: 0.8227\n",
"Epoch 222/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1447 - accuracy: 0.8798\n",
"Epoch 00222: accuracy did not improve from 0.89157\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1447 - accuracy: 0.8798 - val_loss: 0.2039 - val_accuracy: 0.8227\n",
"Epoch 223/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1463 - accuracy: 0.8854\n",
"Epoch 00223: accuracy did not improve from 0.89157\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1463 - accuracy: 0.8854 - val_loss: 0.1968 - val_accuracy: 0.8507\n",
"Epoch 224/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1552 - accuracy: 0.8736\n",
"Epoch 00224: accuracy did not improve from 0.89157\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1552 - accuracy: 0.8736 - val_loss: 0.2348 - val_accuracy: 0.7978\n",
"Epoch 225/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1475 - accuracy: 0.8859\n",
"Epoch 00225: accuracy did not improve from 0.89157\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1475 - accuracy: 0.8859 - val_loss: 0.2543 - val_accuracy: 0.7760\n",
"Epoch 226/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1362 - accuracy: 0.8936\n",
"Epoch 00226: accuracy improved from 0.89157 to 0.89363, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-226-0.89.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1362 - accuracy: 0.8936 - val_loss: 0.2239 - val_accuracy: 0.8087\n",
"Epoch 227/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1403 - accuracy: 0.8900\n",
"Epoch 00227: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1403 - accuracy: 0.8900 - val_loss: 0.1731 - val_accuracy: 0.8523\n",
"Epoch 228/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1341 - accuracy: 0.8895\n",
"Epoch 00228: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1341 - accuracy: 0.8895 - val_loss: 0.1752 - val_accuracy: 0.8725\n",
"Epoch 229/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1393 - accuracy: 0.8936\n",
"Epoch 00229: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1393 - accuracy: 0.8936 - val_loss: 0.1963 - val_accuracy: 0.8398\n",
"Epoch 230/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1390 - accuracy: 0.8905\n",
"Epoch 00230: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1390 - accuracy: 0.8905 - val_loss: 0.1972 - val_accuracy: 0.8383\n",
"Epoch 231/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1407 - accuracy: 0.8895\n",
"Epoch 00231: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1407 - accuracy: 0.8895 - val_loss: 0.1754 - val_accuracy: 0.8523\n",
"Epoch 232/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1522 - accuracy: 0.8736\n",
"Epoch 00232: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1522 - accuracy: 0.8736 - val_loss: 0.1893 - val_accuracy: 0.8398\n",
"Epoch 233/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1383 - accuracy: 0.8869\n",
"Epoch 00233: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1383 - accuracy: 0.8869 - val_loss: 0.1911 - val_accuracy: 0.8460\n",
"Epoch 234/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1428 - accuracy: 0.8900\n",
"Epoch 00234: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1428 - accuracy: 0.8900 - val_loss: 0.1940 - val_accuracy: 0.8289\n",
"Epoch 235/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1402 - accuracy: 0.8787\n",
"Epoch 00235: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1402 - accuracy: 0.8787 - val_loss: 0.1906 - val_accuracy: 0.8274\n",
"Epoch 236/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1437 - accuracy: 0.8844\n",
"Epoch 00236: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1437 - accuracy: 0.8844 - val_loss: 0.2891 - val_accuracy: 0.7387\n",
"Epoch 237/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1371 - accuracy: 0.8911\n",
"Epoch 00237: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1371 - accuracy: 0.8911 - val_loss: 0.1889 - val_accuracy: 0.8523\n",
"Epoch 238/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.8864\n",
"Epoch 00238: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1401 - accuracy: 0.8864 - val_loss: 0.1603 - val_accuracy: 0.8787\n",
"Epoch 239/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1430 - accuracy: 0.8890\n",
"Epoch 00239: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1430 - accuracy: 0.8890 - val_loss: 0.1902 - val_accuracy: 0.8445\n",
"Epoch 240/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.8900\n",
"Epoch 00240: accuracy did not improve from 0.89363\n",
"61/60 [==============================] - 20s 322ms/step - loss: 0.1401 - accuracy: 0.8900 - val_loss: 0.2038 - val_accuracy: 0.8243\n",
"Epoch 241/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1338 - accuracy: 0.8941\n",
"Epoch 00241: accuracy improved from 0.89363 to 0.89414, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-241-0.89.hdf5\n",
"61/60 [==============================] - 25s 417ms/step - loss: 0.1338 - accuracy: 0.8941 - val_loss: 0.1765 - val_accuracy: 0.8631\n",
"Epoch 242/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1403 - accuracy: 0.8921\n",
"Epoch 00242: accuracy did not improve from 0.89414\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1403 - accuracy: 0.8921 - val_loss: 0.2083 - val_accuracy: 0.8243\n",
"Epoch 243/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.8777\n",
"Epoch 00243: accuracy did not improve from 0.89414\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1473 - accuracy: 0.8777 - val_loss: 0.2121 - val_accuracy: 0.8212\n",
"Epoch 244/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1314 - accuracy: 0.8962\n",
"Epoch 00244: accuracy improved from 0.89414 to 0.89620, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-244-0.90.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1314 - accuracy: 0.8962 - val_loss: 0.2323 - val_accuracy: 0.8056\n",
"Epoch 245/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1293 - accuracy: 0.8993\n",
"Epoch 00245: accuracy improved from 0.89620 to 0.89928, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-245-0.90.hdf5\n",
"61/60 [==============================] - 22s 365ms/step - loss: 0.1293 - accuracy: 0.8993 - val_loss: 0.2125 - val_accuracy: 0.8258\n",
"Epoch 246/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1352 - accuracy: 0.8885\n",
"Epoch 00246: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1352 - accuracy: 0.8885 - val_loss: 0.2259 - val_accuracy: 0.8212\n",
"Epoch 247/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.8875\n",
"Epoch 00247: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1401 - accuracy: 0.8875 - val_loss: 0.2053 - val_accuracy: 0.8320\n",
"Epoch 248/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1384 - accuracy: 0.8900\n",
"Epoch 00248: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1384 - accuracy: 0.8900 - val_loss: 0.1906 - val_accuracy: 0.8383\n",
"Epoch 249/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1453 - accuracy: 0.8787\n",
"Epoch 00249: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1453 - accuracy: 0.8787 - val_loss: 0.2084 - val_accuracy: 0.8258\n",
"Epoch 250/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1403 - accuracy: 0.8936\n",
"Epoch 00250: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1403 - accuracy: 0.8936 - val_loss: 0.1959 - val_accuracy: 0.8476\n",
"Epoch 251/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1321 - accuracy: 0.8972\n",
"Epoch 00251: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1321 - accuracy: 0.8972 - val_loss: 0.1975 - val_accuracy: 0.8445\n",
"Epoch 252/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1347 - accuracy: 0.8972\n",
"Epoch 00252: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1347 - accuracy: 0.8972 - val_loss: 0.2054 - val_accuracy: 0.8274\n",
"Epoch 253/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1336 - accuracy: 0.8936\n",
"Epoch 00253: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1336 - accuracy: 0.8936 - val_loss: 0.2020 - val_accuracy: 0.8429\n",
"Epoch 254/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1370 - accuracy: 0.8900\n",
"Epoch 00254: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1370 - accuracy: 0.8900 - val_loss: 0.2521 - val_accuracy: 0.7916\n",
"Epoch 255/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1312 - accuracy: 0.8983\n",
"Epoch 00255: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1312 - accuracy: 0.8983 - val_loss: 0.1896 - val_accuracy: 0.8398\n",
"Epoch 256/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1375 - accuracy: 0.8941\n",
"Epoch 00256: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1375 - accuracy: 0.8941 - val_loss: 0.1770 - val_accuracy: 0.8600\n",
"Epoch 257/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1355 - accuracy: 0.8926\n",
"Epoch 00257: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1355 - accuracy: 0.8926 - val_loss: 0.2099 - val_accuracy: 0.8320\n",
"Epoch 258/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1363 - accuracy: 0.8916\n",
"Epoch 00258: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1363 - accuracy: 0.8916 - val_loss: 0.2319 - val_accuracy: 0.8056\n",
"Epoch 259/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1398 - accuracy: 0.8849\n",
"Epoch 00259: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1398 - accuracy: 0.8849 - val_loss: 0.2072 - val_accuracy: 0.8320\n",
"Epoch 260/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1363 - accuracy: 0.8844\n",
"Epoch 00260: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1363 - accuracy: 0.8844 - val_loss: 0.1863 - val_accuracy: 0.8336\n",
"Epoch 261/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1391 - accuracy: 0.8849\n",
"Epoch 00261: accuracy did not improve from 0.89928\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1391 - accuracy: 0.8849 - val_loss: 0.1563 - val_accuracy: 0.8927\n",
"Epoch 262/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1307 - accuracy: 0.9018\n",
"Epoch 00262: accuracy improved from 0.89928 to 0.90185, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-262-0.90.hdf5\n",
"61/60 [==============================] - 22s 366ms/step - loss: 0.1307 - accuracy: 0.9018 - val_loss: 0.1693 - val_accuracy: 0.8725\n",
"Epoch 263/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1338 - accuracy: 0.8869\n",
"Epoch 00263: accuracy did not improve from 0.90185\n",
"61/60 [==============================] - 19s 306ms/step - loss: 0.1338 - accuracy: 0.8869 - val_loss: 0.2103 - val_accuracy: 0.8243\n",
"Epoch 264/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1294 - accuracy: 0.8972\n",
"Epoch 00264: accuracy did not improve from 0.90185\n",
"61/60 [==============================] - 21s 339ms/step - loss: 0.1294 - accuracy: 0.8972 - val_loss: 0.1991 - val_accuracy: 0.8320\n",
"Epoch 265/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1282 - accuracy: 0.9065\n",
"Epoch 00265: accuracy improved from 0.90185 to 0.90647, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-265-0.91.hdf5\n",
"61/60 [==============================] - 24s 387ms/step - loss: 0.1282 - accuracy: 0.9065 - val_loss: 0.1698 - val_accuracy: 0.8756\n",
"Epoch 266/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1370 - accuracy: 0.8869\n",
"Epoch 00266: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1370 - accuracy: 0.8869 - val_loss: 0.1742 - val_accuracy: 0.8538\n",
"Epoch 267/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1320 - accuracy: 0.8936\n",
"Epoch 00267: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1320 - accuracy: 0.8936 - val_loss: 0.1497 - val_accuracy: 0.8880\n",
"Epoch 268/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1270 - accuracy: 0.8988\n",
"Epoch 00268: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1270 - accuracy: 0.8988 - val_loss: 0.1822 - val_accuracy: 0.8538\n",
"Epoch 269/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1385 - accuracy: 0.8905\n",
"Epoch 00269: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1385 - accuracy: 0.8905 - val_loss: 0.1931 - val_accuracy: 0.8383\n",
"Epoch 270/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1311 - accuracy: 0.8962\n",
"Epoch 00270: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1311 - accuracy: 0.8962 - val_loss: 0.1944 - val_accuracy: 0.8414\n",
"Epoch 271/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9054\n",
"Epoch 00271: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1290 - accuracy: 0.9054 - val_loss: 0.2039 - val_accuracy: 0.8227\n",
"Epoch 272/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1325 - accuracy: 0.8931\n",
"Epoch 00272: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1325 - accuracy: 0.8931 - val_loss: 0.1998 - val_accuracy: 0.8305\n",
"Epoch 273/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1402 - accuracy: 0.8936\n",
"Epoch 00273: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1402 - accuracy: 0.8936 - val_loss: 0.2335 - val_accuracy: 0.8025\n",
"Epoch 274/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1357 - accuracy: 0.8905\n",
"Epoch 00274: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1357 - accuracy: 0.8905 - val_loss: 0.1905 - val_accuracy: 0.8367\n",
"Epoch 275/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1301 - accuracy: 0.8947\n",
"Epoch 00275: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1301 - accuracy: 0.8947 - val_loss: 0.1563 - val_accuracy: 0.8818\n",
"Epoch 276/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1294 - accuracy: 0.8977\n",
"Epoch 00276: accuracy did not improve from 0.90647\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1294 - accuracy: 0.8977 - val_loss: 0.1656 - val_accuracy: 0.8849\n",
"Epoch 277/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1232 - accuracy: 0.9085\n",
"Epoch 00277: accuracy improved from 0.90647 to 0.90853, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-277-0.91.hdf5\n",
"61/60 [==============================] - 22s 365ms/step - loss: 0.1232 - accuracy: 0.9085 - val_loss: 0.1955 - val_accuracy: 0.8476\n",
"Epoch 278/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1394 - accuracy: 0.8911\n",
"Epoch 00278: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 20s 321ms/step - loss: 0.1394 - accuracy: 0.8911 - val_loss: 0.1871 - val_accuracy: 0.8554\n",
"Epoch 279/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1360 - accuracy: 0.8921\n",
"Epoch 00279: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 20s 329ms/step - loss: 0.1360 - accuracy: 0.8921 - val_loss: 0.1873 - val_accuracy: 0.8631\n",
"Epoch 280/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1349 - accuracy: 0.8983\n",
"Epoch 00280: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.1349 - accuracy: 0.8983 - val_loss: 0.1933 - val_accuracy: 0.8476\n",
"Epoch 281/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1399 - accuracy: 0.8921\n",
"Epoch 00281: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1399 - accuracy: 0.8921 - val_loss: 0.1950 - val_accuracy: 0.8367\n",
"Epoch 282/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1309 - accuracy: 0.8957\n",
"Epoch 00282: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1309 - accuracy: 0.8957 - val_loss: 0.1901 - val_accuracy: 0.8460\n",
"Epoch 283/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1373 - accuracy: 0.8828\n",
"Epoch 00283: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1373 - accuracy: 0.8828 - val_loss: 0.2012 - val_accuracy: 0.8289\n",
"Epoch 284/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1253 - accuracy: 0.9049\n",
"Epoch 00284: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1253 - accuracy: 0.9049 - val_loss: 0.1842 - val_accuracy: 0.8523\n",
"Epoch 285/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1219 - accuracy: 0.9049\n",
"Epoch 00285: accuracy did not improve from 0.90853\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1219 - accuracy: 0.9049 - val_loss: 0.1624 - val_accuracy: 0.8647\n",
"Epoch 286/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1260 - accuracy: 0.9096\n",
"Epoch 00286: accuracy improved from 0.90853 to 0.90956, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-286-0.91.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1260 - accuracy: 0.9096 - val_loss: 0.2057 - val_accuracy: 0.8227\n",
"Epoch 287/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1185 - accuracy: 0.9070\n",
"Epoch 00287: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1185 - accuracy: 0.9070 - val_loss: 0.2008 - val_accuracy: 0.8383\n",
"Epoch 288/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1316 - accuracy: 0.8972\n",
"Epoch 00288: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1316 - accuracy: 0.8972 - val_loss: 0.2547 - val_accuracy: 0.7885\n",
"Epoch 289/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1257 - accuracy: 0.8977\n",
"Epoch 00289: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1257 - accuracy: 0.8977 - val_loss: 0.1956 - val_accuracy: 0.8460\n",
"Epoch 290/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1367 - accuracy: 0.8947\n",
"Epoch 00290: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1367 - accuracy: 0.8947 - val_loss: 0.1926 - val_accuracy: 0.8523\n",
"Epoch 291/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1252 - accuracy: 0.9049\n",
"Epoch 00291: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1252 - accuracy: 0.9049 - val_loss: 0.1658 - val_accuracy: 0.8647\n",
"Epoch 292/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1303 - accuracy: 0.9039\n",
"Epoch 00292: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1303 - accuracy: 0.9039 - val_loss: 0.1685 - val_accuracy: 0.8787\n",
"Epoch 293/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1285 - accuracy: 0.8941\n",
"Epoch 00293: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1285 - accuracy: 0.8941 - val_loss: 0.1728 - val_accuracy: 0.8631\n",
"Epoch 294/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1300 - accuracy: 0.8957\n",
"Epoch 00294: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1300 - accuracy: 0.8957 - val_loss: 0.1833 - val_accuracy: 0.8445\n",
"Epoch 295/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1232 - accuracy: 0.9024\n",
"Epoch 00295: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1232 - accuracy: 0.9024 - val_loss: 0.1476 - val_accuracy: 0.8927\n",
"Epoch 296/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1308 - accuracy: 0.8941\n",
"Epoch 00296: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 21s 342ms/step - loss: 0.1308 - accuracy: 0.8941 - val_loss: 0.1365 - val_accuracy: 0.8927\n",
"Epoch 297/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1244 - accuracy: 0.9018\n",
"Epoch 00297: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 19s 318ms/step - loss: 0.1244 - accuracy: 0.9018 - val_loss: 0.1798 - val_accuracy: 0.8678\n",
"Epoch 298/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1165 - accuracy: 0.9080\n",
"Epoch 00298: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1165 - accuracy: 0.9080 - val_loss: 0.1774 - val_accuracy: 0.8600\n",
"Epoch 299/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1265 - accuracy: 0.8957\n",
"Epoch 00299: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1265 - accuracy: 0.8957 - val_loss: 0.1814 - val_accuracy: 0.8585\n",
"Epoch 300/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1288 - accuracy: 0.8941\n",
"Epoch 00300: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1288 - accuracy: 0.8941 - val_loss: 0.2175 - val_accuracy: 0.8103\n",
"Epoch 301/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1209 - accuracy: 0.9085\n",
"Epoch 00301: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1209 - accuracy: 0.9085 - val_loss: 0.2069 - val_accuracy: 0.8289\n",
"Epoch 302/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1273 - accuracy: 0.8993\n",
"Epoch 00302: accuracy did not improve from 0.90956\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1273 - accuracy: 0.8993 - val_loss: 0.2033 - val_accuracy: 0.8305\n",
"Epoch 303/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1175 - accuracy: 0.9121\n",
"Epoch 00303: accuracy improved from 0.90956 to 0.91213, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-303-0.91.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1175 - accuracy: 0.9121 - val_loss: 0.2001 - val_accuracy: 0.8227\n",
"Epoch 304/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1276 - accuracy: 0.8957\n",
"Epoch 00304: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1276 - accuracy: 0.8957 - val_loss: 0.2101 - val_accuracy: 0.8087\n",
"Epoch 305/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1255 - accuracy: 0.8957\n",
"Epoch 00305: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1255 - accuracy: 0.8957 - val_loss: 0.2324 - val_accuracy: 0.7963\n",
"Epoch 306/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1214 - accuracy: 0.9044\n",
"Epoch 00306: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1214 - accuracy: 0.9044 - val_loss: 0.1598 - val_accuracy: 0.8787\n",
"Epoch 307/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1239 - accuracy: 0.9039\n",
"Epoch 00307: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1239 - accuracy: 0.9039 - val_loss: 0.1596 - val_accuracy: 0.8771\n",
"Epoch 308/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1200 - accuracy: 0.9034\n",
"Epoch 00308: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1200 - accuracy: 0.9034 - val_loss: 0.1754 - val_accuracy: 0.8616\n",
"Epoch 309/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1165 - accuracy: 0.9044\n",
"Epoch 00309: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1165 - accuracy: 0.9044 - val_loss: 0.1790 - val_accuracy: 0.8631\n",
"Epoch 310/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1288 - accuracy: 0.8947\n",
"Epoch 00310: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1288 - accuracy: 0.8947 - val_loss: 0.1896 - val_accuracy: 0.8414\n",
"Epoch 311/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1209 - accuracy: 0.9054\n",
"Epoch 00311: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1209 - accuracy: 0.9054 - val_loss: 0.1761 - val_accuracy: 0.8631\n",
"Epoch 312/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1217 - accuracy: 0.9029\n",
"Epoch 00312: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1217 - accuracy: 0.9029 - val_loss: 0.1768 - val_accuracy: 0.8491\n",
"Epoch 313/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1241 - accuracy: 0.8977\n",
"Epoch 00313: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1241 - accuracy: 0.8977 - val_loss: 0.2140 - val_accuracy: 0.8196\n",
"Epoch 314/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1267 - accuracy: 0.8936\n",
"Epoch 00314: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 19s 315ms/step - loss: 0.1267 - accuracy: 0.8936 - val_loss: 0.2148 - val_accuracy: 0.8196\n",
"Epoch 315/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1314 - accuracy: 0.8977\n",
"Epoch 00315: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 20s 332ms/step - loss: 0.1314 - accuracy: 0.8977 - val_loss: 0.2304 - val_accuracy: 0.7994\n",
"Epoch 316/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1311 - accuracy: 0.8911\n",
"Epoch 00316: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.1311 - accuracy: 0.8911 - val_loss: 0.1911 - val_accuracy: 0.8491\n",
"Epoch 317/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1276 - accuracy: 0.9034\n",
"Epoch 00317: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1276 - accuracy: 0.9034 - val_loss: 0.1684 - val_accuracy: 0.8616\n",
"Epoch 318/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1195 - accuracy: 0.9070\n",
"Epoch 00318: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1195 - accuracy: 0.9070 - val_loss: 0.1523 - val_accuracy: 0.8725\n",
"Epoch 319/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1201 - accuracy: 0.9003\n",
"Epoch 00319: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1201 - accuracy: 0.9003 - val_loss: 0.1589 - val_accuracy: 0.8818\n",
"Epoch 320/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1155 - accuracy: 0.9080\n",
"Epoch 00320: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1155 - accuracy: 0.9080 - val_loss: 0.1917 - val_accuracy: 0.8445\n",
"Epoch 321/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.9049\n",
"Epoch 00321: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1242 - accuracy: 0.9049 - val_loss: 0.2137 - val_accuracy: 0.8320\n",
"Epoch 322/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1322 - accuracy: 0.8890\n",
"Epoch 00322: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1322 - accuracy: 0.8890 - val_loss: 0.1538 - val_accuracy: 0.8709\n",
"Epoch 323/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1293 - accuracy: 0.9039\n",
"Epoch 00323: accuracy did not improve from 0.91213\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1293 - accuracy: 0.9039 - val_loss: 0.1527 - val_accuracy: 0.8802\n",
"Epoch 324/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1097 - accuracy: 0.9203\n",
"Epoch 00324: accuracy improved from 0.91213 to 0.92035, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-324-0.92.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.1097 - accuracy: 0.9203 - val_loss: 0.1854 - val_accuracy: 0.8367\n",
"Epoch 325/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1182 - accuracy: 0.9116\n",
"Epoch 00325: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1182 - accuracy: 0.9116 - val_loss: 0.1844 - val_accuracy: 0.8398\n",
"Epoch 326/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1226 - accuracy: 0.9008\n",
"Epoch 00326: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1226 - accuracy: 0.9008 - val_loss: 0.1735 - val_accuracy: 0.8569\n",
"Epoch 327/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1209 - accuracy: 0.9096\n",
"Epoch 00327: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1209 - accuracy: 0.9096 - val_loss: 0.1684 - val_accuracy: 0.8600\n",
"Epoch 328/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1224 - accuracy: 0.9060\n",
"Epoch 00328: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1224 - accuracy: 0.9060 - val_loss: 0.1776 - val_accuracy: 0.8600\n",
"Epoch 329/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1288 - accuracy: 0.9060\n",
"Epoch 00329: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 288ms/step - loss: 0.1288 - accuracy: 0.9060 - val_loss: 0.1999 - val_accuracy: 0.8305\n",
"Epoch 330/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1176 - accuracy: 0.9137\n",
"Epoch 00330: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1176 - accuracy: 0.9137 - val_loss: 0.1953 - val_accuracy: 0.8398\n",
"Epoch 331/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1166 - accuracy: 0.9075\n",
"Epoch 00331: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1166 - accuracy: 0.9075 - val_loss: 0.1564 - val_accuracy: 0.8709\n",
"Epoch 332/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.9096\n",
"Epoch 00332: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 20s 333ms/step - loss: 0.1136 - accuracy: 0.9096 - val_loss: 0.1632 - val_accuracy: 0.8709\n",
"Epoch 333/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1267 - accuracy: 0.8895\n",
"Epoch 00333: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 20s 327ms/step - loss: 0.1267 - accuracy: 0.8895 - val_loss: 0.1451 - val_accuracy: 0.8834\n",
"Epoch 334/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1177 - accuracy: 0.9070\n",
"Epoch 00334: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1177 - accuracy: 0.9070 - val_loss: 0.1522 - val_accuracy: 0.8756\n",
"Epoch 335/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1247 - accuracy: 0.9111\n",
"Epoch 00335: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1247 - accuracy: 0.9111 - val_loss: 0.1961 - val_accuracy: 0.8367\n",
"Epoch 336/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1124 - accuracy: 0.9142\n",
"Epoch 00336: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1124 - accuracy: 0.9142 - val_loss: 0.1856 - val_accuracy: 0.8460\n",
"Epoch 337/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1208 - accuracy: 0.9070\n",
"Epoch 00337: accuracy did not improve from 0.92035\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1208 - accuracy: 0.9070 - val_loss: 0.1540 - val_accuracy: 0.8678\n",
"Epoch 338/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1115 - accuracy: 0.9209\n",
"Epoch 00338: accuracy improved from 0.92035 to 0.92086, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-338-0.92.hdf5\n",
"61/60 [==============================] - 22s 363ms/step - loss: 0.1115 - accuracy: 0.9209 - val_loss: 0.2004 - val_accuracy: 0.8258\n",
"Epoch 339/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1287 - accuracy: 0.8957\n",
"Epoch 00339: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1287 - accuracy: 0.8957 - val_loss: 0.1789 - val_accuracy: 0.8476\n",
"Epoch 340/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.8983\n",
"Epoch 00340: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1242 - accuracy: 0.8983 - val_loss: 0.1424 - val_accuracy: 0.8849\n",
"Epoch 341/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1281 - accuracy: 0.9034\n",
"Epoch 00341: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1281 - accuracy: 0.9034 - val_loss: 0.1450 - val_accuracy: 0.8802\n",
"Epoch 342/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1077 - accuracy: 0.9168\n",
"Epoch 00342: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1077 - accuracy: 0.9168 - val_loss: 0.1614 - val_accuracy: 0.8694\n",
"Epoch 343/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1102 - accuracy: 0.9203\n",
"Epoch 00343: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1102 - accuracy: 0.9203 - val_loss: 0.1629 - val_accuracy: 0.8631\n",
"Epoch 344/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1265 - accuracy: 0.8993\n",
"Epoch 00344: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1265 - accuracy: 0.8993 - val_loss: 0.2001 - val_accuracy: 0.8305\n",
"Epoch 345/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9126\n",
"Epoch 00345: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1134 - accuracy: 0.9126 - val_loss: 0.1859 - val_accuracy: 0.8445\n",
"Epoch 346/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1214 - accuracy: 0.9024\n",
"Epoch 00346: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1214 - accuracy: 0.9024 - val_loss: 0.1803 - val_accuracy: 0.8554\n",
"Epoch 347/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1197 - accuracy: 0.9060\n",
"Epoch 00347: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1197 - accuracy: 0.9060 - val_loss: 0.1663 - val_accuracy: 0.8694\n",
"Epoch 348/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1159 - accuracy: 0.9080\n",
"Epoch 00348: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.1159 - accuracy: 0.9080 - val_loss: 0.1514 - val_accuracy: 0.8787\n",
"Epoch 349/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1137 - accuracy: 0.9132\n",
"Epoch 00349: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 21s 341ms/step - loss: 0.1137 - accuracy: 0.9132 - val_loss: 0.1813 - val_accuracy: 0.8523\n",
"Epoch 350/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1175 - accuracy: 0.9075\n",
"Epoch 00350: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 19s 311ms/step - loss: 0.1175 - accuracy: 0.9075 - val_loss: 0.1827 - val_accuracy: 0.8460\n",
"Epoch 351/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1195 - accuracy: 0.9085\n",
"Epoch 00351: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1195 - accuracy: 0.9085 - val_loss: 0.1822 - val_accuracy: 0.8523\n",
"Epoch 352/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1193 - accuracy: 0.9065\n",
"Epoch 00352: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1193 - accuracy: 0.9065 - val_loss: 0.1815 - val_accuracy: 0.8554\n",
"Epoch 353/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1244 - accuracy: 0.9044\n",
"Epoch 00353: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1244 - accuracy: 0.9044 - val_loss: 0.1789 - val_accuracy: 0.8491\n",
"Epoch 354/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9101\n",
"Epoch 00354: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1134 - accuracy: 0.9101 - val_loss: 0.2139 - val_accuracy: 0.8134\n",
"Epoch 355/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1176 - accuracy: 0.9070\n",
"Epoch 00355: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1176 - accuracy: 0.9070 - val_loss: 0.1838 - val_accuracy: 0.8523\n",
"Epoch 356/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1089 - accuracy: 0.9183\n",
"Epoch 00356: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1089 - accuracy: 0.9183 - val_loss: 0.1925 - val_accuracy: 0.8445\n",
"Epoch 357/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1044 - accuracy: 0.9183\n",
"Epoch 00357: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1044 - accuracy: 0.9183 - val_loss: 0.1948 - val_accuracy: 0.8445\n",
"Epoch 358/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1077 - accuracy: 0.9126\n",
"Epoch 00358: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1077 - accuracy: 0.9126 - val_loss: 0.2164 - val_accuracy: 0.8196\n",
"Epoch 359/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1153 - accuracy: 0.9111\n",
"Epoch 00359: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1153 - accuracy: 0.9111 - val_loss: 0.2312 - val_accuracy: 0.8087\n",
"Epoch 360/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1165 - accuracy: 0.9106\n",
"Epoch 00360: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1165 - accuracy: 0.9106 - val_loss: 0.1620 - val_accuracy: 0.8787\n",
"Epoch 361/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 0.9168\n",
"Epoch 00361: accuracy did not improve from 0.92086\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1018 - accuracy: 0.9168 - val_loss: 0.1452 - val_accuracy: 0.8927\n",
"Epoch 362/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1067 - accuracy: 0.9214\n",
"Epoch 00362: accuracy improved from 0.92086 to 0.92138, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-362-0.92.hdf5\n",
"61/60 [==============================] - 23s 378ms/step - loss: 0.1067 - accuracy: 0.9214 - val_loss: 0.1588 - val_accuracy: 0.8771\n",
"Epoch 363/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1113 - accuracy: 0.9198\n",
"Epoch 00363: accuracy did not improve from 0.92138\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1113 - accuracy: 0.9198 - val_loss: 0.1577 - val_accuracy: 0.8709\n",
"Epoch 364/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1158 - accuracy: 0.9111\n",
"Epoch 00364: accuracy did not improve from 0.92138\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1158 - accuracy: 0.9111 - val_loss: 0.1884 - val_accuracy: 0.8429\n",
"Epoch 365/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1132 - accuracy: 0.9126\n",
"Epoch 00365: accuracy did not improve from 0.92138\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1132 - accuracy: 0.9126 - val_loss: 0.1913 - val_accuracy: 0.8445\n",
"Epoch 366/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1122 - accuracy: 0.9157\n",
"Epoch 00366: accuracy did not improve from 0.92138\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1122 - accuracy: 0.9157 - val_loss: 0.2377 - val_accuracy: 0.8196\n",
"Epoch 367/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1101 - accuracy: 0.9132\n",
"Epoch 00367: accuracy did not improve from 0.92138\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1101 - accuracy: 0.9132 - val_loss: 0.2239 - val_accuracy: 0.8383\n",
"Epoch 368/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1087 - accuracy: 0.9183\n",
"Epoch 00368: accuracy did not improve from 0.92138\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1087 - accuracy: 0.9183 - val_loss: 0.2208 - val_accuracy: 0.8103\n",
"Epoch 369/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1028 - accuracy: 0.9229\n",
"Epoch 00369: accuracy improved from 0.92138 to 0.92292, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-369-0.92.hdf5\n",
"61/60 [==============================] - 22s 366ms/step - loss: 0.1028 - accuracy: 0.9229 - val_loss: 0.1794 - val_accuracy: 0.8709\n",
"Epoch 370/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1082 - accuracy: 0.9183\n",
"Epoch 00370: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1082 - accuracy: 0.9183 - val_loss: 0.1622 - val_accuracy: 0.8865\n",
"Epoch 371/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1109 - accuracy: 0.9142\n",
"Epoch 00371: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1109 - accuracy: 0.9142 - val_loss: 0.1720 - val_accuracy: 0.8616\n",
"Epoch 372/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1089 - accuracy: 0.9173\n",
"Epoch 00372: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1089 - accuracy: 0.9173 - val_loss: 0.2152 - val_accuracy: 0.8212\n",
"Epoch 373/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1268 - accuracy: 0.8967\n",
"Epoch 00373: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1268 - accuracy: 0.8967 - val_loss: 0.1912 - val_accuracy: 0.8460\n",
"Epoch 374/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1092 - accuracy: 0.9142\n",
"Epoch 00374: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1092 - accuracy: 0.9142 - val_loss: 0.1917 - val_accuracy: 0.8507\n",
"Epoch 375/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1043 - accuracy: 0.9168\n",
"Epoch 00375: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1043 - accuracy: 0.9168 - val_loss: 0.2120 - val_accuracy: 0.8429\n",
"Epoch 376/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1148 - accuracy: 0.9132\n",
"Epoch 00376: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1148 - accuracy: 0.9132 - val_loss: 0.2290 - val_accuracy: 0.8118\n",
"Epoch 377/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1092 - accuracy: 0.9157\n",
"Epoch 00377: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1092 - accuracy: 0.9157 - val_loss: 0.2097 - val_accuracy: 0.8383\n",
"Epoch 378/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1079 - accuracy: 0.9188\n",
"Epoch 00378: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1079 - accuracy: 0.9188 - val_loss: 0.1674 - val_accuracy: 0.8663\n",
"Epoch 379/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1081 - accuracy: 0.9183\n",
"Epoch 00379: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1081 - accuracy: 0.9183 - val_loss: 0.1627 - val_accuracy: 0.8616\n",
"Epoch 380/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1028 - accuracy: 0.9147\n",
"Epoch 00380: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1028 - accuracy: 0.9147 - val_loss: 0.1745 - val_accuracy: 0.8554\n",
"Epoch 381/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9157\n",
"Epoch 00381: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1069 - accuracy: 0.9157 - val_loss: 0.1724 - val_accuracy: 0.8600\n",
"Epoch 382/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1017 - accuracy: 0.9142\n",
"Epoch 00382: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1017 - accuracy: 0.9142 - val_loss: 0.1651 - val_accuracy: 0.8725\n",
"Epoch 383/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1039 - accuracy: 0.9183\n",
"Epoch 00383: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1039 - accuracy: 0.9183 - val_loss: 0.1655 - val_accuracy: 0.8616\n",
"Epoch 384/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1027 - accuracy: 0.9183\n",
"Epoch 00384: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1027 - accuracy: 0.9183 - val_loss: 0.1592 - val_accuracy: 0.8709\n",
"Epoch 385/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1114 - accuracy: 0.9162\n",
"Epoch 00385: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1114 - accuracy: 0.9162 - val_loss: 0.1609 - val_accuracy: 0.8740\n",
"Epoch 386/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1073 - accuracy: 0.9147\n",
"Epoch 00386: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 21s 341ms/step - loss: 0.1073 - accuracy: 0.9147 - val_loss: 0.1547 - val_accuracy: 0.8725\n",
"Epoch 387/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1036 - accuracy: 0.9209\n",
"Epoch 00387: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1036 - accuracy: 0.9209 - val_loss: 0.1496 - val_accuracy: 0.8818\n",
"Epoch 388/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1016 - accuracy: 0.9229\n",
"Epoch 00388: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1016 - accuracy: 0.9229 - val_loss: 0.1433 - val_accuracy: 0.8771\n",
"Epoch 389/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1132 - accuracy: 0.9168\n",
"Epoch 00389: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1132 - accuracy: 0.9168 - val_loss: 0.1502 - val_accuracy: 0.8740\n",
"Epoch 390/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1130 - accuracy: 0.9162\n",
"Epoch 00390: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1130 - accuracy: 0.9162 - val_loss: 0.1861 - val_accuracy: 0.8554\n",
"Epoch 391/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1077 - accuracy: 0.9183\n",
"Epoch 00391: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1077 - accuracy: 0.9183 - val_loss: 0.1802 - val_accuracy: 0.8538\n",
"Epoch 392/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1051 - accuracy: 0.9198\n",
"Epoch 00392: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1051 - accuracy: 0.9198 - val_loss: 0.1722 - val_accuracy: 0.8616\n",
"Epoch 393/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1180 - accuracy: 0.9065\n",
"Epoch 00393: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1180 - accuracy: 0.9065 - val_loss: 0.1684 - val_accuracy: 0.8585\n",
"Epoch 394/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1068 - accuracy: 0.9173\n",
"Epoch 00394: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1068 - accuracy: 0.9173 - val_loss: 0.1675 - val_accuracy: 0.8616\n",
"Epoch 395/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1040 - accuracy: 0.9203\n",
"Epoch 00395: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1040 - accuracy: 0.9203 - val_loss: 0.1992 - val_accuracy: 0.8227\n",
"Epoch 396/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1109 - accuracy: 0.9096\n",
"Epoch 00396: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1109 - accuracy: 0.9096 - val_loss: 0.1886 - val_accuracy: 0.8367\n",
"Epoch 397/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1165 - accuracy: 0.9116\n",
"Epoch 00397: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1165 - accuracy: 0.9116 - val_loss: 0.2004 - val_accuracy: 0.8274\n",
"Epoch 398/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1060 - accuracy: 0.9219\n",
"Epoch 00398: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1060 - accuracy: 0.9219 - val_loss: 0.2518 - val_accuracy: 0.8025\n",
"Epoch 399/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1116 - accuracy: 0.9142\n",
"Epoch 00399: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1116 - accuracy: 0.9142 - val_loss: 0.2098 - val_accuracy: 0.8398\n",
"Epoch 400/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1106 - accuracy: 0.9132\n",
"Epoch 00400: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 20s 322ms/step - loss: 0.1106 - accuracy: 0.9132 - val_loss: 0.1802 - val_accuracy: 0.8460\n",
"Epoch 401/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1077 - accuracy: 0.9183\n",
"Epoch 00401: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1077 - accuracy: 0.9183 - val_loss: 0.1723 - val_accuracy: 0.8647\n",
"Epoch 402/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1160 - accuracy: 0.9065\n",
"Epoch 00402: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1160 - accuracy: 0.9065 - val_loss: 0.1488 - val_accuracy: 0.8818\n",
"Epoch 403/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1061 - accuracy: 0.9157\n",
"Epoch 00403: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1061 - accuracy: 0.9157 - val_loss: 0.1615 - val_accuracy: 0.8647\n",
"Epoch 404/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1122 - accuracy: 0.9096\n",
"Epoch 00404: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1122 - accuracy: 0.9096 - val_loss: 0.1665 - val_accuracy: 0.8616\n",
"Epoch 405/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1013 - accuracy: 0.9168\n",
"Epoch 00405: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1013 - accuracy: 0.9168 - val_loss: 0.1807 - val_accuracy: 0.8476\n",
"Epoch 406/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9209\n",
"Epoch 00406: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1083 - accuracy: 0.9209 - val_loss: 0.1845 - val_accuracy: 0.8585\n",
"Epoch 407/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1115 - accuracy: 0.9085\n",
"Epoch 00407: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1115 - accuracy: 0.9085 - val_loss: 0.2358 - val_accuracy: 0.8212\n",
"Epoch 408/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1049 - accuracy: 0.9214\n",
"Epoch 00408: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1049 - accuracy: 0.9214 - val_loss: 0.2053 - val_accuracy: 0.8320\n",
"Epoch 409/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9070\n",
"Epoch 00409: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1123 - accuracy: 0.9070 - val_loss: 0.1978 - val_accuracy: 0.8414\n",
"Epoch 410/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1072 - accuracy: 0.9126\n",
"Epoch 00410: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1072 - accuracy: 0.9126 - val_loss: 0.1909 - val_accuracy: 0.8554\n",
"Epoch 411/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1073 - accuracy: 0.9173\n",
"Epoch 00411: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1073 - accuracy: 0.9173 - val_loss: 0.1752 - val_accuracy: 0.8678\n",
"Epoch 412/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1102 - accuracy: 0.9142\n",
"Epoch 00412: accuracy did not improve from 0.92292\n",
"61/60 [==============================] - 18s 301ms/step - loss: 0.1102 - accuracy: 0.9142 - val_loss: 0.1566 - val_accuracy: 0.8756\n",
"Epoch 413/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1043 - accuracy: 0.9234\n",
"Epoch 00413: accuracy improved from 0.92292 to 0.92343, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-413-0.92.hdf5\n",
"61/60 [==============================] - 24s 402ms/step - loss: 0.1043 - accuracy: 0.9234 - val_loss: 0.1595 - val_accuracy: 0.8756\n",
"Epoch 414/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0987 - accuracy: 0.9327\n",
"Epoch 00414: accuracy improved from 0.92343 to 0.93268, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-414-0.93.hdf5\n",
"61/60 [==============================] - 22s 367ms/step - loss: 0.0987 - accuracy: 0.9327 - val_loss: 0.1474 - val_accuracy: 0.8849\n",
"Epoch 415/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1021 - accuracy: 0.9193\n",
"Epoch 00415: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1021 - accuracy: 0.9193 - val_loss: 0.1972 - val_accuracy: 0.8305\n",
"Epoch 416/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1015 - accuracy: 0.9168\n",
"Epoch 00416: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1015 - accuracy: 0.9168 - val_loss: 0.2011 - val_accuracy: 0.8243\n",
"Epoch 417/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1030 - accuracy: 0.9255\n",
"Epoch 00417: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1030 - accuracy: 0.9255 - val_loss: 0.1832 - val_accuracy: 0.8445\n",
"Epoch 418/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1140 - accuracy: 0.9152\n",
"Epoch 00418: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.1140 - accuracy: 0.9152 - val_loss: 0.1952 - val_accuracy: 0.8289\n",
"Epoch 419/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1118 - accuracy: 0.9132\n",
"Epoch 00419: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1118 - accuracy: 0.9132 - val_loss: 0.1945 - val_accuracy: 0.8414\n",
"Epoch 420/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1062 - accuracy: 0.9178\n",
"Epoch 00420: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1062 - accuracy: 0.9178 - val_loss: 0.1856 - val_accuracy: 0.8507\n",
"Epoch 421/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1183 - accuracy: 0.9132\n",
"Epoch 00421: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1183 - accuracy: 0.9132 - val_loss: 0.1914 - val_accuracy: 0.8351\n",
"Epoch 422/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1055 - accuracy: 0.9198\n",
"Epoch 00422: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1055 - accuracy: 0.9198 - val_loss: 0.1864 - val_accuracy: 0.8507\n",
"Epoch 423/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 0.9255\n",
"Epoch 00423: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1018 - accuracy: 0.9255 - val_loss: 0.1621 - val_accuracy: 0.8678\n",
"Epoch 424/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1089 - accuracy: 0.9085\n",
"Epoch 00424: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1089 - accuracy: 0.9085 - val_loss: 0.1764 - val_accuracy: 0.8538\n",
"Epoch 425/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1030 - accuracy: 0.9209\n",
"Epoch 00425: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1030 - accuracy: 0.9209 - val_loss: 0.1707 - val_accuracy: 0.8694\n",
"Epoch 426/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9111\n",
"Epoch 00426: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1134 - accuracy: 0.9111 - val_loss: 0.1863 - val_accuracy: 0.8460\n",
"Epoch 427/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1109 - accuracy: 0.9132\n",
"Epoch 00427: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1109 - accuracy: 0.9132 - val_loss: 0.1748 - val_accuracy: 0.8491\n",
"Epoch 428/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1072 - accuracy: 0.9142\n",
"Epoch 00428: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 299ms/step - loss: 0.1072 - accuracy: 0.9142 - val_loss: 0.1871 - val_accuracy: 0.8507\n",
"Epoch 429/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1080 - accuracy: 0.9137\n",
"Epoch 00429: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1080 - accuracy: 0.9137 - val_loss: 0.1601 - val_accuracy: 0.8818\n",
"Epoch 430/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1094 - accuracy: 0.9096\n",
"Epoch 00430: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1094 - accuracy: 0.9096 - val_loss: 0.1623 - val_accuracy: 0.8709\n",
"Epoch 431/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1044 - accuracy: 0.9224\n",
"Epoch 00431: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1044 - accuracy: 0.9224 - val_loss: 0.1736 - val_accuracy: 0.8616\n",
"Epoch 432/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1058 - accuracy: 0.9203\n",
"Epoch 00432: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1058 - accuracy: 0.9203 - val_loss: 0.1974 - val_accuracy: 0.8383\n",
"Epoch 433/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1066 - accuracy: 0.9132\n",
"Epoch 00433: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1066 - accuracy: 0.9132 - val_loss: 0.1742 - val_accuracy: 0.8647\n",
"Epoch 434/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1020 - accuracy: 0.9157\n",
"Epoch 00434: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1020 - accuracy: 0.9157 - val_loss: 0.1640 - val_accuracy: 0.8678\n",
"Epoch 435/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1101 - accuracy: 0.9142\n",
"Epoch 00435: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.1101 - accuracy: 0.9142 - val_loss: 0.1438 - val_accuracy: 0.8818\n",
"Epoch 436/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.9219\n",
"Epoch 00436: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0998 - accuracy: 0.9219 - val_loss: 0.1652 - val_accuracy: 0.8756\n",
"Epoch 437/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1126 - accuracy: 0.9116\n",
"Epoch 00437: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1126 - accuracy: 0.9116 - val_loss: 0.2110 - val_accuracy: 0.8320\n",
"Epoch 438/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0990 - accuracy: 0.9239\n",
"Epoch 00438: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0990 - accuracy: 0.9239 - val_loss: 0.2599 - val_accuracy: 0.8040\n",
"Epoch 439/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1022 - accuracy: 0.9281\n",
"Epoch 00439: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1022 - accuracy: 0.9281 - val_loss: 0.2007 - val_accuracy: 0.8383\n",
"Epoch 440/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9229\n",
"Epoch 00440: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0972 - accuracy: 0.9229 - val_loss: 0.1911 - val_accuracy: 0.8398\n",
"Epoch 441/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1030 - accuracy: 0.9203\n",
"Epoch 00441: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1030 - accuracy: 0.9203 - val_loss: 0.1737 - val_accuracy: 0.8569\n",
"Epoch 442/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0959 - accuracy: 0.9224\n",
"Epoch 00442: accuracy did not improve from 0.93268\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0959 - accuracy: 0.9224 - val_loss: 0.1542 - val_accuracy: 0.8678\n",
"Epoch 443/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9332\n",
"Epoch 00443: accuracy improved from 0.93268 to 0.93320, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-443-0.93.hdf5\n",
"61/60 [==============================] - 22s 368ms/step - loss: 0.0963 - accuracy: 0.9332 - val_loss: 0.1394 - val_accuracy: 0.8787\n",
"Epoch 444/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0991 - accuracy: 0.9178\n",
"Epoch 00444: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0991 - accuracy: 0.9178 - val_loss: 0.1752 - val_accuracy: 0.8414\n",
"Epoch 445/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0962 - accuracy: 0.9219\n",
"Epoch 00445: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0962 - accuracy: 0.9219 - val_loss: 0.1882 - val_accuracy: 0.8476\n",
"Epoch 446/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0976 - accuracy: 0.9245\n",
"Epoch 00446: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0976 - accuracy: 0.9245 - val_loss: 0.1918 - val_accuracy: 0.8476\n",
"Epoch 447/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1098 - accuracy: 0.9162\n",
"Epoch 00447: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1098 - accuracy: 0.9162 - val_loss: 0.1775 - val_accuracy: 0.8523\n",
"Epoch 448/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0992 - accuracy: 0.9239\n",
"Epoch 00448: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0992 - accuracy: 0.9239 - val_loss: 0.1898 - val_accuracy: 0.8507\n",
"Epoch 449/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1027 - accuracy: 0.9203\n",
"Epoch 00449: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1027 - accuracy: 0.9203 - val_loss: 0.1492 - val_accuracy: 0.8802\n",
"Epoch 450/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1112 - accuracy: 0.9193\n",
"Epoch 00450: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.1112 - accuracy: 0.9193 - val_loss: 0.1855 - val_accuracy: 0.8460\n",
"Epoch 451/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9306\n",
"Epoch 00451: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0963 - accuracy: 0.9306 - val_loss: 0.1439 - val_accuracy: 0.8911\n",
"Epoch 452/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1007 - accuracy: 0.9250\n",
"Epoch 00452: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1007 - accuracy: 0.9250 - val_loss: 0.1637 - val_accuracy: 0.8740\n",
"Epoch 453/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1015 - accuracy: 0.9224\n",
"Epoch 00453: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1015 - accuracy: 0.9224 - val_loss: 0.1619 - val_accuracy: 0.8694\n",
"Epoch 454/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1022 - accuracy: 0.9234\n",
"Epoch 00454: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1022 - accuracy: 0.9234 - val_loss: 0.1421 - val_accuracy: 0.8974\n",
"Epoch 455/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0982 - accuracy: 0.9327\n",
"Epoch 00455: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0982 - accuracy: 0.9327 - val_loss: 0.1604 - val_accuracy: 0.8709\n",
"Epoch 456/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1073 - accuracy: 0.9209\n",
"Epoch 00456: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.1073 - accuracy: 0.9209 - val_loss: 0.1481 - val_accuracy: 0.8834\n",
"Epoch 457/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9173\n",
"Epoch 00457: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0988 - accuracy: 0.9173 - val_loss: 0.1641 - val_accuracy: 0.8647\n",
"Epoch 458/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0944 - accuracy: 0.9286\n",
"Epoch 00458: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0944 - accuracy: 0.9286 - val_loss: 0.1440 - val_accuracy: 0.8849\n",
"Epoch 459/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0912 - accuracy: 0.9327\n",
"Epoch 00459: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0912 - accuracy: 0.9327 - val_loss: 0.1489 - val_accuracy: 0.8818\n",
"Epoch 460/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0931 - accuracy: 0.9219\n",
"Epoch 00460: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0931 - accuracy: 0.9219 - val_loss: 0.1686 - val_accuracy: 0.8647\n",
"Epoch 461/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.9224\n",
"Epoch 00461: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0998 - accuracy: 0.9224 - val_loss: 0.2089 - val_accuracy: 0.8258\n",
"Epoch 462/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0964 - accuracy: 0.9291\n",
"Epoch 00462: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0964 - accuracy: 0.9291 - val_loss: 0.1898 - val_accuracy: 0.8383\n",
"Epoch 463/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0982 - accuracy: 0.9224\n",
"Epoch 00463: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0982 - accuracy: 0.9224 - val_loss: 0.1286 - val_accuracy: 0.8896\n",
"Epoch 464/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1032 - accuracy: 0.9173\n",
"Epoch 00464: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1032 - accuracy: 0.9173 - val_loss: 0.1564 - val_accuracy: 0.8663\n",
"Epoch 465/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0980 - accuracy: 0.9270\n",
"Epoch 00465: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0980 - accuracy: 0.9270 - val_loss: 0.1562 - val_accuracy: 0.8678\n",
"Epoch 466/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1074 - accuracy: 0.9147\n",
"Epoch 00466: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.1074 - accuracy: 0.9147 - val_loss: 0.1676 - val_accuracy: 0.8631\n",
"Epoch 467/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0980 - accuracy: 0.9219\n",
"Epoch 00467: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0980 - accuracy: 0.9219 - val_loss: 0.1835 - val_accuracy: 0.8538\n",
"Epoch 468/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1019 - accuracy: 0.9209\n",
"Epoch 00468: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1019 - accuracy: 0.9209 - val_loss: 0.1555 - val_accuracy: 0.8709\n",
"Epoch 469/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0911 - accuracy: 0.9301\n",
"Epoch 00469: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0911 - accuracy: 0.9301 - val_loss: 0.2186 - val_accuracy: 0.8212\n",
"Epoch 470/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1010 - accuracy: 0.9219\n",
"Epoch 00470: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1010 - accuracy: 0.9219 - val_loss: 0.1740 - val_accuracy: 0.8647\n",
"Epoch 471/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0994 - accuracy: 0.9234\n",
"Epoch 00471: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0994 - accuracy: 0.9234 - val_loss: 0.2213 - val_accuracy: 0.8289\n",
"Epoch 472/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9250\n",
"Epoch 00472: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0924 - accuracy: 0.9250 - val_loss: 0.1810 - val_accuracy: 0.8460\n",
"Epoch 473/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0926 - accuracy: 0.9275\n",
"Epoch 00473: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0926 - accuracy: 0.9275 - val_loss: 0.1850 - val_accuracy: 0.8585\n",
"Epoch 474/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0892 - accuracy: 0.9306\n",
"Epoch 00474: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0892 - accuracy: 0.9306 - val_loss: 0.1630 - val_accuracy: 0.8663\n",
"Epoch 475/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0919 - accuracy: 0.9327\n",
"Epoch 00475: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 289ms/step - loss: 0.0919 - accuracy: 0.9327 - val_loss: 0.1767 - val_accuracy: 0.8538\n",
"Epoch 476/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 0.9245\n",
"Epoch 00476: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0957 - accuracy: 0.9245 - val_loss: 0.1758 - val_accuracy: 0.8585\n",
"Epoch 477/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1002 - accuracy: 0.9245\n",
"Epoch 00477: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1002 - accuracy: 0.9245 - val_loss: 0.2096 - val_accuracy: 0.8274\n",
"Epoch 478/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0953 - accuracy: 0.9291\n",
"Epoch 00478: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0953 - accuracy: 0.9291 - val_loss: 0.2068 - val_accuracy: 0.8305\n",
"Epoch 479/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9229\n",
"Epoch 00479: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0963 - accuracy: 0.9229 - val_loss: 0.2114 - val_accuracy: 0.8383\n",
"Epoch 480/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9229\n",
"Epoch 00480: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 19s 311ms/step - loss: 0.0977 - accuracy: 0.9229 - val_loss: 0.2049 - val_accuracy: 0.8398\n",
"Epoch 481/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0942 - accuracy: 0.9255\n",
"Epoch 00481: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 301ms/step - loss: 0.0942 - accuracy: 0.9255 - val_loss: 0.1640 - val_accuracy: 0.8694\n",
"Epoch 482/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0948 - accuracy: 0.9275\n",
"Epoch 00482: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0948 - accuracy: 0.9275 - val_loss: 0.1851 - val_accuracy: 0.8507\n",
"Epoch 483/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0995 - accuracy: 0.9209\n",
"Epoch 00483: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0995 - accuracy: 0.9209 - val_loss: 0.2033 - val_accuracy: 0.8289\n",
"Epoch 484/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9270\n",
"Epoch 00484: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0936 - accuracy: 0.9270 - val_loss: 0.2077 - val_accuracy: 0.8351\n",
"Epoch 485/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0978 - accuracy: 0.9270\n",
"Epoch 00485: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0978 - accuracy: 0.9270 - val_loss: 0.1646 - val_accuracy: 0.8600\n",
"Epoch 486/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0980 - accuracy: 0.9255\n",
"Epoch 00486: accuracy did not improve from 0.93320\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0980 - accuracy: 0.9255 - val_loss: 0.2125 - val_accuracy: 0.8320\n",
"Epoch 487/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9368\n",
"Epoch 00487: accuracy improved from 0.93320 to 0.93679, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-487-0.94.hdf5\n",
"61/60 [==============================] - 22s 367ms/step - loss: 0.0895 - accuracy: 0.9368 - val_loss: 0.1913 - val_accuracy: 0.8429\n",
"Epoch 488/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0884 - accuracy: 0.9394\n",
"Epoch 00488: accuracy improved from 0.93679 to 0.93936, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-488-0.94.hdf5\n",
"61/60 [==============================] - 22s 368ms/step - loss: 0.0884 - accuracy: 0.9394 - val_loss: 0.2055 - val_accuracy: 0.8305\n",
"Epoch 489/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0951 - accuracy: 0.9224\n",
"Epoch 00489: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0951 - accuracy: 0.9224 - val_loss: 0.1736 - val_accuracy: 0.8600\n",
"Epoch 490/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0913 - accuracy: 0.9311\n",
"Epoch 00490: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0913 - accuracy: 0.9311 - val_loss: 0.1794 - val_accuracy: 0.8507\n",
"Epoch 491/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1000 - accuracy: 0.9183\n",
"Epoch 00491: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.1000 - accuracy: 0.9183 - val_loss: 0.1717 - val_accuracy: 0.8507\n",
"Epoch 492/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.1001 - accuracy: 0.9281\n",
"Epoch 00492: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.1001 - accuracy: 0.9281 - val_loss: 0.1833 - val_accuracy: 0.8429\n",
"Epoch 493/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0888 - accuracy: 0.9378\n",
"Epoch 00493: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 19s 311ms/step - loss: 0.0888 - accuracy: 0.9378 - val_loss: 0.2010 - val_accuracy: 0.8429\n",
"Epoch 494/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0961 - accuracy: 0.9224\n",
"Epoch 00494: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0961 - accuracy: 0.9224 - val_loss: 0.2061 - val_accuracy: 0.8367\n",
"Epoch 495/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0904 - accuracy: 0.9337\n",
"Epoch 00495: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0904 - accuracy: 0.9337 - val_loss: 0.1816 - val_accuracy: 0.8507\n",
"Epoch 496/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0889 - accuracy: 0.9337\n",
"Epoch 00496: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0889 - accuracy: 0.9337 - val_loss: 0.1733 - val_accuracy: 0.8554\n",
"Epoch 497/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9296\n",
"Epoch 00497: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0924 - accuracy: 0.9296 - val_loss: 0.1842 - val_accuracy: 0.8709\n",
"Epoch 498/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0891 - accuracy: 0.9311\n",
"Epoch 00498: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0891 - accuracy: 0.9311 - val_loss: 0.1784 - val_accuracy: 0.8600\n",
"Epoch 499/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9265\n",
"Epoch 00499: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0924 - accuracy: 0.9265 - val_loss: 0.1686 - val_accuracy: 0.8600\n",
"Epoch 500/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9219\n",
"Epoch 00500: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0988 - accuracy: 0.9219 - val_loss: 0.1643 - val_accuracy: 0.8709\n",
"Epoch 501/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0966 - accuracy: 0.9260\n",
"Epoch 00501: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0966 - accuracy: 0.9260 - val_loss: 0.1671 - val_accuracy: 0.8569\n",
"Epoch 502/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0938 - accuracy: 0.9291\n",
"Epoch 00502: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0938 - accuracy: 0.9291 - val_loss: 0.1770 - val_accuracy: 0.8600\n",
"Epoch 503/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0989 - accuracy: 0.9239\n",
"Epoch 00503: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0989 - accuracy: 0.9239 - val_loss: 0.1953 - val_accuracy: 0.8476\n",
"Epoch 504/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0929 - accuracy: 0.9301\n",
"Epoch 00504: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0929 - accuracy: 0.9301 - val_loss: 0.2179 - val_accuracy: 0.8274\n",
"Epoch 505/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0987 - accuracy: 0.9281\n",
"Epoch 00505: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 19s 304ms/step - loss: 0.0987 - accuracy: 0.9281 - val_loss: 0.1821 - val_accuracy: 0.8491\n",
"Epoch 506/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9265\n",
"Epoch 00506: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 20s 320ms/step - loss: 0.0922 - accuracy: 0.9265 - val_loss: 0.1756 - val_accuracy: 0.8491\n",
"Epoch 507/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0935 - accuracy: 0.9275\n",
"Epoch 00507: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0935 - accuracy: 0.9275 - val_loss: 0.2074 - val_accuracy: 0.8305\n",
"Epoch 508/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0949 - accuracy: 0.9347\n",
"Epoch 00508: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0949 - accuracy: 0.9347 - val_loss: 0.2121 - val_accuracy: 0.8305\n",
"Epoch 509/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0951 - accuracy: 0.9306\n",
"Epoch 00509: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0951 - accuracy: 0.9306 - val_loss: 0.2351 - val_accuracy: 0.8087\n",
"Epoch 510/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0868 - accuracy: 0.9368\n",
"Epoch 00510: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0868 - accuracy: 0.9368 - val_loss: 0.1786 - val_accuracy: 0.8414\n",
"Epoch 511/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0906 - accuracy: 0.9322\n",
"Epoch 00511: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0906 - accuracy: 0.9322 - val_loss: 0.1992 - val_accuracy: 0.8460\n",
"Epoch 512/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0853 - accuracy: 0.9363\n",
"Epoch 00512: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0853 - accuracy: 0.9363 - val_loss: 0.1836 - val_accuracy: 0.8507\n",
"Epoch 513/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0866 - accuracy: 0.9337\n",
"Epoch 00513: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0866 - accuracy: 0.9337 - val_loss: 0.1705 - val_accuracy: 0.8631\n",
"Epoch 514/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0901 - accuracy: 0.9327\n",
"Epoch 00514: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0901 - accuracy: 0.9327 - val_loss: 0.1625 - val_accuracy: 0.8725\n",
"Epoch 515/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0891 - accuracy: 0.9358\n",
"Epoch 00515: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0891 - accuracy: 0.9358 - val_loss: 0.1726 - val_accuracy: 0.8647\n",
"Epoch 516/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0886 - accuracy: 0.9291\n",
"Epoch 00516: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0886 - accuracy: 0.9291 - val_loss: 0.1556 - val_accuracy: 0.8756\n",
"Epoch 517/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9255\n",
"Epoch 00517: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0988 - accuracy: 0.9255 - val_loss: 0.2057 - val_accuracy: 0.8367\n",
"Epoch 518/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0926 - accuracy: 0.9265\n",
"Epoch 00518: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0926 - accuracy: 0.9265 - val_loss: 0.1856 - val_accuracy: 0.8476\n",
"Epoch 519/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0890 - accuracy: 0.9306\n",
"Epoch 00519: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0890 - accuracy: 0.9306 - val_loss: 0.2098 - val_accuracy: 0.8320\n",
"Epoch 520/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0890 - accuracy: 0.9306\n",
"Epoch 00520: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 19s 310ms/step - loss: 0.0890 - accuracy: 0.9306 - val_loss: 0.1542 - val_accuracy: 0.8802\n",
"Epoch 521/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0932 - accuracy: 0.9286\n",
"Epoch 00521: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 20s 323ms/step - loss: 0.0932 - accuracy: 0.9286 - val_loss: 0.1904 - val_accuracy: 0.8445\n",
"Epoch 522/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9317\n",
"Epoch 00522: accuracy did not improve from 0.93936\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0895 - accuracy: 0.9317 - val_loss: 0.1921 - val_accuracy: 0.8445\n",
"Epoch 523/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0814 - accuracy: 0.9445\n",
"Epoch 00523: accuracy improved from 0.93936 to 0.94450, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-523-0.94.hdf5\n",
"61/60 [==============================] - 22s 364ms/step - loss: 0.0814 - accuracy: 0.9445 - val_loss: 0.1885 - val_accuracy: 0.8523\n",
"Epoch 524/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0896 - accuracy: 0.9265\n",
"Epoch 00524: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0896 - accuracy: 0.9265 - val_loss: 0.1917 - val_accuracy: 0.8414\n",
"Epoch 525/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0885 - accuracy: 0.9327\n",
"Epoch 00525: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0885 - accuracy: 0.9327 - val_loss: 0.1811 - val_accuracy: 0.8631\n",
"Epoch 526/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0942 - accuracy: 0.9255\n",
"Epoch 00526: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0942 - accuracy: 0.9255 - val_loss: 0.1704 - val_accuracy: 0.8600\n",
"Epoch 527/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0832 - accuracy: 0.9353\n",
"Epoch 00527: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0832 - accuracy: 0.9353 - val_loss: 0.1687 - val_accuracy: 0.8694\n",
"Epoch 528/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0916 - accuracy: 0.9317\n",
"Epoch 00528: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0916 - accuracy: 0.9317 - val_loss: 0.1537 - val_accuracy: 0.8725\n",
"Epoch 529/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.9399\n",
"Epoch 00529: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0894 - accuracy: 0.9399 - val_loss: 0.1569 - val_accuracy: 0.8647\n",
"Epoch 530/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0905 - accuracy: 0.9245\n",
"Epoch 00530: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0905 - accuracy: 0.9245 - val_loss: 0.2120 - val_accuracy: 0.8274\n",
"Epoch 531/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0956 - accuracy: 0.9311\n",
"Epoch 00531: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0956 - accuracy: 0.9311 - val_loss: 0.1941 - val_accuracy: 0.8476\n",
"Epoch 532/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0854 - accuracy: 0.9332\n",
"Epoch 00532: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0854 - accuracy: 0.9332 - val_loss: 0.1777 - val_accuracy: 0.8491\n",
"Epoch 533/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0854 - accuracy: 0.9327\n",
"Epoch 00533: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0854 - accuracy: 0.9327 - val_loss: 0.2393 - val_accuracy: 0.8025\n",
"Epoch 534/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0938 - accuracy: 0.9296\n",
"Epoch 00534: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0938 - accuracy: 0.9296 - val_loss: 0.1821 - val_accuracy: 0.8476\n",
"Epoch 535/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0871 - accuracy: 0.9286\n",
"Epoch 00535: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0871 - accuracy: 0.9286 - val_loss: 0.1660 - val_accuracy: 0.8756\n",
"Epoch 536/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0850 - accuracy: 0.9378\n",
"Epoch 00536: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0850 - accuracy: 0.9378 - val_loss: 0.1677 - val_accuracy: 0.8600\n",
"Epoch 537/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0890 - accuracy: 0.9347\n",
"Epoch 00537: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 19s 319ms/step - loss: 0.0890 - accuracy: 0.9347 - val_loss: 0.1755 - val_accuracy: 0.8460\n",
"Epoch 538/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0866 - accuracy: 0.9317\n",
"Epoch 00538: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 20s 327ms/step - loss: 0.0866 - accuracy: 0.9317 - val_loss: 0.1838 - val_accuracy: 0.8538\n",
"Epoch 539/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0885 - accuracy: 0.9281\n",
"Epoch 00539: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0885 - accuracy: 0.9281 - val_loss: 0.1814 - val_accuracy: 0.8491\n",
"Epoch 540/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0885 - accuracy: 0.9275\n",
"Epoch 00540: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0885 - accuracy: 0.9275 - val_loss: 0.1950 - val_accuracy: 0.8429\n",
"Epoch 541/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0826 - accuracy: 0.9445\n",
"Epoch 00541: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0826 - accuracy: 0.9445 - val_loss: 0.1610 - val_accuracy: 0.8678\n",
"Epoch 542/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0903 - accuracy: 0.9311\n",
"Epoch 00542: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0903 - accuracy: 0.9311 - val_loss: 0.1878 - val_accuracy: 0.8460\n",
"Epoch 543/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0860 - accuracy: 0.9347\n",
"Epoch 00543: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0860 - accuracy: 0.9347 - val_loss: 0.2038 - val_accuracy: 0.8243\n",
"Epoch 544/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0848 - accuracy: 0.9327\n",
"Epoch 00544: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0848 - accuracy: 0.9327 - val_loss: 0.2055 - val_accuracy: 0.8429\n",
"Epoch 545/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0843 - accuracy: 0.9394\n",
"Epoch 00545: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0843 - accuracy: 0.9394 - val_loss: 0.1452 - val_accuracy: 0.8818\n",
"Epoch 546/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0871 - accuracy: 0.9296\n",
"Epoch 00546: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0871 - accuracy: 0.9296 - val_loss: 0.1604 - val_accuracy: 0.8631\n",
"Epoch 547/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9414\n",
"Epoch 00547: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0777 - accuracy: 0.9414 - val_loss: 0.1792 - val_accuracy: 0.8585\n",
"Epoch 548/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0825 - accuracy: 0.9378\n",
"Epoch 00548: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0825 - accuracy: 0.9378 - val_loss: 0.1567 - val_accuracy: 0.8678\n",
"Epoch 549/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0850 - accuracy: 0.9332\n",
"Epoch 00549: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0850 - accuracy: 0.9332 - val_loss: 0.1343 - val_accuracy: 0.8989\n",
"Epoch 550/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0901 - accuracy: 0.9306\n",
"Epoch 00550: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0901 - accuracy: 0.9306 - val_loss: 0.1873 - val_accuracy: 0.8429\n",
"Epoch 551/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.9414\n",
"Epoch 00551: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0813 - accuracy: 0.9414 - val_loss: 0.2256 - val_accuracy: 0.8134\n",
"Epoch 552/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0842 - accuracy: 0.9337\n",
"Epoch 00552: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0842 - accuracy: 0.9337 - val_loss: 0.1742 - val_accuracy: 0.8523\n",
"Epoch 553/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0867 - accuracy: 0.9383\n",
"Epoch 00553: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0867 - accuracy: 0.9383 - val_loss: 0.1556 - val_accuracy: 0.8725\n",
"Epoch 554/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0898 - accuracy: 0.9301\n",
"Epoch 00554: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0898 - accuracy: 0.9301 - val_loss: 0.1659 - val_accuracy: 0.8616\n",
"Epoch 555/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0801 - accuracy: 0.9388\n",
"Epoch 00555: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 20s 324ms/step - loss: 0.0801 - accuracy: 0.9388 - val_loss: 0.1754 - val_accuracy: 0.8585\n",
"Epoch 556/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0869 - accuracy: 0.9342\n",
"Epoch 00556: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 21s 336ms/step - loss: 0.0869 - accuracy: 0.9342 - val_loss: 0.1877 - val_accuracy: 0.8538\n",
"Epoch 557/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.9353\n",
"Epoch 00557: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0813 - accuracy: 0.9353 - val_loss: 0.1746 - val_accuracy: 0.8647\n",
"Epoch 558/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0907 - accuracy: 0.9322\n",
"Epoch 00558: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0907 - accuracy: 0.9322 - val_loss: 0.1787 - val_accuracy: 0.8600\n",
"Epoch 559/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.9440\n",
"Epoch 00559: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0813 - accuracy: 0.9440 - val_loss: 0.1765 - val_accuracy: 0.8616\n",
"Epoch 560/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0852 - accuracy: 0.9353\n",
"Epoch 00560: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0852 - accuracy: 0.9353 - val_loss: 0.1753 - val_accuracy: 0.8663\n",
"Epoch 561/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0864 - accuracy: 0.9342\n",
"Epoch 00561: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0864 - accuracy: 0.9342 - val_loss: 0.1567 - val_accuracy: 0.8740\n",
"Epoch 562/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0847 - accuracy: 0.9317\n",
"Epoch 00562: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0847 - accuracy: 0.9317 - val_loss: 0.1758 - val_accuracy: 0.8507\n",
"Epoch 563/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9301\n",
"Epoch 00563: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0945 - accuracy: 0.9301 - val_loss: 0.1422 - val_accuracy: 0.8974\n",
"Epoch 564/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0868 - accuracy: 0.9368\n",
"Epoch 00564: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0868 - accuracy: 0.9368 - val_loss: 0.1611 - val_accuracy: 0.8694\n",
"Epoch 565/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0932 - accuracy: 0.9275\n",
"Epoch 00565: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0932 - accuracy: 0.9275 - val_loss: 0.1815 - val_accuracy: 0.8476\n",
"Epoch 566/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0890 - accuracy: 0.9306\n",
"Epoch 00566: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0890 - accuracy: 0.9306 - val_loss: 0.1861 - val_accuracy: 0.8507\n",
"Epoch 567/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0866 - accuracy: 0.9322\n",
"Epoch 00567: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0866 - accuracy: 0.9322 - val_loss: 0.2012 - val_accuracy: 0.8429\n",
"Epoch 568/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0927 - accuracy: 0.9311\n",
"Epoch 00568: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0927 - accuracy: 0.9311 - val_loss: 0.1929 - val_accuracy: 0.8429\n",
"Epoch 569/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0848 - accuracy: 0.9378\n",
"Epoch 00569: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0848 - accuracy: 0.9378 - val_loss: 0.1900 - val_accuracy: 0.8523\n",
"Epoch 570/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0883 - accuracy: 0.9270\n",
"Epoch 00570: accuracy did not improve from 0.94450\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0883 - accuracy: 0.9270 - val_loss: 0.1927 - val_accuracy: 0.8460\n",
"Epoch 571/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0769 - accuracy: 0.9476\n",
"Epoch 00571: accuracy improved from 0.94450 to 0.94758, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-571-0.95.hdf5\n",
"61/60 [==============================] - 23s 383ms/step - loss: 0.0769 - accuracy: 0.9476 - val_loss: 0.1737 - val_accuracy: 0.8756\n",
"Epoch 572/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0837 - accuracy: 0.9353\n",
"Epoch 00572: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 19s 304ms/step - loss: 0.0837 - accuracy: 0.9353 - val_loss: 0.1814 - val_accuracy: 0.8523\n",
"Epoch 573/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9440\n",
"Epoch 00573: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 19s 309ms/step - loss: 0.0777 - accuracy: 0.9440 - val_loss: 0.1609 - val_accuracy: 0.8834\n",
"Epoch 574/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0930 - accuracy: 0.9260\n",
"Epoch 00574: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0930 - accuracy: 0.9260 - val_loss: 0.1602 - val_accuracy: 0.8756\n",
"Epoch 575/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0802 - accuracy: 0.9409\n",
"Epoch 00575: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0802 - accuracy: 0.9409 - val_loss: 0.2070 - val_accuracy: 0.8445\n",
"Epoch 576/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9347\n",
"Epoch 00576: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0895 - accuracy: 0.9347 - val_loss: 0.1990 - val_accuracy: 0.8507\n",
"Epoch 577/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0853 - accuracy: 0.9332\n",
"Epoch 00577: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0853 - accuracy: 0.9332 - val_loss: 0.1683 - val_accuracy: 0.8694\n",
"Epoch 578/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0815 - accuracy: 0.9378\n",
"Epoch 00578: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0815 - accuracy: 0.9378 - val_loss: 0.1372 - val_accuracy: 0.8989\n",
"Epoch 579/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0822 - accuracy: 0.9409\n",
"Epoch 00579: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0822 - accuracy: 0.9409 - val_loss: 0.1297 - val_accuracy: 0.9067\n",
"Epoch 580/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0798 - accuracy: 0.9327\n",
"Epoch 00580: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0798 - accuracy: 0.9327 - val_loss: 0.1271 - val_accuracy: 0.9005\n",
"Epoch 581/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0798 - accuracy: 0.9337\n",
"Epoch 00581: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0798 - accuracy: 0.9337 - val_loss: 0.1475 - val_accuracy: 0.8865\n",
"Epoch 582/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0881 - accuracy: 0.9342\n",
"Epoch 00582: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0881 - accuracy: 0.9342 - val_loss: 0.1577 - val_accuracy: 0.8771\n",
"Epoch 583/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0826 - accuracy: 0.9332\n",
"Epoch 00583: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0826 - accuracy: 0.9332 - val_loss: 0.1712 - val_accuracy: 0.8694\n",
"Epoch 584/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0839 - accuracy: 0.9363\n",
"Epoch 00584: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0839 - accuracy: 0.9363 - val_loss: 0.2117 - val_accuracy: 0.8398\n",
"Epoch 585/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0811 - accuracy: 0.9353\n",
"Epoch 00585: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0811 - accuracy: 0.9353 - val_loss: 0.1652 - val_accuracy: 0.8663\n",
"Epoch 586/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.9311\n",
"Epoch 00586: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 19s 315ms/step - loss: 0.0894 - accuracy: 0.9311 - val_loss: 0.1942 - val_accuracy: 0.8398\n",
"Epoch 587/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0852 - accuracy: 0.9332\n",
"Epoch 00587: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 302ms/step - loss: 0.0852 - accuracy: 0.9332 - val_loss: 0.1391 - val_accuracy: 0.8942\n",
"Epoch 588/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0793 - accuracy: 0.9388\n",
"Epoch 00588: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0793 - accuracy: 0.9388 - val_loss: 0.1562 - val_accuracy: 0.8694\n",
"Epoch 589/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0826 - accuracy: 0.9378\n",
"Epoch 00589: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0826 - accuracy: 0.9378 - val_loss: 0.1384 - val_accuracy: 0.8865\n",
"Epoch 590/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.9404\n",
"Epoch 00590: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0833 - accuracy: 0.9404 - val_loss: 0.1358 - val_accuracy: 0.8942\n",
"Epoch 591/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0800 - accuracy: 0.9353\n",
"Epoch 00591: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0800 - accuracy: 0.9353 - val_loss: 0.1587 - val_accuracy: 0.8802\n",
"Epoch 592/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 0.9430\n",
"Epoch 00592: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0779 - accuracy: 0.9430 - val_loss: 0.1341 - val_accuracy: 0.9067\n",
"Epoch 593/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.9347\n",
"Epoch 00593: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0813 - accuracy: 0.9347 - val_loss: 0.1766 - val_accuracy: 0.8694\n",
"Epoch 594/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0885 - accuracy: 0.9337\n",
"Epoch 00594: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0885 - accuracy: 0.9337 - val_loss: 0.2037 - val_accuracy: 0.8398\n",
"Epoch 595/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0787 - accuracy: 0.9399\n",
"Epoch 00595: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0787 - accuracy: 0.9399 - val_loss: 0.1681 - val_accuracy: 0.8631\n",
"Epoch 596/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 0.9424\n",
"Epoch 00596: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0779 - accuracy: 0.9424 - val_loss: 0.2297 - val_accuracy: 0.8258\n",
"Epoch 597/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0830 - accuracy: 0.9358\n",
"Epoch 00597: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0830 - accuracy: 0.9358 - val_loss: 0.2331 - val_accuracy: 0.8180\n",
"Epoch 598/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0817 - accuracy: 0.9358\n",
"Epoch 00598: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0817 - accuracy: 0.9358 - val_loss: 0.1497 - val_accuracy: 0.8802\n",
"Epoch 599/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0776 - accuracy: 0.9445\n",
"Epoch 00599: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0776 - accuracy: 0.9445 - val_loss: 0.1519 - val_accuracy: 0.8865\n",
"Epoch 600/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0887 - accuracy: 0.9281\n",
"Epoch 00600: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0887 - accuracy: 0.9281 - val_loss: 0.1545 - val_accuracy: 0.8802\n",
"Epoch 601/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0767 - accuracy: 0.9476\n",
"Epoch 00601: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0767 - accuracy: 0.9476 - val_loss: 0.1912 - val_accuracy: 0.8491\n",
"Epoch 602/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0879 - accuracy: 0.9322\n",
"Epoch 00602: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0879 - accuracy: 0.9322 - val_loss: 0.2011 - val_accuracy: 0.8523\n",
"Epoch 603/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0829 - accuracy: 0.9332\n",
"Epoch 00603: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0829 - accuracy: 0.9332 - val_loss: 0.2023 - val_accuracy: 0.8554\n",
"Epoch 604/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0851 - accuracy: 0.9337\n",
"Epoch 00604: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 300ms/step - loss: 0.0851 - accuracy: 0.9337 - val_loss: 0.1664 - val_accuracy: 0.8802\n",
"Epoch 605/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0839 - accuracy: 0.9419\n",
"Epoch 00605: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 21s 336ms/step - loss: 0.0839 - accuracy: 0.9419 - val_loss: 0.1632 - val_accuracy: 0.8787\n",
"Epoch 606/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9419\n",
"Epoch 00606: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 302ms/step - loss: 0.0762 - accuracy: 0.9419 - val_loss: 0.1775 - val_accuracy: 0.8585\n",
"Epoch 607/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0877 - accuracy: 0.9311\n",
"Epoch 00607: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0877 - accuracy: 0.9311 - val_loss: 0.1748 - val_accuracy: 0.8694\n",
"Epoch 608/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0830 - accuracy: 0.9399\n",
"Epoch 00608: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0830 - accuracy: 0.9399 - val_loss: 0.1851 - val_accuracy: 0.8771\n",
"Epoch 609/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0852 - accuracy: 0.9281\n",
"Epoch 00609: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0852 - accuracy: 0.9281 - val_loss: 0.2107 - val_accuracy: 0.8460\n",
"Epoch 610/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0799 - accuracy: 0.9368\n",
"Epoch 00610: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0799 - accuracy: 0.9368 - val_loss: 0.1459 - val_accuracy: 0.8896\n",
"Epoch 611/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9291\n",
"Epoch 00611: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0875 - accuracy: 0.9291 - val_loss: 0.1612 - val_accuracy: 0.8709\n",
"Epoch 612/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0823 - accuracy: 0.9332\n",
"Epoch 00612: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0823 - accuracy: 0.9332 - val_loss: 0.1712 - val_accuracy: 0.8694\n",
"Epoch 613/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0705 - accuracy: 0.9476\n",
"Epoch 00613: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0705 - accuracy: 0.9476 - val_loss: 0.1738 - val_accuracy: 0.8678\n",
"Epoch 614/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0714 - accuracy: 0.9409\n",
"Epoch 00614: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0714 - accuracy: 0.9409 - val_loss: 0.1553 - val_accuracy: 0.8834\n",
"Epoch 615/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0848 - accuracy: 0.9322\n",
"Epoch 00615: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0848 - accuracy: 0.9322 - val_loss: 0.1674 - val_accuracy: 0.8725\n",
"Epoch 616/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0791 - accuracy: 0.9358\n",
"Epoch 00616: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0791 - accuracy: 0.9358 - val_loss: 0.1522 - val_accuracy: 0.8802\n",
"Epoch 617/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0778 - accuracy: 0.9460\n",
"Epoch 00617: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0778 - accuracy: 0.9460 - val_loss: 0.1671 - val_accuracy: 0.8709\n",
"Epoch 618/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0807 - accuracy: 0.9424\n",
"Epoch 00618: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0807 - accuracy: 0.9424 - val_loss: 0.1497 - val_accuracy: 0.8865\n",
"Epoch 619/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0770 - accuracy: 0.9419\n",
"Epoch 00619: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0770 - accuracy: 0.9419 - val_loss: 0.1571 - val_accuracy: 0.8756\n",
"Epoch 620/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0754 - accuracy: 0.9424\n",
"Epoch 00620: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0754 - accuracy: 0.9424 - val_loss: 0.1576 - val_accuracy: 0.8678\n",
"Epoch 621/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9404\n",
"Epoch 00621: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0752 - accuracy: 0.9404 - val_loss: 0.1691 - val_accuracy: 0.8554\n",
"Epoch 622/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0730 - accuracy: 0.9399\n",
"Epoch 00622: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 21s 342ms/step - loss: 0.0730 - accuracy: 0.9399 - val_loss: 0.1491 - val_accuracy: 0.8756\n",
"Epoch 623/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0764 - accuracy: 0.9388\n",
"Epoch 00623: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 19s 314ms/step - loss: 0.0764 - accuracy: 0.9388 - val_loss: 0.1484 - val_accuracy: 0.8725\n",
"Epoch 624/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0736 - accuracy: 0.9445\n",
"Epoch 00624: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0736 - accuracy: 0.9445 - val_loss: 0.1411 - val_accuracy: 0.8818\n",
"Epoch 625/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9342\n",
"Epoch 00625: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0794 - accuracy: 0.9342 - val_loss: 0.1435 - val_accuracy: 0.8911\n",
"Epoch 626/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0737 - accuracy: 0.9388\n",
"Epoch 00626: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0737 - accuracy: 0.9388 - val_loss: 0.1548 - val_accuracy: 0.8725\n",
"Epoch 627/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9455\n",
"Epoch 00627: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0739 - accuracy: 0.9455 - val_loss: 0.1560 - val_accuracy: 0.8647\n",
"Epoch 628/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0790 - accuracy: 0.9419\n",
"Epoch 00628: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0790 - accuracy: 0.9419 - val_loss: 0.1479 - val_accuracy: 0.8709\n",
"Epoch 629/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.9394\n",
"Epoch 00629: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0833 - accuracy: 0.9394 - val_loss: 0.1613 - val_accuracy: 0.8694\n",
"Epoch 630/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0755 - accuracy: 0.9440\n",
"Epoch 00630: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0755 - accuracy: 0.9440 - val_loss: 0.1493 - val_accuracy: 0.8834\n",
"Epoch 631/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9414\n",
"Epoch 00631: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0761 - accuracy: 0.9414 - val_loss: 0.1512 - val_accuracy: 0.8802\n",
"Epoch 632/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0758 - accuracy: 0.9471\n",
"Epoch 00632: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0758 - accuracy: 0.9471 - val_loss: 0.1646 - val_accuracy: 0.8694\n",
"Epoch 633/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0792 - accuracy: 0.9358\n",
"Epoch 00633: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0792 - accuracy: 0.9358 - val_loss: 0.1650 - val_accuracy: 0.8818\n",
"Epoch 634/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0775 - accuracy: 0.9378\n",
"Epoch 00634: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0775 - accuracy: 0.9378 - val_loss: 0.1340 - val_accuracy: 0.8958\n",
"Epoch 635/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0866 - accuracy: 0.9322\n",
"Epoch 00635: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0866 - accuracy: 0.9322 - val_loss: 0.1842 - val_accuracy: 0.8616\n",
"Epoch 636/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0853 - accuracy: 0.9337\n",
"Epoch 00636: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0853 - accuracy: 0.9337 - val_loss: 0.1532 - val_accuracy: 0.8725\n",
"Epoch 637/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0760 - accuracy: 0.9419\n",
"Epoch 00637: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0760 - accuracy: 0.9419 - val_loss: 0.1524 - val_accuracy: 0.8911\n",
"Epoch 638/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0784 - accuracy: 0.9347\n",
"Epoch 00638: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0784 - accuracy: 0.9347 - val_loss: 0.1491 - val_accuracy: 0.8942\n",
"Epoch 639/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0818 - accuracy: 0.9399\n",
"Epoch 00639: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 20s 332ms/step - loss: 0.0818 - accuracy: 0.9399 - val_loss: 0.1574 - val_accuracy: 0.8740\n",
"Epoch 640/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0771 - accuracy: 0.9358\n",
"Epoch 00640: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 21s 337ms/step - loss: 0.0771 - accuracy: 0.9358 - val_loss: 0.1603 - val_accuracy: 0.8663\n",
"Epoch 641/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0862 - accuracy: 0.9347\n",
"Epoch 00641: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0862 - accuracy: 0.9347 - val_loss: 0.1893 - val_accuracy: 0.8538\n",
"Epoch 642/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0798 - accuracy: 0.9440\n",
"Epoch 00642: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0798 - accuracy: 0.9440 - val_loss: 0.1978 - val_accuracy: 0.8523\n",
"Epoch 643/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9404\n",
"Epoch 00643: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0761 - accuracy: 0.9404 - val_loss: 0.2002 - val_accuracy: 0.8445\n",
"Epoch 644/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0740 - accuracy: 0.9394\n",
"Epoch 00644: accuracy did not improve from 0.94758\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0740 - accuracy: 0.9394 - val_loss: 0.1638 - val_accuracy: 0.8787\n",
"Epoch 645/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0715 - accuracy: 0.9486\n",
"Epoch 00645: accuracy improved from 0.94758 to 0.94861, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-645-0.95.hdf5\n",
"61/60 [==============================] - 23s 374ms/step - loss: 0.0715 - accuracy: 0.9486 - val_loss: 0.1611 - val_accuracy: 0.8663\n",
"Epoch 646/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0783 - accuracy: 0.9440\n",
"Epoch 00646: accuracy did not improve from 0.94861\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0783 - accuracy: 0.9440 - val_loss: 0.1345 - val_accuracy: 0.8911\n",
"Epoch 647/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0727 - accuracy: 0.9496\n",
"Epoch 00647: accuracy improved from 0.94861 to 0.94964, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-647-0.95.hdf5\n",
"61/60 [==============================] - 23s 384ms/step - loss: 0.0727 - accuracy: 0.9496 - val_loss: 0.1710 - val_accuracy: 0.8663\n",
"Epoch 648/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0711 - accuracy: 0.9450\n",
"Epoch 00648: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0711 - accuracy: 0.9450 - val_loss: 0.2124 - val_accuracy: 0.8351\n",
"Epoch 649/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0758 - accuracy: 0.9399\n",
"Epoch 00649: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0758 - accuracy: 0.9399 - val_loss: 0.2239 - val_accuracy: 0.8320\n",
"Epoch 650/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0743 - accuracy: 0.9404\n",
"Epoch 00650: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0743 - accuracy: 0.9404 - val_loss: 0.1813 - val_accuracy: 0.8678\n",
"Epoch 651/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0846 - accuracy: 0.9317\n",
"Epoch 00651: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0846 - accuracy: 0.9317 - val_loss: 0.1361 - val_accuracy: 0.8958\n",
"Epoch 652/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0786 - accuracy: 0.9399\n",
"Epoch 00652: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0786 - accuracy: 0.9399 - val_loss: 0.1393 - val_accuracy: 0.8896\n",
"Epoch 653/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0714 - accuracy: 0.9445\n",
"Epoch 00653: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0714 - accuracy: 0.9445 - val_loss: 0.1221 - val_accuracy: 0.9005\n",
"Epoch 654/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9476\n",
"Epoch 00654: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0739 - accuracy: 0.9476 - val_loss: 0.1384 - val_accuracy: 0.9005\n",
"Epoch 655/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9445\n",
"Epoch 00655: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 303ms/step - loss: 0.0752 - accuracy: 0.9445 - val_loss: 0.1514 - val_accuracy: 0.8896\n",
"Epoch 656/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0708 - accuracy: 0.9435\n",
"Epoch 00656: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 20s 334ms/step - loss: 0.0708 - accuracy: 0.9435 - val_loss: 0.1452 - val_accuracy: 0.8896\n",
"Epoch 657/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0808 - accuracy: 0.9368\n",
"Epoch 00657: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 299ms/step - loss: 0.0808 - accuracy: 0.9368 - val_loss: 0.1579 - val_accuracy: 0.8771\n",
"Epoch 658/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0765 - accuracy: 0.9383\n",
"Epoch 00658: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0765 - accuracy: 0.9383 - val_loss: 0.2071 - val_accuracy: 0.8460\n",
"Epoch 659/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0754 - accuracy: 0.9414\n",
"Epoch 00659: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0754 - accuracy: 0.9414 - val_loss: 0.1847 - val_accuracy: 0.8585\n",
"Epoch 660/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0769 - accuracy: 0.9435\n",
"Epoch 00660: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0769 - accuracy: 0.9435 - val_loss: 0.1699 - val_accuracy: 0.8709\n",
"Epoch 661/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0799 - accuracy: 0.9399\n",
"Epoch 00661: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0799 - accuracy: 0.9399 - val_loss: 0.1514 - val_accuracy: 0.8802\n",
"Epoch 662/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0712 - accuracy: 0.9455\n",
"Epoch 00662: accuracy did not improve from 0.94964\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0712 - accuracy: 0.9455 - val_loss: 0.1294 - val_accuracy: 0.8865\n",
"Epoch 663/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0695 - accuracy: 0.9522\n",
"Epoch 00663: accuracy improved from 0.94964 to 0.95221, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-663-0.95.hdf5\n",
"61/60 [==============================] - 23s 373ms/step - loss: 0.0695 - accuracy: 0.9522 - val_loss: 0.1521 - val_accuracy: 0.8896\n",
"Epoch 664/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9486\n",
"Epoch 00664: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0653 - accuracy: 0.9486 - val_loss: 0.1608 - val_accuracy: 0.8802\n",
"Epoch 665/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0753 - accuracy: 0.9399\n",
"Epoch 00665: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0753 - accuracy: 0.9399 - val_loss: 0.1538 - val_accuracy: 0.8756\n",
"Epoch 666/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0766 - accuracy: 0.9404\n",
"Epoch 00666: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0766 - accuracy: 0.9404 - val_loss: 0.2047 - val_accuracy: 0.8445\n",
"Epoch 667/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0797 - accuracy: 0.9409\n",
"Epoch 00667: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0797 - accuracy: 0.9409 - val_loss: 0.2043 - val_accuracy: 0.8398\n",
"Epoch 668/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0775 - accuracy: 0.9358\n",
"Epoch 00668: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0775 - accuracy: 0.9358 - val_loss: 0.2412 - val_accuracy: 0.8258\n",
"Epoch 669/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9404\n",
"Epoch 00669: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0752 - accuracy: 0.9404 - val_loss: 0.1735 - val_accuracy: 0.8616\n",
"Epoch 670/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0704 - accuracy: 0.9440\n",
"Epoch 00670: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0704 - accuracy: 0.9440 - val_loss: 0.1461 - val_accuracy: 0.8927\n",
"Epoch 671/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0714 - accuracy: 0.9445\n",
"Epoch 00671: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 20s 328ms/step - loss: 0.0714 - accuracy: 0.9445 - val_loss: 0.1443 - val_accuracy: 0.8942\n",
"Epoch 672/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0712 - accuracy: 0.9435\n",
"Epoch 00672: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 20s 330ms/step - loss: 0.0712 - accuracy: 0.9435 - val_loss: 0.1463 - val_accuracy: 0.8756\n",
"Epoch 673/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0800 - accuracy: 0.9409\n",
"Epoch 00673: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0800 - accuracy: 0.9409 - val_loss: 0.1832 - val_accuracy: 0.8507\n",
"Epoch 674/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0670 - accuracy: 0.9512\n",
"Epoch 00674: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0670 - accuracy: 0.9512 - val_loss: 0.1803 - val_accuracy: 0.8538\n",
"Epoch 675/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9409\n",
"Epoch 00675: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0752 - accuracy: 0.9409 - val_loss: 0.1685 - val_accuracy: 0.8569\n",
"Epoch 676/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0831 - accuracy: 0.9322\n",
"Epoch 00676: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0831 - accuracy: 0.9322 - val_loss: 0.2356 - val_accuracy: 0.8165\n",
"Epoch 677/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0665 - accuracy: 0.9496\n",
"Epoch 00677: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0665 - accuracy: 0.9496 - val_loss: 0.2232 - val_accuracy: 0.8243\n",
"Epoch 678/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0830 - accuracy: 0.9358\n",
"Epoch 00678: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0830 - accuracy: 0.9358 - val_loss: 0.1863 - val_accuracy: 0.8554\n",
"Epoch 679/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0742 - accuracy: 0.9486\n",
"Epoch 00679: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0742 - accuracy: 0.9486 - val_loss: 0.1765 - val_accuracy: 0.8554\n",
"Epoch 680/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0755 - accuracy: 0.9466\n",
"Epoch 00680: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0755 - accuracy: 0.9466 - val_loss: 0.1656 - val_accuracy: 0.8600\n",
"Epoch 681/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0674 - accuracy: 0.9450\n",
"Epoch 00681: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0674 - accuracy: 0.9450 - val_loss: 0.1776 - val_accuracy: 0.8585\n",
"Epoch 682/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0648 - accuracy: 0.9491\n",
"Epoch 00682: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0648 - accuracy: 0.9491 - val_loss: 0.1478 - val_accuracy: 0.8834\n",
"Epoch 683/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0751 - accuracy: 0.9409\n",
"Epoch 00683: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0751 - accuracy: 0.9409 - val_loss: 0.1858 - val_accuracy: 0.8429\n",
"Epoch 684/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0722 - accuracy: 0.9450\n",
"Epoch 00684: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0722 - accuracy: 0.9450 - val_loss: 0.1707 - val_accuracy: 0.8647\n",
"Epoch 685/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0715 - accuracy: 0.9424\n",
"Epoch 00685: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0715 - accuracy: 0.9424 - val_loss: 0.1765 - val_accuracy: 0.8507\n",
"Epoch 686/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0760 - accuracy: 0.9414\n",
"Epoch 00686: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0760 - accuracy: 0.9414 - val_loss: 0.1765 - val_accuracy: 0.8585\n",
"Epoch 687/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9373\n",
"Epoch 00687: accuracy did not improve from 0.95221\n",
"61/60 [==============================] - 19s 318ms/step - loss: 0.0739 - accuracy: 0.9373 - val_loss: 0.1764 - val_accuracy: 0.8585\n",
"Epoch 688/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0735 - accuracy: 0.9532\n",
"Epoch 00688: accuracy improved from 0.95221 to 0.95324, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-688-0.95.hdf5\n",
"61/60 [==============================] - 27s 441ms/step - loss: 0.0735 - accuracy: 0.9532 - val_loss: 0.2176 - val_accuracy: 0.8289\n",
"Epoch 689/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0686 - accuracy: 0.9522\n",
"Epoch 00689: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0686 - accuracy: 0.9522 - val_loss: 0.1667 - val_accuracy: 0.8756\n",
"Epoch 690/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0726 - accuracy: 0.9404\n",
"Epoch 00690: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0726 - accuracy: 0.9404 - val_loss: 0.1682 - val_accuracy: 0.8771\n",
"Epoch 691/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0704 - accuracy: 0.9435\n",
"Epoch 00691: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0704 - accuracy: 0.9435 - val_loss: 0.1943 - val_accuracy: 0.8538\n",
"Epoch 692/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0735 - accuracy: 0.9440\n",
"Epoch 00692: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0735 - accuracy: 0.9440 - val_loss: 0.1969 - val_accuracy: 0.8523\n",
"Epoch 693/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0821 - accuracy: 0.9311\n",
"Epoch 00693: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0821 - accuracy: 0.9311 - val_loss: 0.1766 - val_accuracy: 0.8694\n",
"Epoch 694/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0753 - accuracy: 0.9363\n",
"Epoch 00694: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0753 - accuracy: 0.9363 - val_loss: 0.1840 - val_accuracy: 0.8491\n",
"Epoch 695/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0749 - accuracy: 0.9419\n",
"Epoch 00695: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0749 - accuracy: 0.9419 - val_loss: 0.1584 - val_accuracy: 0.8818\n",
"Epoch 696/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0769 - accuracy: 0.9414\n",
"Epoch 00696: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0769 - accuracy: 0.9414 - val_loss: 0.2117 - val_accuracy: 0.8351\n",
"Epoch 697/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0695 - accuracy: 0.9491\n",
"Epoch 00697: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0695 - accuracy: 0.9491 - val_loss: 0.1551 - val_accuracy: 0.8865\n",
"Epoch 698/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0658 - accuracy: 0.9527\n",
"Epoch 00698: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0658 - accuracy: 0.9527 - val_loss: 0.1822 - val_accuracy: 0.8647\n",
"Epoch 699/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0717 - accuracy: 0.9491\n",
"Epoch 00699: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0717 - accuracy: 0.9491 - val_loss: 0.1617 - val_accuracy: 0.8818\n",
"Epoch 700/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0747 - accuracy: 0.9419\n",
"Epoch 00700: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0747 - accuracy: 0.9419 - val_loss: 0.2141 - val_accuracy: 0.8351\n",
"Epoch 701/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0757 - accuracy: 0.9460\n",
"Epoch 00701: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0757 - accuracy: 0.9460 - val_loss: 0.2473 - val_accuracy: 0.8149\n",
"Epoch 702/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0737 - accuracy: 0.9450\n",
"Epoch 00702: accuracy did not improve from 0.95324\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0737 - accuracy: 0.9450 - val_loss: 0.2790 - val_accuracy: 0.7978\n",
"Epoch 703/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0660 - accuracy: 0.9543\n",
"Epoch 00703: accuracy improved from 0.95324 to 0.95427, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-703-0.95.hdf5\n",
"61/60 [==============================] - 23s 371ms/step - loss: 0.0660 - accuracy: 0.9543 - val_loss: 0.1859 - val_accuracy: 0.8600\n",
"Epoch 704/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0811 - accuracy: 0.9368\n",
"Epoch 00704: accuracy did not improve from 0.95427\n",
"61/60 [==============================] - 18s 300ms/step - loss: 0.0811 - accuracy: 0.9368 - val_loss: 0.2499 - val_accuracy: 0.8072\n",
"Epoch 705/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0589 - accuracy: 0.9558\n",
"Epoch 00705: accuracy improved from 0.95427 to 0.95581, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-705-0.96.hdf5\n",
"61/60 [==============================] - 23s 369ms/step - loss: 0.0589 - accuracy: 0.9558 - val_loss: 0.1856 - val_accuracy: 0.8491\n",
"Epoch 706/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0763 - accuracy: 0.9430\n",
"Epoch 00706: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0763 - accuracy: 0.9430 - val_loss: 0.1644 - val_accuracy: 0.8771\n",
"Epoch 707/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0786 - accuracy: 0.9450\n",
"Epoch 00707: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0786 - accuracy: 0.9450 - val_loss: 0.1851 - val_accuracy: 0.8647\n",
"Epoch 708/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0753 - accuracy: 0.9435\n",
"Epoch 00708: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0753 - accuracy: 0.9435 - val_loss: 0.1918 - val_accuracy: 0.8476\n",
"Epoch 709/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0703 - accuracy: 0.9460\n",
"Epoch 00709: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0703 - accuracy: 0.9460 - val_loss: 0.1595 - val_accuracy: 0.8740\n",
"Epoch 710/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0686 - accuracy: 0.9507\n",
"Epoch 00710: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0686 - accuracy: 0.9507 - val_loss: 0.1845 - val_accuracy: 0.8600\n",
"Epoch 711/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0731 - accuracy: 0.9404\n",
"Epoch 00711: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0731 - accuracy: 0.9404 - val_loss: 0.1919 - val_accuracy: 0.8507\n",
"Epoch 712/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0689 - accuracy: 0.9538\n",
"Epoch 00712: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0689 - accuracy: 0.9538 - val_loss: 0.1776 - val_accuracy: 0.8631\n",
"Epoch 713/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9460\n",
"Epoch 00713: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0687 - accuracy: 0.9460 - val_loss: 0.1792 - val_accuracy: 0.8585\n",
"Epoch 714/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9512\n",
"Epoch 00714: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0687 - accuracy: 0.9512 - val_loss: 0.1735 - val_accuracy: 0.8663\n",
"Epoch 715/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0554 - accuracy: 0.9558\n",
"Epoch 00715: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0554 - accuracy: 0.9558 - val_loss: 0.1626 - val_accuracy: 0.8787\n",
"Epoch 716/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0626 - accuracy: 0.9532\n",
"Epoch 00716: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0626 - accuracy: 0.9532 - val_loss: 0.1663 - val_accuracy: 0.8880\n",
"Epoch 717/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0705 - accuracy: 0.9491\n",
"Epoch 00717: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0705 - accuracy: 0.9491 - val_loss: 0.1693 - val_accuracy: 0.8678\n",
"Epoch 718/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0656 - accuracy: 0.9496\n",
"Epoch 00718: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0656 - accuracy: 0.9496 - val_loss: 0.1681 - val_accuracy: 0.8725\n",
"Epoch 719/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0660 - accuracy: 0.9496\n",
"Epoch 00719: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0660 - accuracy: 0.9496 - val_loss: 0.1947 - val_accuracy: 0.8523\n",
"Epoch 720/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0716 - accuracy: 0.9455\n",
"Epoch 00720: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0716 - accuracy: 0.9455 - val_loss: 0.1668 - val_accuracy: 0.8771\n",
"Epoch 721/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0701 - accuracy: 0.9522\n",
"Epoch 00721: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0701 - accuracy: 0.9522 - val_loss: 0.1468 - val_accuracy: 0.8911\n",
"Epoch 722/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0709 - accuracy: 0.9450\n",
"Epoch 00722: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0709 - accuracy: 0.9450 - val_loss: 0.1625 - val_accuracy: 0.8849\n",
"Epoch 723/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0626 - accuracy: 0.9522\n",
"Epoch 00723: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0626 - accuracy: 0.9522 - val_loss: 0.1555 - val_accuracy: 0.8911\n",
"Epoch 724/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0703 - accuracy: 0.9476\n",
"Epoch 00724: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0703 - accuracy: 0.9476 - val_loss: 0.1850 - val_accuracy: 0.8600\n",
"Epoch 725/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0658 - accuracy: 0.9471\n",
"Epoch 00725: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0658 - accuracy: 0.9471 - val_loss: 0.1720 - val_accuracy: 0.8678\n",
"Epoch 726/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0692 - accuracy: 0.9496\n",
"Epoch 00726: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0692 - accuracy: 0.9496 - val_loss: 0.1659 - val_accuracy: 0.8709\n",
"Epoch 727/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9486\n",
"Epoch 00727: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 21s 344ms/step - loss: 0.0762 - accuracy: 0.9486 - val_loss: 0.2012 - val_accuracy: 0.8460\n",
"Epoch 728/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0728 - accuracy: 0.9466\n",
"Epoch 00728: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 19s 316ms/step - loss: 0.0728 - accuracy: 0.9466 - val_loss: 0.1910 - val_accuracy: 0.8585\n",
"Epoch 729/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0722 - accuracy: 0.9517\n",
"Epoch 00729: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0722 - accuracy: 0.9517 - val_loss: 0.1813 - val_accuracy: 0.8600\n",
"Epoch 730/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0663 - accuracy: 0.9486\n",
"Epoch 00730: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0663 - accuracy: 0.9486 - val_loss: 0.1993 - val_accuracy: 0.8491\n",
"Epoch 731/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 0.9558\n",
"Epoch 00731: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0644 - accuracy: 0.9558 - val_loss: 0.1893 - val_accuracy: 0.8538\n",
"Epoch 732/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0611 - accuracy: 0.9538\n",
"Epoch 00732: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0611 - accuracy: 0.9538 - val_loss: 0.1807 - val_accuracy: 0.8756\n",
"Epoch 733/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0683 - accuracy: 0.9502\n",
"Epoch 00733: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0683 - accuracy: 0.9502 - val_loss: 0.1888 - val_accuracy: 0.8709\n",
"Epoch 734/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0731 - accuracy: 0.9466\n",
"Epoch 00734: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0731 - accuracy: 0.9466 - val_loss: 0.1760 - val_accuracy: 0.8647\n",
"Epoch 735/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0659 - accuracy: 0.9553\n",
"Epoch 00735: accuracy did not improve from 0.95581\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0659 - accuracy: 0.9553 - val_loss: 0.2071 - val_accuracy: 0.8523\n",
"Epoch 736/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0593 - accuracy: 0.9563\n",
"Epoch 00736: accuracy improved from 0.95581 to 0.95632, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-736-0.96.hdf5\n",
"61/60 [==============================] - 22s 367ms/step - loss: 0.0593 - accuracy: 0.9563 - val_loss: 0.1905 - val_accuracy: 0.8616\n",
"Epoch 737/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0600 - accuracy: 0.9568\n",
"Epoch 00737: accuracy improved from 0.95632 to 0.95683, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-737-0.96.hdf5\n",
"61/60 [==============================] - 23s 370ms/step - loss: 0.0600 - accuracy: 0.9568 - val_loss: 0.1764 - val_accuracy: 0.8725\n",
"Epoch 738/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0660 - accuracy: 0.9455\n",
"Epoch 00738: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0660 - accuracy: 0.9455 - val_loss: 0.1832 - val_accuracy: 0.8647\n",
"Epoch 739/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0751 - accuracy: 0.9399\n",
"Epoch 00739: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0751 - accuracy: 0.9399 - val_loss: 0.1836 - val_accuracy: 0.8647\n",
"Epoch 740/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0755 - accuracy: 0.9311\n",
"Epoch 00740: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0755 - accuracy: 0.9311 - val_loss: 0.2128 - val_accuracy: 0.8351\n",
"Epoch 741/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0731 - accuracy: 0.9460\n",
"Epoch 00741: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0731 - accuracy: 0.9460 - val_loss: 0.2729 - val_accuracy: 0.8149\n",
"Epoch 742/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0665 - accuracy: 0.9491\n",
"Epoch 00742: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 21s 339ms/step - loss: 0.0665 - accuracy: 0.9491 - val_loss: 0.2082 - val_accuracy: 0.8491\n",
"Epoch 743/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0684 - accuracy: 0.9481\n",
"Epoch 00743: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 20s 326ms/step - loss: 0.0684 - accuracy: 0.9481 - val_loss: 0.1998 - val_accuracy: 0.8507\n",
"Epoch 744/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9507\n",
"Epoch 00744: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0653 - accuracy: 0.9507 - val_loss: 0.1757 - val_accuracy: 0.8554\n",
"Epoch 745/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 0.9496\n",
"Epoch 00745: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0681 - accuracy: 0.9496 - val_loss: 0.1535 - val_accuracy: 0.8694\n",
"Epoch 746/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9476\n",
"Epoch 00746: accuracy did not improve from 0.95683\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0662 - accuracy: 0.9476 - val_loss: 0.1607 - val_accuracy: 0.8631\n",
"Epoch 747/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0583 - accuracy: 0.9599\n",
"Epoch 00747: accuracy improved from 0.95683 to 0.95992, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-747-0.96.hdf5\n",
"61/60 [==============================] - 23s 377ms/step - loss: 0.0583 - accuracy: 0.9599 - val_loss: 0.1793 - val_accuracy: 0.8554\n",
"Epoch 748/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0768 - accuracy: 0.9404\n",
"Epoch 00748: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0768 - accuracy: 0.9404 - val_loss: 0.2144 - val_accuracy: 0.8351\n",
"Epoch 749/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0635 - accuracy: 0.9496\n",
"Epoch 00749: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0635 - accuracy: 0.9496 - val_loss: 0.1926 - val_accuracy: 0.8460\n",
"Epoch 750/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0659 - accuracy: 0.9517\n",
"Epoch 00750: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0659 - accuracy: 0.9517 - val_loss: 0.1716 - val_accuracy: 0.8709\n",
"Epoch 751/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0751 - accuracy: 0.9481\n",
"Epoch 00751: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0751 - accuracy: 0.9481 - val_loss: 0.2437 - val_accuracy: 0.8243\n",
"Epoch 752/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9460\n",
"Epoch 00752: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0713 - accuracy: 0.9460 - val_loss: 0.2420 - val_accuracy: 0.8165\n",
"Epoch 753/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0676 - accuracy: 0.9512\n",
"Epoch 00753: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0676 - accuracy: 0.9512 - val_loss: 0.2072 - val_accuracy: 0.8445\n",
"Epoch 754/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0609 - accuracy: 0.9584\n",
"Epoch 00754: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0609 - accuracy: 0.9584 - val_loss: 0.1845 - val_accuracy: 0.8709\n",
"Epoch 755/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0626 - accuracy: 0.9502\n",
"Epoch 00755: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0626 - accuracy: 0.9502 - val_loss: 0.2117 - val_accuracy: 0.8429\n",
"Epoch 756/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0621 - accuracy: 0.9543\n",
"Epoch 00756: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0621 - accuracy: 0.9543 - val_loss: 0.2392 - val_accuracy: 0.8227\n",
"Epoch 757/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0694 - accuracy: 0.9512\n",
"Epoch 00757: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0694 - accuracy: 0.9512 - val_loss: 0.1837 - val_accuracy: 0.8725\n",
"Epoch 758/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0590 - accuracy: 0.9589\n",
"Epoch 00758: accuracy did not improve from 0.95992\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0590 - accuracy: 0.9589 - val_loss: 0.1267 - val_accuracy: 0.9067\n",
"Epoch 759/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0488 - accuracy: 0.9687\n",
"Epoch 00759: accuracy improved from 0.95992 to 0.96865, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-759-0.97.hdf5\n",
"61/60 [==============================] - 31s 512ms/step - loss: 0.0488 - accuracy: 0.9687 - val_loss: 0.1494 - val_accuracy: 0.8927\n",
"Epoch 760/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0661 - accuracy: 0.9471\n",
"Epoch 00760: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 304ms/step - loss: 0.0661 - accuracy: 0.9471 - val_loss: 0.1385 - val_accuracy: 0.8942\n",
"Epoch 761/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0593 - accuracy: 0.9558\n",
"Epoch 00761: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0593 - accuracy: 0.9558 - val_loss: 0.1712 - val_accuracy: 0.8756\n",
"Epoch 762/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0630 - accuracy: 0.9507\n",
"Epoch 00762: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0630 - accuracy: 0.9507 - val_loss: 0.1732 - val_accuracy: 0.8678\n",
"Epoch 763/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0639 - accuracy: 0.9512\n",
"Epoch 00763: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0639 - accuracy: 0.9512 - val_loss: 0.1681 - val_accuracy: 0.8709\n",
"Epoch 764/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0696 - accuracy: 0.9450\n",
"Epoch 00764: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0696 - accuracy: 0.9450 - val_loss: 0.1900 - val_accuracy: 0.8647\n",
"Epoch 765/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0612 - accuracy: 0.9553\n",
"Epoch 00765: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0612 - accuracy: 0.9553 - val_loss: 0.1600 - val_accuracy: 0.8834\n",
"Epoch 766/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9568\n",
"Epoch 00766: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0581 - accuracy: 0.9568 - val_loss: 0.1685 - val_accuracy: 0.8802\n",
"Epoch 767/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0730 - accuracy: 0.9409\n",
"Epoch 00767: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0730 - accuracy: 0.9409 - val_loss: 0.1510 - val_accuracy: 0.8880\n",
"Epoch 768/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0622 - accuracy: 0.9476\n",
"Epoch 00768: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0622 - accuracy: 0.9476 - val_loss: 0.1609 - val_accuracy: 0.8756\n",
"Epoch 769/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 0.9532\n",
"Epoch 00769: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0601 - accuracy: 0.9532 - val_loss: 0.1550 - val_accuracy: 0.8756\n",
"Epoch 770/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0676 - accuracy: 0.9476\n",
"Epoch 00770: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0676 - accuracy: 0.9476 - val_loss: 0.1260 - val_accuracy: 0.8974\n",
"Epoch 771/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0577 - accuracy: 0.9604\n",
"Epoch 00771: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0577 - accuracy: 0.9604 - val_loss: 0.1543 - val_accuracy: 0.8818\n",
"Epoch 772/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0627 - accuracy: 0.9563\n",
"Epoch 00772: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0627 - accuracy: 0.9563 - val_loss: 0.1951 - val_accuracy: 0.8538\n",
"Epoch 773/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0634 - accuracy: 0.9471\n",
"Epoch 00773: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0634 - accuracy: 0.9471 - val_loss: 0.1654 - val_accuracy: 0.8771\n",
"Epoch 774/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0657 - accuracy: 0.9460\n",
"Epoch 00774: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0657 - accuracy: 0.9460 - val_loss: 0.1505 - val_accuracy: 0.8818\n",
"Epoch 775/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0632 - accuracy: 0.9522\n",
"Epoch 00775: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 300ms/step - loss: 0.0632 - accuracy: 0.9522 - val_loss: 0.1396 - val_accuracy: 0.8865\n",
"Epoch 776/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0618 - accuracy: 0.9527\n",
"Epoch 00776: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 20s 326ms/step - loss: 0.0618 - accuracy: 0.9527 - val_loss: 0.1479 - val_accuracy: 0.8942\n",
"Epoch 777/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9532\n",
"Epoch 00777: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 303ms/step - loss: 0.0662 - accuracy: 0.9532 - val_loss: 0.1580 - val_accuracy: 0.8771\n",
"Epoch 778/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 0.9563\n",
"Epoch 00778: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0644 - accuracy: 0.9563 - val_loss: 0.1467 - val_accuracy: 0.8989\n",
"Epoch 779/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0659 - accuracy: 0.9532\n",
"Epoch 00779: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0659 - accuracy: 0.9532 - val_loss: 0.1574 - val_accuracy: 0.8756\n",
"Epoch 780/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0665 - accuracy: 0.9486\n",
"Epoch 00780: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0665 - accuracy: 0.9486 - val_loss: 0.1406 - val_accuracy: 0.8958\n",
"Epoch 781/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0618 - accuracy: 0.9553\n",
"Epoch 00781: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0618 - accuracy: 0.9553 - val_loss: 0.1786 - val_accuracy: 0.8491\n",
"Epoch 782/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0683 - accuracy: 0.9471\n",
"Epoch 00782: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0683 - accuracy: 0.9471 - val_loss: 0.1943 - val_accuracy: 0.8320\n",
"Epoch 783/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0641 - accuracy: 0.9491\n",
"Epoch 00783: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0641 - accuracy: 0.9491 - val_loss: 0.1890 - val_accuracy: 0.8491\n",
"Epoch 784/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0649 - accuracy: 0.9486\n",
"Epoch 00784: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0649 - accuracy: 0.9486 - val_loss: 0.1815 - val_accuracy: 0.8585\n",
"Epoch 785/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0661 - accuracy: 0.9476\n",
"Epoch 00785: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0661 - accuracy: 0.9476 - val_loss: 0.1694 - val_accuracy: 0.8709\n",
"Epoch 786/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0636 - accuracy: 0.9532\n",
"Epoch 00786: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 290ms/step - loss: 0.0636 - accuracy: 0.9532 - val_loss: 0.1977 - val_accuracy: 0.8569\n",
"Epoch 787/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0592 - accuracy: 0.9532\n",
"Epoch 00787: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0592 - accuracy: 0.9532 - val_loss: 0.1778 - val_accuracy: 0.8631\n",
"Epoch 788/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0594 - accuracy: 0.9594\n",
"Epoch 00788: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0594 - accuracy: 0.9594 - val_loss: 0.1785 - val_accuracy: 0.8663\n",
"Epoch 789/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0624 - accuracy: 0.9620\n",
"Epoch 00789: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0624 - accuracy: 0.9620 - val_loss: 0.1502 - val_accuracy: 0.8756\n",
"Epoch 790/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 0.9502\n",
"Epoch 00790: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0644 - accuracy: 0.9502 - val_loss: 0.1742 - val_accuracy: 0.8709\n",
"Epoch 791/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0607 - accuracy: 0.9594\n",
"Epoch 00791: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 302ms/step - loss: 0.0607 - accuracy: 0.9594 - val_loss: 0.1681 - val_accuracy: 0.8771\n",
"Epoch 792/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0562 - accuracy: 0.9553\n",
"Epoch 00792: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 21s 344ms/step - loss: 0.0562 - accuracy: 0.9553 - val_loss: 0.1436 - val_accuracy: 0.8958\n",
"Epoch 793/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0676 - accuracy: 0.9502\n",
"Epoch 00793: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0676 - accuracy: 0.9502 - val_loss: 0.1626 - val_accuracy: 0.8709\n",
"Epoch 794/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0607 - accuracy: 0.9491\n",
"Epoch 00794: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0607 - accuracy: 0.9491 - val_loss: 0.1550 - val_accuracy: 0.8771\n",
"Epoch 795/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0589 - accuracy: 0.9589\n",
"Epoch 00795: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0589 - accuracy: 0.9589 - val_loss: 0.1396 - val_accuracy: 0.8911\n",
"Epoch 796/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0628 - accuracy: 0.9538\n",
"Epoch 00796: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0628 - accuracy: 0.9538 - val_loss: 0.1552 - val_accuracy: 0.8756\n",
"Epoch 797/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0638 - accuracy: 0.9502\n",
"Epoch 00797: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0638 - accuracy: 0.9502 - val_loss: 0.1479 - val_accuracy: 0.8834\n",
"Epoch 798/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0640 - accuracy: 0.9517\n",
"Epoch 00798: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0640 - accuracy: 0.9517 - val_loss: 0.1313 - val_accuracy: 0.8911\n",
"Epoch 799/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0616 - accuracy: 0.9507\n",
"Epoch 00799: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0616 - accuracy: 0.9507 - val_loss: 0.1320 - val_accuracy: 0.8880\n",
"Epoch 800/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0612 - accuracy: 0.9553\n",
"Epoch 00800: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0612 - accuracy: 0.9553 - val_loss: 0.1408 - val_accuracy: 0.8880\n",
"Epoch 801/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0716 - accuracy: 0.9430\n",
"Epoch 00801: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0716 - accuracy: 0.9430 - val_loss: 0.1579 - val_accuracy: 0.8709\n",
"Epoch 802/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0637 - accuracy: 0.9553\n",
"Epoch 00802: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0637 - accuracy: 0.9553 - val_loss: 0.1381 - val_accuracy: 0.8880\n",
"Epoch 803/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0689 - accuracy: 0.9445\n",
"Epoch 00803: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0689 - accuracy: 0.9445 - val_loss: 0.1744 - val_accuracy: 0.8725\n",
"Epoch 804/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0603 - accuracy: 0.9568\n",
"Epoch 00804: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0603 - accuracy: 0.9568 - val_loss: 0.1689 - val_accuracy: 0.8756\n",
"Epoch 805/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.9579\n",
"Epoch 00805: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0604 - accuracy: 0.9579 - val_loss: 0.1899 - val_accuracy: 0.8507\n",
"Epoch 806/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0640 - accuracy: 0.9496\n",
"Epoch 00806: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0640 - accuracy: 0.9496 - val_loss: 0.1809 - val_accuracy: 0.8585\n",
"Epoch 807/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9512\n",
"Epoch 00807: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0653 - accuracy: 0.9512 - val_loss: 0.1755 - val_accuracy: 0.8725\n",
"Epoch 808/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0579 - accuracy: 0.9548\n",
"Epoch 00808: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 307ms/step - loss: 0.0579 - accuracy: 0.9548 - val_loss: 0.1502 - val_accuracy: 0.8834\n",
"Epoch 809/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0582 - accuracy: 0.9543\n",
"Epoch 00809: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0582 - accuracy: 0.9543 - val_loss: 0.1736 - val_accuracy: 0.8647\n",
"Epoch 810/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0633 - accuracy: 0.9491\n",
"Epoch 00810: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0633 - accuracy: 0.9491 - val_loss: 0.1409 - val_accuracy: 0.8865\n",
"Epoch 811/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0584 - accuracy: 0.9573\n",
"Epoch 00811: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0584 - accuracy: 0.9573 - val_loss: 0.1324 - val_accuracy: 0.8927\n",
"Epoch 812/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0588 - accuracy: 0.9548\n",
"Epoch 00812: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0588 - accuracy: 0.9548 - val_loss: 0.1500 - val_accuracy: 0.8787\n",
"Epoch 813/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0599 - accuracy: 0.9532\n",
"Epoch 00813: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0599 - accuracy: 0.9532 - val_loss: 0.1304 - val_accuracy: 0.8989\n",
"Epoch 814/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0574 - accuracy: 0.9573\n",
"Epoch 00814: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0574 - accuracy: 0.9573 - val_loss: 0.1394 - val_accuracy: 0.8849\n",
"Epoch 815/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0664 - accuracy: 0.9471\n",
"Epoch 00815: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 298ms/step - loss: 0.0664 - accuracy: 0.9471 - val_loss: 0.1406 - val_accuracy: 0.8849\n",
"Epoch 816/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0606 - accuracy: 0.9476\n",
"Epoch 00816: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0606 - accuracy: 0.9476 - val_loss: 0.1163 - val_accuracy: 0.9051\n",
"Epoch 817/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0642 - accuracy: 0.9527\n",
"Epoch 00817: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0642 - accuracy: 0.9527 - val_loss: 0.1278 - val_accuracy: 0.8942\n",
"Epoch 818/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0680 - accuracy: 0.9496\n",
"Epoch 00818: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0680 - accuracy: 0.9496 - val_loss: 0.1282 - val_accuracy: 0.8911\n",
"Epoch 819/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0737 - accuracy: 0.9471\n",
"Epoch 00819: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0737 - accuracy: 0.9471 - val_loss: 0.1784 - val_accuracy: 0.8631\n",
"Epoch 820/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0650 - accuracy: 0.9527\n",
"Epoch 00820: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0650 - accuracy: 0.9527 - val_loss: 0.1471 - val_accuracy: 0.8771\n",
"Epoch 821/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0596 - accuracy: 0.9491\n",
"Epoch 00821: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0596 - accuracy: 0.9491 - val_loss: 0.1099 - val_accuracy: 0.9036\n",
"Epoch 822/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0640 - accuracy: 0.9553\n",
"Epoch 00822: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0640 - accuracy: 0.9553 - val_loss: 0.1512 - val_accuracy: 0.8756\n",
"Epoch 823/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0610 - accuracy: 0.9553\n",
"Epoch 00823: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0610 - accuracy: 0.9553 - val_loss: 0.1348 - val_accuracy: 0.8880\n",
"Epoch 824/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0602 - accuracy: 0.9538\n",
"Epoch 00824: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0602 - accuracy: 0.9538 - val_loss: 0.1271 - val_accuracy: 0.9005\n",
"Epoch 825/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9573\n",
"Epoch 00825: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0581 - accuracy: 0.9573 - val_loss: 0.1483 - val_accuracy: 0.8818\n",
"Epoch 826/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0656 - accuracy: 0.9512\n",
"Epoch 00826: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0656 - accuracy: 0.9512 - val_loss: 0.1301 - val_accuracy: 0.9005\n",
"Epoch 827/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0675 - accuracy: 0.9502\n",
"Epoch 00827: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0675 - accuracy: 0.9502 - val_loss: 0.1723 - val_accuracy: 0.8600\n",
"Epoch 828/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0561 - accuracy: 0.9609\n",
"Epoch 00828: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0561 - accuracy: 0.9609 - val_loss: 0.1554 - val_accuracy: 0.8818\n",
"Epoch 829/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0551 - accuracy: 0.9589\n",
"Epoch 00829: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0551 - accuracy: 0.9589 - val_loss: 0.1603 - val_accuracy: 0.8787\n",
"Epoch 830/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.9589\n",
"Epoch 00830: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0604 - accuracy: 0.9589 - val_loss: 0.2062 - val_accuracy: 0.8554\n",
"Epoch 831/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0609 - accuracy: 0.9543\n",
"Epoch 00831: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 21s 347ms/step - loss: 0.0609 - accuracy: 0.9543 - val_loss: 0.1987 - val_accuracy: 0.8523\n",
"Epoch 832/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0565 - accuracy: 0.9599\n",
"Epoch 00832: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 20s 320ms/step - loss: 0.0565 - accuracy: 0.9599 - val_loss: 0.1872 - val_accuracy: 0.8523\n",
"Epoch 833/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0629 - accuracy: 0.9558\n",
"Epoch 00833: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0629 - accuracy: 0.9558 - val_loss: 0.1497 - val_accuracy: 0.8849\n",
"Epoch 834/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0666 - accuracy: 0.9502\n",
"Epoch 00834: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0666 - accuracy: 0.9502 - val_loss: 0.1592 - val_accuracy: 0.8771\n",
"Epoch 835/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0564 - accuracy: 0.9573\n",
"Epoch 00835: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0564 - accuracy: 0.9573 - val_loss: 0.1746 - val_accuracy: 0.8678\n",
"Epoch 836/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0599 - accuracy: 0.9615\n",
"Epoch 00836: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0599 - accuracy: 0.9615 - val_loss: 0.1772 - val_accuracy: 0.8663\n",
"Epoch 837/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0595 - accuracy: 0.9522\n",
"Epoch 00837: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0595 - accuracy: 0.9522 - val_loss: 0.1583 - val_accuracy: 0.8740\n",
"Epoch 838/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0547 - accuracy: 0.9620\n",
"Epoch 00838: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0547 - accuracy: 0.9620 - val_loss: 0.1328 - val_accuracy: 0.8896\n",
"Epoch 839/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0599 - accuracy: 0.9532\n",
"Epoch 00839: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0599 - accuracy: 0.9532 - val_loss: 0.1367 - val_accuracy: 0.8834\n",
"Epoch 840/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0588 - accuracy: 0.9579\n",
"Epoch 00840: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0588 - accuracy: 0.9579 - val_loss: 0.1464 - val_accuracy: 0.8818\n",
"Epoch 841/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0587 - accuracy: 0.9527\n",
"Epoch 00841: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0587 - accuracy: 0.9527 - val_loss: 0.1618 - val_accuracy: 0.8616\n",
"Epoch 842/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0550 - accuracy: 0.9584\n",
"Epoch 00842: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0550 - accuracy: 0.9584 - val_loss: 0.1832 - val_accuracy: 0.8445\n",
"Epoch 843/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0593 - accuracy: 0.9589\n",
"Epoch 00843: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0593 - accuracy: 0.9589 - val_loss: 0.1957 - val_accuracy: 0.8414\n",
"Epoch 844/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0627 - accuracy: 0.9517\n",
"Epoch 00844: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0627 - accuracy: 0.9517 - val_loss: 0.2497 - val_accuracy: 0.8212\n",
"Epoch 845/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9553\n",
"Epoch 00845: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0581 - accuracy: 0.9553 - val_loss: 0.1932 - val_accuracy: 0.8476\n",
"Epoch 846/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0589 - accuracy: 0.9573\n",
"Epoch 00846: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0589 - accuracy: 0.9573 - val_loss: 0.2426 - val_accuracy: 0.8274\n",
"Epoch 847/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0639 - accuracy: 0.9502\n",
"Epoch 00847: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 299ms/step - loss: 0.0639 - accuracy: 0.9502 - val_loss: 0.2128 - val_accuracy: 0.8383\n",
"Epoch 848/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0642 - accuracy: 0.9517\n",
"Epoch 00848: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 20s 331ms/step - loss: 0.0642 - accuracy: 0.9517 - val_loss: 0.2187 - val_accuracy: 0.8320\n",
"Epoch 849/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0637 - accuracy: 0.9543\n",
"Epoch 00849: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 304ms/step - loss: 0.0637 - accuracy: 0.9543 - val_loss: 0.2084 - val_accuracy: 0.8320\n",
"Epoch 850/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0623 - accuracy: 0.9532\n",
"Epoch 00850: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0623 - accuracy: 0.9532 - val_loss: 0.2033 - val_accuracy: 0.8491\n",
"Epoch 851/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0548 - accuracy: 0.9630\n",
"Epoch 00851: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0548 - accuracy: 0.9630 - val_loss: 0.2117 - val_accuracy: 0.8243\n",
"Epoch 852/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0643 - accuracy: 0.9538\n",
"Epoch 00852: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0643 - accuracy: 0.9538 - val_loss: 0.1810 - val_accuracy: 0.8538\n",
"Epoch 853/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0558 - accuracy: 0.9615\n",
"Epoch 00853: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0558 - accuracy: 0.9615 - val_loss: 0.1779 - val_accuracy: 0.8600\n",
"Epoch 854/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0526 - accuracy: 0.9625\n",
"Epoch 00854: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0526 - accuracy: 0.9625 - val_loss: 0.1936 - val_accuracy: 0.8476\n",
"Epoch 855/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0534 - accuracy: 0.9594\n",
"Epoch 00855: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0534 - accuracy: 0.9594 - val_loss: 0.1664 - val_accuracy: 0.8678\n",
"Epoch 856/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0529 - accuracy: 0.9615\n",
"Epoch 00856: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0529 - accuracy: 0.9615 - val_loss: 0.1817 - val_accuracy: 0.8491\n",
"Epoch 857/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0589 - accuracy: 0.9579\n",
"Epoch 00857: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0589 - accuracy: 0.9579 - val_loss: 0.1557 - val_accuracy: 0.8694\n",
"Epoch 858/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0563 - accuracy: 0.9568\n",
"Epoch 00858: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0563 - accuracy: 0.9568 - val_loss: 0.1171 - val_accuracy: 0.9051\n",
"Epoch 859/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0584 - accuracy: 0.9604\n",
"Epoch 00859: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0584 - accuracy: 0.9604 - val_loss: 0.1572 - val_accuracy: 0.8694\n",
"Epoch 860/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0592 - accuracy: 0.9594\n",
"Epoch 00860: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0592 - accuracy: 0.9594 - val_loss: 0.1550 - val_accuracy: 0.8725\n",
"Epoch 861/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0596 - accuracy: 0.9538\n",
"Epoch 00861: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0596 - accuracy: 0.9538 - val_loss: 0.1742 - val_accuracy: 0.8523\n",
"Epoch 862/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0569 - accuracy: 0.9553\n",
"Epoch 00862: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0569 - accuracy: 0.9553 - val_loss: 0.1553 - val_accuracy: 0.8802\n",
"Epoch 863/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 0.9558\n",
"Epoch 00863: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0601 - accuracy: 0.9558 - val_loss: 0.1493 - val_accuracy: 0.8849\n",
"Epoch 864/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0619 - accuracy: 0.9496\n",
"Epoch 00864: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0619 - accuracy: 0.9496 - val_loss: 0.1503 - val_accuracy: 0.8865\n",
"Epoch 865/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0592 - accuracy: 0.9507\n",
"Epoch 00865: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0592 - accuracy: 0.9507 - val_loss: 0.2096 - val_accuracy: 0.8414\n",
"Epoch 866/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0598 - accuracy: 0.9558\n",
"Epoch 00866: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0598 - accuracy: 0.9558 - val_loss: 0.2106 - val_accuracy: 0.8429\n",
"Epoch 867/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0568 - accuracy: 0.9573\n",
"Epoch 00867: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0568 - accuracy: 0.9573 - val_loss: 0.2050 - val_accuracy: 0.8383\n",
"Epoch 868/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0529 - accuracy: 0.9599\n",
"Epoch 00868: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0529 - accuracy: 0.9599 - val_loss: 0.1670 - val_accuracy: 0.8818\n",
"Epoch 869/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0574 - accuracy: 0.9579\n",
"Epoch 00869: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0574 - accuracy: 0.9579 - val_loss: 0.1345 - val_accuracy: 0.9005\n",
"Epoch 870/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0642 - accuracy: 0.9527\n",
"Epoch 00870: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0642 - accuracy: 0.9527 - val_loss: 0.1761 - val_accuracy: 0.8600\n",
"Epoch 871/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9563\n",
"Epoch 00871: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0581 - accuracy: 0.9563 - val_loss: 0.1403 - val_accuracy: 0.8787\n",
"Epoch 872/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9563\n",
"Epoch 00872: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 302ms/step - loss: 0.0581 - accuracy: 0.9563 - val_loss: 0.1591 - val_accuracy: 0.8740\n",
"Epoch 873/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0517 - accuracy: 0.9635\n",
"Epoch 00873: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 320ms/step - loss: 0.0517 - accuracy: 0.9635 - val_loss: 0.1841 - val_accuracy: 0.8569\n",
"Epoch 874/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0610 - accuracy: 0.9512\n",
"Epoch 00874: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0610 - accuracy: 0.9512 - val_loss: 0.1575 - val_accuracy: 0.8834\n",
"Epoch 875/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0548 - accuracy: 0.9563\n",
"Epoch 00875: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0548 - accuracy: 0.9563 - val_loss: 0.1857 - val_accuracy: 0.8491\n",
"Epoch 876/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.9532\n",
"Epoch 00876: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0604 - accuracy: 0.9532 - val_loss: 0.2462 - val_accuracy: 0.8087\n",
"Epoch 877/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0607 - accuracy: 0.9522\n",
"Epoch 00877: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0607 - accuracy: 0.9522 - val_loss: 0.1752 - val_accuracy: 0.8678\n",
"Epoch 878/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0569 - accuracy: 0.9568\n",
"Epoch 00878: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0569 - accuracy: 0.9568 - val_loss: 0.2096 - val_accuracy: 0.8320\n",
"Epoch 879/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0573 - accuracy: 0.9563\n",
"Epoch 00879: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0573 - accuracy: 0.9563 - val_loss: 0.2403 - val_accuracy: 0.8087\n",
"Epoch 880/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0539 - accuracy: 0.9579\n",
"Epoch 00880: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0539 - accuracy: 0.9579 - val_loss: 0.2367 - val_accuracy: 0.8243\n",
"Epoch 881/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0552 - accuracy: 0.9620\n",
"Epoch 00881: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0552 - accuracy: 0.9620 - val_loss: 0.2366 - val_accuracy: 0.8180\n",
"Epoch 882/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9527\n",
"Epoch 00882: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0581 - accuracy: 0.9527 - val_loss: 0.2563 - val_accuracy: 0.8227\n",
"Epoch 883/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9625\n",
"Epoch 00883: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0536 - accuracy: 0.9625 - val_loss: 0.2210 - val_accuracy: 0.8243\n",
"Epoch 884/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0549 - accuracy: 0.9579\n",
"Epoch 00884: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0549 - accuracy: 0.9579 - val_loss: 0.1928 - val_accuracy: 0.8507\n",
"Epoch 885/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0528 - accuracy: 0.9589\n",
"Epoch 00885: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0528 - accuracy: 0.9589 - val_loss: 0.2210 - val_accuracy: 0.8305\n",
"Epoch 886/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0595 - accuracy: 0.9512\n",
"Epoch 00886: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0595 - accuracy: 0.9512 - val_loss: 0.2140 - val_accuracy: 0.8258\n",
"Epoch 887/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0617 - accuracy: 0.9486\n",
"Epoch 00887: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 316ms/step - loss: 0.0617 - accuracy: 0.9486 - val_loss: 0.2270 - val_accuracy: 0.8289\n",
"Epoch 888/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0499 - accuracy: 0.9661\n",
"Epoch 00888: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 20s 334ms/step - loss: 0.0499 - accuracy: 0.9661 - val_loss: 0.1544 - val_accuracy: 0.8771\n",
"Epoch 889/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0582 - accuracy: 0.9568\n",
"Epoch 00889: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0582 - accuracy: 0.9568 - val_loss: 0.2036 - val_accuracy: 0.8429\n",
"Epoch 890/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9635\n",
"Epoch 00890: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0511 - accuracy: 0.9635 - val_loss: 0.2078 - val_accuracy: 0.8491\n",
"Epoch 891/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0579 - accuracy: 0.9594\n",
"Epoch 00891: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0579 - accuracy: 0.9594 - val_loss: 0.2117 - val_accuracy: 0.8336\n",
"Epoch 892/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0586 - accuracy: 0.9579\n",
"Epoch 00892: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0586 - accuracy: 0.9579 - val_loss: 0.2273 - val_accuracy: 0.8243\n",
"Epoch 893/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0594 - accuracy: 0.9573\n",
"Epoch 00893: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0594 - accuracy: 0.9573 - val_loss: 0.1961 - val_accuracy: 0.8523\n",
"Epoch 894/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0617 - accuracy: 0.9563\n",
"Epoch 00894: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0617 - accuracy: 0.9563 - val_loss: 0.1647 - val_accuracy: 0.8678\n",
"Epoch 895/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0602 - accuracy: 0.9579\n",
"Epoch 00895: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0602 - accuracy: 0.9579 - val_loss: 0.1596 - val_accuracy: 0.8834\n",
"Epoch 896/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0646 - accuracy: 0.9502\n",
"Epoch 00896: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0646 - accuracy: 0.9502 - val_loss: 0.1874 - val_accuracy: 0.8507\n",
"Epoch 897/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0516 - accuracy: 0.9625\n",
"Epoch 00897: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0516 - accuracy: 0.9625 - val_loss: 0.2125 - val_accuracy: 0.8398\n",
"Epoch 898/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0564 - accuracy: 0.9553\n",
"Epoch 00898: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0564 - accuracy: 0.9553 - val_loss: 0.1874 - val_accuracy: 0.8600\n",
"Epoch 899/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9584\n",
"Epoch 00899: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0536 - accuracy: 0.9584 - val_loss: 0.1599 - val_accuracy: 0.8771\n",
"Epoch 900/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0440 - accuracy: 0.9687\n",
"Epoch 00900: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 305ms/step - loss: 0.0440 - accuracy: 0.9687 - val_loss: 0.1422 - val_accuracy: 0.8880\n",
"Epoch 901/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0559 - accuracy: 0.9563\n",
"Epoch 00901: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 19s 310ms/step - loss: 0.0559 - accuracy: 0.9563 - val_loss: 0.1667 - val_accuracy: 0.8616\n",
"Epoch 902/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0528 - accuracy: 0.9625\n",
"Epoch 00902: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0528 - accuracy: 0.9625 - val_loss: 0.1809 - val_accuracy: 0.8600\n",
"Epoch 903/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0493 - accuracy: 0.9661\n",
"Epoch 00903: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 297ms/step - loss: 0.0493 - accuracy: 0.9661 - val_loss: 0.1925 - val_accuracy: 0.8491\n",
"Epoch 904/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0620 - accuracy: 0.9543\n",
"Epoch 00904: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0620 - accuracy: 0.9543 - val_loss: 0.1786 - val_accuracy: 0.8491\n",
"Epoch 905/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0508 - accuracy: 0.9573\n",
"Epoch 00905: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0508 - accuracy: 0.9573 - val_loss: 0.1805 - val_accuracy: 0.8585\n",
"Epoch 906/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0625 - accuracy: 0.9548\n",
"Epoch 00906: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0625 - accuracy: 0.9548 - val_loss: 0.2069 - val_accuracy: 0.8398\n",
"Epoch 907/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0521 - accuracy: 0.9589\n",
"Epoch 00907: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0521 - accuracy: 0.9589 - val_loss: 0.1439 - val_accuracy: 0.8849\n",
"Epoch 908/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0575 - accuracy: 0.9594\n",
"Epoch 00908: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0575 - accuracy: 0.9594 - val_loss: 0.2126 - val_accuracy: 0.8383\n",
"Epoch 909/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0577 - accuracy: 0.9563\n",
"Epoch 00909: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 297ms/step - loss: 0.0577 - accuracy: 0.9563 - val_loss: 0.1812 - val_accuracy: 0.8663\n",
"Epoch 910/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0557 - accuracy: 0.9563\n",
"Epoch 00910: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0557 - accuracy: 0.9563 - val_loss: 0.1440 - val_accuracy: 0.8865\n",
"Epoch 911/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0574 - accuracy: 0.9579\n",
"Epoch 00911: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0574 - accuracy: 0.9579 - val_loss: 0.1631 - val_accuracy: 0.8911\n",
"Epoch 912/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0580 - accuracy: 0.9604\n",
"Epoch 00912: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0580 - accuracy: 0.9604 - val_loss: 0.1729 - val_accuracy: 0.8663\n",
"Epoch 913/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0502 - accuracy: 0.9651\n",
"Epoch 00913: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0502 - accuracy: 0.9651 - val_loss: 0.1844 - val_accuracy: 0.8585\n",
"Epoch 914/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0532 - accuracy: 0.9630\n",
"Epoch 00914: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0532 - accuracy: 0.9630 - val_loss: 0.1790 - val_accuracy: 0.8616\n",
"Epoch 915/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0539 - accuracy: 0.9615\n",
"Epoch 00915: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0539 - accuracy: 0.9615 - val_loss: 0.1400 - val_accuracy: 0.8911\n",
"Epoch 916/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0546 - accuracy: 0.9584\n",
"Epoch 00916: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 297ms/step - loss: 0.0546 - accuracy: 0.9584 - val_loss: 0.1647 - val_accuracy: 0.8694\n",
"Epoch 917/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0518 - accuracy: 0.9573\n",
"Epoch 00917: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0518 - accuracy: 0.9573 - val_loss: 0.1840 - val_accuracy: 0.8554\n",
"Epoch 918/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0532 - accuracy: 0.9681\n",
"Epoch 00918: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0532 - accuracy: 0.9681 - val_loss: 0.2037 - val_accuracy: 0.8460\n",
"Epoch 919/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0480 - accuracy: 0.9666\n",
"Epoch 00919: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0480 - accuracy: 0.9666 - val_loss: 0.2108 - val_accuracy: 0.8414\n",
"Epoch 920/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0631 - accuracy: 0.9538\n",
"Epoch 00920: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0631 - accuracy: 0.9538 - val_loss: 0.1728 - val_accuracy: 0.8647\n",
"Epoch 921/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9589\n",
"Epoch 00921: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0536 - accuracy: 0.9589 - val_loss: 0.2341 - val_accuracy: 0.8243\n",
"Epoch 922/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0523 - accuracy: 0.9620\n",
"Epoch 00922: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0523 - accuracy: 0.9620 - val_loss: 0.1900 - val_accuracy: 0.8631\n",
"Epoch 923/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0516 - accuracy: 0.9645\n",
"Epoch 00923: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0516 - accuracy: 0.9645 - val_loss: 0.1724 - val_accuracy: 0.8787\n",
"Epoch 924/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0618 - accuracy: 0.9538\n",
"Epoch 00924: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 292ms/step - loss: 0.0618 - accuracy: 0.9538 - val_loss: 0.2014 - val_accuracy: 0.8507\n",
"Epoch 925/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0524 - accuracy: 0.9609\n",
"Epoch 00925: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0524 - accuracy: 0.9609 - val_loss: 0.1826 - val_accuracy: 0.8616\n",
"Epoch 926/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0506 - accuracy: 0.9630\n",
"Epoch 00926: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0506 - accuracy: 0.9630 - val_loss: 0.1862 - val_accuracy: 0.8647\n",
"Epoch 927/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 0.9584\n",
"Epoch 00927: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0601 - accuracy: 0.9584 - val_loss: 0.1728 - val_accuracy: 0.8678\n",
"Epoch 928/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0521 - accuracy: 0.9620\n",
"Epoch 00928: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0521 - accuracy: 0.9620 - val_loss: 0.1793 - val_accuracy: 0.8616\n",
"Epoch 929/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0490 - accuracy: 0.9666\n",
"Epoch 00929: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0490 - accuracy: 0.9666 - val_loss: 0.1379 - val_accuracy: 0.8911\n",
"Epoch 930/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9645\n",
"Epoch 00930: accuracy did not improve from 0.96865\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0511 - accuracy: 0.9645 - val_loss: 0.1584 - val_accuracy: 0.8725\n",
"Epoch 931/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0431 - accuracy: 0.9697\n",
"Epoch 00931: accuracy improved from 0.96865 to 0.96968, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-931-0.97.hdf5\n",
"61/60 [==============================] - 23s 370ms/step - loss: 0.0431 - accuracy: 0.9697 - val_loss: 0.1822 - val_accuracy: 0.8585\n",
"Epoch 932/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0539 - accuracy: 0.9630\n",
"Epoch 00932: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 19s 306ms/step - loss: 0.0539 - accuracy: 0.9630 - val_loss: 0.1799 - val_accuracy: 0.8663\n",
"Epoch 933/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0543 - accuracy: 0.9594\n",
"Epoch 00933: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 20s 330ms/step - loss: 0.0543 - accuracy: 0.9594 - val_loss: 0.2061 - val_accuracy: 0.8445\n",
"Epoch 934/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0455 - accuracy: 0.9692\n",
"Epoch 00934: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0455 - accuracy: 0.9692 - val_loss: 0.1717 - val_accuracy: 0.8585\n",
"Epoch 935/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0530 - accuracy: 0.9620\n",
"Epoch 00935: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0530 - accuracy: 0.9620 - val_loss: 0.1469 - val_accuracy: 0.8849\n",
"Epoch 936/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0579 - accuracy: 0.9599\n",
"Epoch 00936: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 291ms/step - loss: 0.0579 - accuracy: 0.9599 - val_loss: 0.1675 - val_accuracy: 0.8709\n",
"Epoch 937/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0518 - accuracy: 0.9604\n",
"Epoch 00937: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0518 - accuracy: 0.9604 - val_loss: 0.1657 - val_accuracy: 0.8709\n",
"Epoch 938/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0557 - accuracy: 0.9599\n",
"Epoch 00938: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 294ms/step - loss: 0.0557 - accuracy: 0.9599 - val_loss: 0.2044 - val_accuracy: 0.8445\n",
"Epoch 939/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0576 - accuracy: 0.9589\n",
"Epoch 00939: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0576 - accuracy: 0.9589 - val_loss: 0.2213 - val_accuracy: 0.8274\n",
"Epoch 940/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0507 - accuracy: 0.9604\n",
"Epoch 00940: accuracy did not improve from 0.96968\n",
"61/60 [==============================] - 18s 296ms/step - loss: 0.0507 - accuracy: 0.9604 - val_loss: 0.2216 - val_accuracy: 0.8445\n",
"Epoch 941/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0446 - accuracy: 0.9702\n",
"Epoch 00941: accuracy improved from 0.96968 to 0.97020, saving model to /content/drive/My Drive/Thirdsem/DLforA/Assign_6/weights-improvement-941-0.97.hdf5\n",
"61/60 [==============================] - 22s 366ms/step - loss: 0.0446 - accuracy: 0.9702 - val_loss: 0.1871 - val_accuracy: 0.8600\n",
"Epoch 942/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0450 - accuracy: 0.9661\n",
"Epoch 00942: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 18s 295ms/step - loss: 0.0450 - accuracy: 0.9661 - val_loss: 0.1499 - val_accuracy: 0.8787\n",
"Epoch 943/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0559 - accuracy: 0.9620\n",
"Epoch 00943: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.0559 - accuracy: 0.9620 - val_loss: 0.1526 - val_accuracy: 0.8849\n",
"Epoch 944/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0592 - accuracy: 0.9532\n",
"Epoch 00944: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0592 - accuracy: 0.9532 - val_loss: 0.1554 - val_accuracy: 0.8756\n",
"Epoch 945/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0540 - accuracy: 0.9640\n",
"Epoch 00945: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0540 - accuracy: 0.9640 - val_loss: 0.1980 - val_accuracy: 0.8414\n",
"Epoch 946/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0510 - accuracy: 0.9635\n",
"Epoch 00946: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0510 - accuracy: 0.9635 - val_loss: 0.1903 - val_accuracy: 0.8507\n",
"Epoch 947/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0520 - accuracy: 0.9656\n",
"Epoch 00947: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0520 - accuracy: 0.9656 - val_loss: 0.1788 - val_accuracy: 0.8616\n",
"Epoch 948/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0478 - accuracy: 0.9609\n",
"Epoch 00948: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.0478 - accuracy: 0.9609 - val_loss: 0.1641 - val_accuracy: 0.8834\n",
"Epoch 949/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0564 - accuracy: 0.9584\n",
"Epoch 00949: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 19s 317ms/step - loss: 0.0564 - accuracy: 0.9584 - val_loss: 0.1542 - val_accuracy: 0.8849\n",
"Epoch 950/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0544 - accuracy: 0.9604\n",
"Epoch 00950: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 18s 297ms/step - loss: 0.0544 - accuracy: 0.9604 - val_loss: 0.1414 - val_accuracy: 0.8880\n",
"Epoch 951/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0468 - accuracy: 0.9594\n",
"Epoch 00951: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0468 - accuracy: 0.9594 - val_loss: 0.1644 - val_accuracy: 0.8834\n",
"Epoch 952/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0557 - accuracy: 0.9584\n",
"Epoch 00952: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0557 - accuracy: 0.9584 - val_loss: 0.1783 - val_accuracy: 0.8756\n",
"Epoch 953/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0508 - accuracy: 0.9635\n",
"Epoch 00953: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0508 - accuracy: 0.9635 - val_loss: 0.1896 - val_accuracy: 0.8709\n",
"Epoch 954/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0510 - accuracy: 0.9630\n",
"Epoch 00954: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0510 - accuracy: 0.9630 - val_loss: 0.1453 - val_accuracy: 0.8818\n",
"Epoch 955/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0509 - accuracy: 0.9645\n",
"Epoch 00955: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0509 - accuracy: 0.9645 - val_loss: 0.1518 - val_accuracy: 0.8771\n",
"Epoch 956/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0495 - accuracy: 0.9635\n",
"Epoch 00956: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0495 - accuracy: 0.9635 - val_loss: 0.1722 - val_accuracy: 0.8818\n",
"Epoch 957/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0540 - accuracy: 0.9615\n",
"Epoch 00957: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0540 - accuracy: 0.9615 - val_loss: 0.1448 - val_accuracy: 0.8942\n",
"Epoch 958/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9604\n",
"Epoch 00958: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0511 - accuracy: 0.9604 - val_loss: 0.1869 - val_accuracy: 0.8600\n",
"Epoch 959/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0558 - accuracy: 0.9553\n",
"Epoch 00959: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0558 - accuracy: 0.9553 - val_loss: 0.1921 - val_accuracy: 0.8616\n",
"Epoch 960/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0524 - accuracy: 0.9651\n",
"Epoch 00960: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0524 - accuracy: 0.9651 - val_loss: 0.2107 - val_accuracy: 0.8445\n",
"Epoch 961/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0533 - accuracy: 0.9630\n",
"Epoch 00961: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0533 - accuracy: 0.9630 - val_loss: 0.2042 - val_accuracy: 0.8507\n",
"Epoch 962/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0547 - accuracy: 0.9604\n",
"Epoch 00962: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0547 - accuracy: 0.9604 - val_loss: 0.2228 - val_accuracy: 0.8569\n",
"Epoch 963/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0529 - accuracy: 0.9645\n",
"Epoch 00963: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0529 - accuracy: 0.9645 - val_loss: 0.2255 - val_accuracy: 0.8429\n",
"Epoch 964/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0471 - accuracy: 0.9666\n",
"Epoch 00964: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 283ms/step - loss: 0.0471 - accuracy: 0.9666 - val_loss: 0.2057 - val_accuracy: 0.8600\n",
"Epoch 965/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0586 - accuracy: 0.9543\n",
"Epoch 00965: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0586 - accuracy: 0.9543 - val_loss: 0.1884 - val_accuracy: 0.8554\n",
"Epoch 966/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0483 - accuracy: 0.9661\n",
"Epoch 00966: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 18s 293ms/step - loss: 0.0483 - accuracy: 0.9661 - val_loss: 0.1913 - val_accuracy: 0.8631\n",
"Epoch 967/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9573\n",
"Epoch 00967: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 20s 325ms/step - loss: 0.0536 - accuracy: 0.9573 - val_loss: 0.1763 - val_accuracy: 0.8694\n",
"Epoch 968/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0504 - accuracy: 0.9656\n",
"Epoch 00968: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 18s 287ms/step - loss: 0.0504 - accuracy: 0.9656 - val_loss: 0.1735 - val_accuracy: 0.8818\n",
"Epoch 969/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0484 - accuracy: 0.9645\n",
"Epoch 00969: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0484 - accuracy: 0.9645 - val_loss: 0.2134 - val_accuracy: 0.8616\n",
"Epoch 970/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0472 - accuracy: 0.9661\n",
"Epoch 00970: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0472 - accuracy: 0.9661 - val_loss: 0.1997 - val_accuracy: 0.8647\n",
"Epoch 971/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0532 - accuracy: 0.9609\n",
"Epoch 00971: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0532 - accuracy: 0.9609 - val_loss: 0.1418 - val_accuracy: 0.8911\n",
"Epoch 972/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0541 - accuracy: 0.9609\n",
"Epoch 00972: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0541 - accuracy: 0.9609 - val_loss: 0.1464 - val_accuracy: 0.8834\n",
"Epoch 973/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0497 - accuracy: 0.9645\n",
"Epoch 00973: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0497 - accuracy: 0.9645 - val_loss: 0.1766 - val_accuracy: 0.8725\n",
"Epoch 974/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0480 - accuracy: 0.9656\n",
"Epoch 00974: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0480 - accuracy: 0.9656 - val_loss: 0.1745 - val_accuracy: 0.8569\n",
"Epoch 975/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0488 - accuracy: 0.9645\n",
"Epoch 00975: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0488 - accuracy: 0.9645 - val_loss: 0.1664 - val_accuracy: 0.8694\n",
"Epoch 976/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0523 - accuracy: 0.9584\n",
"Epoch 00976: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0523 - accuracy: 0.9584 - val_loss: 0.1791 - val_accuracy: 0.8678\n",
"Epoch 977/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0534 - accuracy: 0.9635\n",
"Epoch 00977: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0534 - accuracy: 0.9635 - val_loss: 0.2188 - val_accuracy: 0.8383\n",
"Epoch 978/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0497 - accuracy: 0.9640\n",
"Epoch 00978: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0497 - accuracy: 0.9640 - val_loss: 0.1664 - val_accuracy: 0.8802\n",
"Epoch 979/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0556 - accuracy: 0.9645\n",
"Epoch 00979: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0556 - accuracy: 0.9645 - val_loss: 0.1834 - val_accuracy: 0.8694\n",
"Epoch 980/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0463 - accuracy: 0.9697\n",
"Epoch 00980: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0463 - accuracy: 0.9697 - val_loss: 0.1614 - val_accuracy: 0.8771\n",
"Epoch 981/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0542 - accuracy: 0.9594\n",
"Epoch 00981: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0542 - accuracy: 0.9594 - val_loss: 0.1905 - val_accuracy: 0.8491\n",
"Epoch 982/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0638 - accuracy: 0.9532\n",
"Epoch 00982: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0638 - accuracy: 0.9532 - val_loss: 0.1523 - val_accuracy: 0.8865\n",
"Epoch 983/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0561 - accuracy: 0.9579\n",
"Epoch 00983: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.0561 - accuracy: 0.9579 - val_loss: 0.1815 - val_accuracy: 0.8647\n",
"Epoch 984/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0441 - accuracy: 0.9687\n",
"Epoch 00984: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 19s 311ms/step - loss: 0.0441 - accuracy: 0.9687 - val_loss: 0.1603 - val_accuracy: 0.8756\n",
"Epoch 985/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0466 - accuracy: 0.9640\n",
"Epoch 00985: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 20s 323ms/step - loss: 0.0466 - accuracy: 0.9640 - val_loss: 0.1853 - val_accuracy: 0.8678\n",
"Epoch 986/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0464 - accuracy: 0.9666\n",
"Epoch 00986: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 286ms/step - loss: 0.0464 - accuracy: 0.9666 - val_loss: 0.1877 - val_accuracy: 0.8694\n",
"Epoch 987/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0578 - accuracy: 0.9563\n",
"Epoch 00987: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0578 - accuracy: 0.9563 - val_loss: 0.2028 - val_accuracy: 0.8507\n",
"Epoch 988/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0555 - accuracy: 0.9558\n",
"Epoch 00988: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0555 - accuracy: 0.9558 - val_loss: 0.2346 - val_accuracy: 0.8398\n",
"Epoch 989/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0476 - accuracy: 0.9671\n",
"Epoch 00989: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0476 - accuracy: 0.9671 - val_loss: 0.2016 - val_accuracy: 0.8538\n",
"Epoch 990/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0516 - accuracy: 0.9579\n",
"Epoch 00990: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0516 - accuracy: 0.9579 - val_loss: 0.1875 - val_accuracy: 0.8585\n",
"Epoch 991/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0500 - accuracy: 0.9620\n",
"Epoch 00991: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0500 - accuracy: 0.9620 - val_loss: 0.1986 - val_accuracy: 0.8538\n",
"Epoch 992/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0491 - accuracy: 0.9604\n",
"Epoch 00992: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0491 - accuracy: 0.9604 - val_loss: 0.1530 - val_accuracy: 0.8849\n",
"Epoch 993/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0515 - accuracy: 0.9589\n",
"Epoch 00993: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0515 - accuracy: 0.9589 - val_loss: 0.1604 - val_accuracy: 0.8802\n",
"Epoch 994/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9651\n",
"Epoch 00994: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0511 - accuracy: 0.9651 - val_loss: 0.1785 - val_accuracy: 0.8678\n",
"Epoch 995/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0523 - accuracy: 0.9620\n",
"Epoch 00995: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0523 - accuracy: 0.9620 - val_loss: 0.1977 - val_accuracy: 0.8694\n",
"Epoch 996/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0467 - accuracy: 0.9692\n",
"Epoch 00996: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0467 - accuracy: 0.9692 - val_loss: 0.1857 - val_accuracy: 0.8709\n",
"Epoch 997/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0504 - accuracy: 0.9620\n",
"Epoch 00997: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0504 - accuracy: 0.9620 - val_loss: 0.1685 - val_accuracy: 0.8818\n",
"Epoch 998/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0475 - accuracy: 0.9661\n",
"Epoch 00998: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 284ms/step - loss: 0.0475 - accuracy: 0.9661 - val_loss: 0.1974 - val_accuracy: 0.8663\n",
"Epoch 999/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0474 - accuracy: 0.9635\n",
"Epoch 00999: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0474 - accuracy: 0.9635 - val_loss: 0.2103 - val_accuracy: 0.8616\n",
"Epoch 1000/1000\n",
"61/60 [==============================] - ETA: 0s - loss: 0.0454 - accuracy: 0.9671\n",
"Epoch 01000: accuracy did not improve from 0.97020\n",
"61/60 [==============================] - 17s 285ms/step - loss: 0.0454 - accuracy: 0.9671 - val_loss: 0.1423 - val_accuracy: 0.9005\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cYduXAFH5q1t"
},
"source": [
"for keras.layers in base_model.layers[:70]:\n",
" #count=count+1\n",
" keras.layers.trainable=False"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Je8clAgs1PZW",
"outputId": "e6ca7d9a-0225-4c3c-8d66-8d75cffdb258",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from keras.preprocessing.image import load_img\n",
"from keras.preprocessing.image import img_to_array\n",
"from keras.applications.vgg16 import preprocess_input\n",
"from keras.applications.vgg16 import decode_predictions\n",
"from keras.applications.vgg16 import VGG16\n",
"from keras.models import Model\n",
"from pickle import dump\n",
"# load an image from file\n",
"image = load_img('/content/COVID-19 Radiography Database/COVID-19/COVID-19 (1).png', target_size=(224, 224,3))\n",
"# convert the image pixels to a numpy array\n",
"image = img_to_array(image)\n",
"# reshape data for the model\n",
"image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n",
"# prepare the image for the VGG model\n",
"image = preprocess_input(image)\n",
"# load model\n",
"model = VGG16()\n",
"# remove the output layer\n",
"model = Model(inputs=model.inputs, outputs=model.layers[-2].output)\n",
"# get extracted features\n",
"features = model.predict(image)\n",
"print(features.shape)\n",
"# save to file\n",
"#dump(features, open('dog.pkl', 'wb'))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(1, 4096)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PWdltqYf1kie",
"outputId": "2e0c4d63-d79b-461d-bfae-1a27864db57f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"image.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1, 224, 224, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rSxQwzJu0aq8",
"outputId": "282271b7-c8c0-4998-c529-32d2b428de36",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"plt.imshow(arr)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f7d2b7e5be0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mgl8fgkirqTh",
"outputId": "b475e0ea-174b-49f2-9cc4-57f5660fbf37",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(COV_L)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"219"
]
},
"metadata": {
"tags": []
},
"execution_count": 96
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5wXiQ8EZq0jE",
"outputId": "e9c55a1e-17b3-444e-ad59-0f3ca3c65073",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"COV_L[0].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(128, 128, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 92
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SBQHU4nSq8Ok"
},
"source": [
"x_train=np.zeros((len(COV_L),128,128,3))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XBIYrk68rexH",
"outputId": "5a097a68-d697-4501-de1c-9eebdcce98f6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"x_train.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(219, 128, 128, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 93
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_XgklDfbqqxL",
"outputId": "2fa0f6b0-cc0b-427e-e7d2-eb8a6df8a088",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
}
},
"source": [
"for i in range(0,len(x_train)):\n",
" x_train[i,:,:,:]=COV_L[i]\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-97-e91e367a48b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCOV_L\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (128,128) into shape (128,128,3)"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SjMeiOgOoRqR"
},
"source": [
"f=np.repeat(0,len(COV_L))\n",
"g=np.repeat(1,len(NOR_L))\n",
"h=np.repeat(2,len(VIR_L))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0Zd_g7KmzEGi"
},
"source": [
"g=np.repeat(0,len(NOR_L))\n",
"h=np.repeat(1,len(VIR_L))\n",
"lab=np.concatenate((g,h))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nUMwkNHazXJC"
},
"source": [],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "l336Rzx_zVzY",
"outputId": "5747a504-8657-478e-9aff-d2e2cc25a99b",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"np.where(lab==3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([], dtype=int64),)"
]
},
"metadata": {
"tags": []
},
"execution_count": 193
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nfklevfIewDQ"
},
"source": [
"NOR_L=[]\n",
"for i in NOR :\n",
" NOR_L.append(readImage(i))\n",
"\n",
"VIR_L=[]\n",
"for i in VIR :\n",
" VIR_L.append(readImage(i))\n",
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FViFqOIAkvgF",
"outputId": "b68dfac6-7288-4a79-e0d0-a15313f6564f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"VIR_L[1].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(128, 128, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kl9Re52Rm1Kt",
"outputId": "8c33f522-e1c1-4d03-9307-4f5f48b549c6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"plt.imshow(NOR_L[1])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f53fb665320>"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "izCZG2Slewwb"
},
"source": [
"aug = ImageDataGenerator(\n",
"\t\trotation_range=20,\n",
"\t\tzoom_range=0.15,\n",
"\t\twidth_shift_range=0.2,\n",
"\t\theight_shift_range=0.2,\n",
"\t\tshear_range=0.15,\n",
"\t\thorizontal_flip=True,\n",
"\t\tfill_mode=\"nearest\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5z6jWvNhkQhc"
},
"source": [
"base_model = keras.applications.VGG16(\n",
" weights='imagenet', # Load weights pre-trained on ImageNet.\n",
" input_shape=(128, 128, 3),\n",
" include_top=False) # Do not include the ImageNet classifier at the top."
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PLoMjjTDpXOX"
},
"source": [
"from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten\n",
"from keras.optimizers import SGD"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "woepEvsfw0qx"
},
"source": [
"base_model = keras.applications.VGG16(\n",
" weights='imagenet', # Load weights pre-trained on ImageNet.\n",
" input_shape=(128, 128, 3),\n",
" include_top=False) # Do not include the ImageNet classifier at the top."
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yUcPJGoCoy-y"
},
"source": [
"model=keras.Sequential()\n",
"model.add(base_model)\n",
"model.add(Flatten())\n",
"model.add(Dense(2,activation='sigmoid'))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "z6ANHwXOpEs1",
"outputId": "de6e6863-e54a-48a2-8dac-1d5d954435a6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"model.summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential_11\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"vgg16 (Functional) (None, 4, 4, 512) 14714688 \n",
"=================================================================\n",
"Total params: 14,714,688\n",
"Trainable params: 14,714,688\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sroFrBrcpGIu"
},
"source": [
"opt = SGD(lr=0.001, momentum=0.9)\n",
"model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LW7I4l5vxbCX"
},
"source": [],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JBRDzSYysW1v"
},
"source": [
"model.compile()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mSIl9CdYqDVM",
"outputId": "c4389514-d8b1-491e-f9e4-64b53a9734fc",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"COV_L[10].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(128, 128, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 135
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ianHQZR8sEhD"
},
"source": [
"ar=np.asarray(NOR_L)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-Q04x2y0vx7T"
},
"source": [
"ar1=np.asarray(VIR_L)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YefsAPtDsr7Y"
},
"source": [
"x_train=np.concatenate((ar,ar1))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Dbl0D9lcswG7"
},
"source": [
"y_train=np.concatenate((g,h))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "me65r1FKphTX",
"outputId": "6c1973a3-43c4-443b-8828-8c3dba7b081d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"len(x_train)-len(y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0"
]
},
"metadata": {
"tags": []
},
"execution_count": 160
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6X5qy2AZyZBK",
"outputId": "39c5e3a9-2b85-4645-ece8-68613d398224",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"y_train[2000]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2"
]
},
"metadata": {
"tags": []
},
"execution_count": 180
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n35vNhJoyJTo"
},
"source": [
"from keras.utils.np_utils import to_categorical\n",
"\n",
"Y_Lab = to_categorical(lab,num_classes=2)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dybjl-nPy3YB",
"outputId": "f592d9d2-e73a-426d-b92b-e7da453965a6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"Y_Lab.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2686, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 189
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2uEGC3aDqY8r"
},
"source": [
"H = model.fit(\n",
"\tx=aug.flow(x_train,Y_Lab, batch_size=32),\n",
"\tsteps_per_epoch=len(x_train)/32,\n",
"\tepochs=10)"
],
"execution_count": null,
"outputs": []
}
]
}
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