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plant-disease-using-siamese-network-keras.ipynb
{
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"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
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"colab": {
"name": "plant-disease-using-siamese-network-keras.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/bulentsiyah/a1188e29cf4a74d382d2606560001d4c/plant-disease-using-siamese-network-keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KYXZWmShSDiO",
"colab_type": "text"
},
"source": [
"# Plant Disease Using Siamese Network - Keras"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nzzh0FDJSDiP",
"colab_type": "text"
},
"source": [
"We will understand the siamese network by building the plant disease model. The objective of our network is to understand whether two plants are similar or dissimilar.\n",
"\n",
"Once we have our data as pairs along with their labels, we train our siamese network. From the image pair, we feed one image to the network A and another image to the network B. The role of these two networks is only to extract the feature vectors. So, we use two convolution layers with relu activations for extracting the features. Once we have learned the feature, we feed the resultant feature vector from both of the networks to the energy function which measures the similarity, we use Euclidean distance as our energy function. So, we train our network by feeding the image pair to learn the semantic similarity between them."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IgvNEEWlSDiQ",
"colab_type": "text"
},
"source": [
"References :\n",
"* https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/02.%20Face%20and%20Audio%20Recognition%20using%20Siamese%20Networks/2.4%20Face%20Recognition%20Using%20Siamese%20Network.ipynb\n",
"* https://keras.io/examples/mnist_siamese/\n",
"* https://msiam.github.io/Few-Shot-Learning/\n",
"* https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d\n",
"* https://medium.com/@subham.tiwari186/siamese-neural-network-for-one-shot-image-recognition-paper-analysis-44cf7f0c66cb\n",
"* https://www.katnoria.com/siamese-one-shot/\n",
"* https://sorenbouma.github.io/blog/oneshot/"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Fi3Uxq32SDiR",
"colab_type": "code",
"colab": {},
"outputId": "48ec8733-81ac-469c-a5df-96c4acbadd75"
},
"source": [
"import re\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from keras import backend as K\n",
"from keras.layers import Activation\n",
"from keras.layers import Input, Lambda, Dense, Dropout, Convolution2D, MaxPooling2D, Flatten\n",
"from keras.models import Sequential, Model\n",
"from keras.optimizers import RMSprop\n",
"from keras import optimizers\n",
"\n",
"import matplotlib.image as mpimg \n",
"import matplotlib.pyplot as plt \n",
"\n",
"from keras import callbacks\n",
"from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard\n",
"import os\n",
"from keras.models import Model,load_model\n",
"import json\n",
"from keras.models import model_from_json, load_model\n",
"from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JFcKEnelSDiY",
"colab_type": "code",
"colab": {},
"outputId": "e688e6f4-04ce-4658-dbab-667e1ac830c7"
},
"source": [
"selected_image_size = 224\n",
"resize = True\n",
"total_sample_size = 10000 # 5k-50k\n",
"\n",
"channel = 1\n",
"size = 2\n",
"\n",
"folder_count = 38\n",
"image_count = 20 #0-50\n",
"\n",
"if resize == True:\n",
" batch_size=256\n",
"else:\n",
" batch_size=64\n",
"\n",
"path = os.path.join('../input/plantvillage/plantvillage_resize_224/PlantVillage_resize_224/')\n",
"print(path)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"../input/plantvillage/plantvillage_resize_224/PlantVillage_resize_224/\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O6WegDYzSDic",
"colab_type": "text"
},
"source": [
"Now, we define a function for reading our input image. The function read_image takes input as an image and returns the numpy array.\n",
"These feat_vecs_a and feat_vecs_b are the feature vectors of our image pair. Next, we feed this feature vectors to the energy function to compute the distance between them, we use Euclidean distance as our energy function. Next, we define our loss function as contrastive_loss function and compile the model."
]
},
{
"cell_type": "code",
"metadata": {
"id": "JzRsFn4YSDid",
"colab_type": "code",
"colab": {}
},
"source": [
"def read_image(filename, byteorder='>'):\n",
" \n",
" #first we read the image, as a raw file to the buffer\n",
" with open(filename, 'rb') as f:\n",
" buffer = f.read()\n",
" \n",
" #using regex, we extract the header, width, height and maxval of the image\n",
" header, width, height, maxval = re.search(\n",
" b\"(^P5\\s(?:\\s*#.*[\\r\\n])*\"\n",
" b\"(\\d+)\\s(?:\\s*#.*[\\r\\n])*\"\n",
" b\"(\\d+)\\s(?:\\s*#.*[\\r\\n])*\"\n",
" b\"(\\d+)\\s(?:\\s*#.*[\\r\\n]\\s)*)\", buffer).groups()\n",
" \n",
" #then we convert the image to numpy array using np.frombuffer which interprets buffer as one dimensional array\n",
" return np.frombuffer(buffer,\n",
" dtype='u1' if int(maxval) < 256 else byteorder+'u2',\n",
" count=int(width)*int(height),\n",
" offset=len(header)\n",
" ).reshape((int(height), int(width)))\n",
"\n",
"\n",
"def euclidean_distance(vects):\n",
" x, y = vects\n",
" return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))\n",
"\n",
"\n",
"def eucl_dist_output_shape(shapes):\n",
" shape1, shape2 = shapes\n",
" return (shape1[0], 1)\n",
"\n",
"def contrastive_loss(y_true, y_pred):\n",
" margin = 1\n",
" return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))\n",
"\n",
"def compute_accuracy(predictions, labels):\n",
" '''Compute classification accuracy with a fixed threshold on distances.\n",
" '''\n",
" return labels[predictions.ravel() < 0.5].mean()\n",
"\n",
"def accuracy(y_true, y_pred):\n",
" '''Compute classification accuracy with a fixed threshold on distances.\n",
" '''\n",
" return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Qn3cJj0ZSDih",
"colab_type": "text"
},
"source": [
"For an example, Let us open one image,"
]
},
{
"cell_type": "code",
"metadata": {
"id": "h1Jdf--ISDii",
"colab_type": "code",
"colab": {},
"outputId": "7b5e2f07-ffba-46be-df35-b1c5bae2f537"
},
"source": [
"Image.open(path+'s1/1.jpg')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=L size=224x224 at 0x7F4B9A284CC0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "z6NrR094SDim",
"colab_type": "code",
"colab": {},
"outputId": "17876396-d057-4428-fd45-ee8d6b0467da"
},
"source": [
"image = mpimg.imread(path+'s1/1.jpg')\n",
"dim1 = image.shape[0]\n",
"print('dim1',dim1)\n",
"dim2 = image.shape[1]\n",
"print('dim2',dim2)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"dim1 224\n",
"dim2 224\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Fc2T2lKPSDiq",
"colab_type": "code",
"colab": {},
"outputId": "aac41806-0db0-45e7-8b85-b2d4c593c8f8"
},
"source": [
"image.shape"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(224, 224)"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "59nMdEfySDiu",
"colab_type": "text"
},
"source": [
"Now, we define another function get_data for generating our data. As we know, for the Siamese network, data should be in the form of pairs (genuine and imposite) with a binary label.\n",
"\n",
"First, we read the images (img1, img2) from the same directory and store them in the x_genuine_pair array and assign y_genuine to 1. Next, we read the images (img1, img2) from the different directory and store them in the x_imposite pair and assign y_imposite to 0.\n",
"\n",
"Finally, we concatenate both x_genuine_pair, x_imposite to X and y_genuine, y_imposite to Y:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ay6DpNnESDiv",
"colab_type": "code",
"colab": {}
},
"source": [
"def get_data(size, total_sample_size):\n",
" #read the image\n",
" image = mpimg.imread(path+'s' + str(1) + '/' + str(1) + '.jpg', 'rw+')\n",
" #reduce the size\n",
" if resize == True:\n",
" image = image[::size, ::size]\n",
" #get the new size\n",
" dim1 = image.shape[0]\n",
" dim2 = image.shape[1]\n",
"\n",
" count = 0\n",
"\n",
" #initialize the numpy array with the shape of [total_sample, no_of_pairs, dim1, dim2]\n",
" x_geuine_pair = np.zeros([total_sample_size, 2, 1, dim1, dim2])\n",
"\n",
" y_genuine = np.zeros([total_sample_size,1])\n",
"\n",
" for i in range(folder_count):\n",
" for j in range(int(total_sample_size/folder_count)):\n",
" ind1 = 0\n",
" ind2 = 0\n",
"\n",
" #read images from same directory (genuine pair)\n",
" while ind1 == ind2:\n",
" ind1 = np.random.randint(image_count)\n",
" ind2 = np.random.randint(image_count)\n",
"\n",
" # read the two images\n",
" img1 = mpimg.imread(path+'s' + str(i+1) + '/' + str(ind1 + 1) + '.jpg', 'rw+')\n",
" img2 = mpimg.imread(path+'s' + str(i+1) + '/' + str(ind2 + 1) + '.jpg', 'rw+')\n",
"\n",
" #reduce the size\n",
" if resize == True:\n",
" img1 = img1[::size, ::size]\n",
" img2 = img2[::size, ::size]\n",
"\n",
" #store the images to the initialized numpy array\n",
" print\n",
" x_geuine_pair[count, 0, 0, :, :] = img1\n",
" x_geuine_pair[count, 1, 0, :, :] = img2\n",
"\n",
" #as we are drawing images from the same directory we assign label as 1. (genuine pair)\n",
" y_genuine[count] = 1\n",
" count += 1\n",
"\n",
" count = 0\n",
" x_imposite_pair = np.zeros([total_sample_size, 2, 1, dim1, dim2])\n",
" y_imposite = np.zeros([total_sample_size, 1])\n",
"\n",
" for i in range(int(total_sample_size/image_count)):\n",
" for j in range(image_count):\n",
"\n",
" #read images from different directory (imposite pair)\n",
" while True:\n",
" ind1 = np.random.randint(folder_count)\n",
" ind2 = np.random.randint(folder_count)\n",
" if ind1 != ind2:\n",
" break\n",
"\n",
" img1 = mpimg.imread(path+'s' + str(ind1+1) + '/' + str(j + 1) + '.jpg', 'rw+')\n",
" img2 = mpimg.imread(path+'s' + str(ind2+1) + '/' + str(j + 1) + '.jpg', 'rw+')\n",
"\n",
" if resize == True:\n",
" img1 = img1[::size, ::size]\n",
" img2 = img2[::size, ::size]\n",
"\n",
" x_imposite_pair[count, 0, 0, :, :] = img1\n",
" x_imposite_pair[count, 1, 0, :, :] = img2\n",
" #as we are drawing images from the different directory we assign label as 0. (imposite pair)\n",
" y_imposite[count] = 0\n",
" count += 1\n",
"\n",
" #now, concatenate, genuine pairs and imposite pair to get the whole data\n",
" #print(x_geuine_pair.shape)\n",
" #print(x_imposite_pair.shape)\n",
" X = np.concatenate([x_geuine_pair, x_imposite_pair], axis=0)/255\n",
" Y = np.concatenate([y_genuine, y_imposite], axis=0)\n",
"\n",
" return X, Y\n",
"X, Y = get_data(size, total_sample_size)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "K-Hbe8LZSDiy",
"colab_type": "text"
},
"source": [
"\n",
"Now, we generate our data and check our data size. As you can see we have 20,000 data points, out of these 10,000 are genuine pairs and 10,000 are imposite pairs."
]
},
{
"cell_type": "code",
"metadata": {
"id": "K69I-FfxSDiz",
"colab_type": "code",
"colab": {},
"outputId": "25f33714-848f-453d-b19a-2e3670c3c252"
},
"source": [
"X.shape"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(20000, 2, 1, 112, 112)"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZTqXaVrbSDi3",
"colab_type": "code",
"colab": {},
"outputId": "4e1539cd-fe06-4db1-a5bc-57011f524112"
},
"source": [
"Y.shape"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(20000, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AA3qeh9MSDi8",
"colab_type": "code",
"colab": {},
"outputId": "a87d2bf4-b246-4aab-9fe3-c89369b6aa4d"
},
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"plt.figure(figsize=(10,5))\n",
"sns.countplot(Y[:,0])\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wZs8yeTMSDjA",
"colab_type": "text"
},
"source": [
"Next, we split our data for training and testing with 85% training and 15% testing proportions:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4nJarz7OSDjB",
"colab_type": "code",
"colab": {}
},
"source": [
"x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=.15)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OxUXe-vmSDjF",
"colab_type": "code",
"colab": {},
"outputId": "5653a65e-bf74-4d08-ea23-f2f6be9f8d10"
},
"source": [
"print('x_train',x_train.shape)\n",
"print('x_test',x_test.shape)\n",
"print('y_train',y_train.shape)\n",
"print('y_test',y_test.shape)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"x_train (17000, 2, 1, 112, 112)\n",
"x_test (3000, 2, 1, 112, 112)\n",
"y_train (17000, 1)\n",
"y_test (3000, 1)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nSC8dIVMSDjK",
"colab_type": "text"
},
"source": [
"Now that, we have successfully generated our data, we build our siamese network. First, we define the base network which is basically a convolutional network used for feature extraction. We build two convolutional layers with rectified linear unit (ReLU) activations and max pooling followed by flat layer."
]
},
{
"cell_type": "code",
"metadata": {
"id": "ip-7x4ftSDjL",
"colab_type": "code",
"colab": {}
},
"source": [
"def build_base_network(input_shape):\n",
" \n",
" seq = Sequential()\n",
" \n",
" nb_filter = [16, 32, 16]\n",
" kernel_size = 3\n",
" \n",
" \n",
" #convolutional layer 1\n",
" seq.add(Convolution2D(nb_filter[0], kernel_size, kernel_size, input_shape=input_shape,border_mode='valid', dim_ordering='th'))\n",
" seq.add(Activation('relu'))\n",
" seq.add(MaxPooling2D(pool_size=(2, 2))) \n",
" seq.add(Dropout(.25))\n",
" \n",
" #convolutional layer 2\n",
" seq.add(Convolution2D(nb_filter[1], kernel_size, kernel_size, border_mode='valid', dim_ordering='th'))\n",
" seq.add(Activation('relu'))\n",
" seq.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='th')) \n",
" seq.add(Dropout(.25))\n",
" \n",
" #convolutional layer 2\n",
" seq.add(Convolution2D(nb_filter[2], kernel_size, kernel_size, border_mode='valid', dim_ordering='th'))\n",
" seq.add(Activation('relu'))\n",
" seq.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='th')) \n",
" seq.add(Dropout(.25))\n",
"\n",
" #flatten \n",
" seq.add(Flatten())\n",
" seq.add(Dense(128, activation='relu'))\n",
" seq.add(Dropout(0.1))\n",
" seq.add(Dense(50, activation='relu'))\n",
" return seq"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kvzy3gZySDjQ",
"colab_type": "text"
},
"source": [
"Next, we feed the image pair, to the base network, which will return the embeddings that is, feature vectors:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "g66w3aMtSDjR",
"colab_type": "code",
"colab": {},
"outputId": "2747171a-8274-41bc-e36c-213dc1f90ffa"
},
"source": [
"input_dim = x_train.shape[2:]\n",
"img_a = Input(shape=input_dim)\n",
"img_b = Input(shape=input_dim)\n",
"print('input_dim',input_dim)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"input_dim (1, 112, 112)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "frqPhvhFSDjW",
"colab_type": "code",
"colab": {}
},
"source": [
"base_network = build_base_network(input_dim)\n",
"feat_vecs_a = base_network(img_a)\n",
"feat_vecs_b = base_network(img_b)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "M7Ub_b8bSDjd",
"colab_type": "code",
"colab": {}
},
"source": [
"distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([feat_vecs_a, feat_vecs_b])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7Cj3FwmuSDjj",
"colab_type": "text"
},
"source": [
"Now, we set the epoch length to 20 and we use RMS prop for optimization and define our model."
]
},
{
"cell_type": "code",
"metadata": {
"id": "SHVdnOejSDjk",
"colab_type": "code",
"colab": {}
},
"source": [
"epochs = 20\n",
"rms = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)#RMSprop()\n",
"rms = RMSprop()\n",
"\n",
"earlyStopping = EarlyStopping(monitor='val_loss',\n",
" min_delta=0,\n",
" patience=3,\n",
" verbose=1,\n",
" restore_best_weights=True)\n",
"callback_early_stop_reduceLROnPlateau=[earlyStopping]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Fk5WOTHISDjn",
"colab_type": "code",
"colab": {},
"outputId": "26c750ca-5964-4fbc-9ab3-ab875df6b9b3"
},
"source": [
"model = Model(input=[img_a, img_b], output=distance)\n",
"model.compile(loss=contrastive_loss, optimizer=rms,metrics=[accuracy])\n",
"model.summary()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_1 (InputLayer) (None, 1, 112, 112) 0 \n",
"__________________________________________________________________________________________________\n",
"input_2 (InputLayer) (None, 1, 112, 112) 0 \n",
"__________________________________________________________________________________________________\n",
"sequential_1 (Sequential) (None, 50) 652674 input_1[0][0] \n",
" input_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_1 (Lambda) (None, 1) 0 sequential_1[1][0] \n",
" sequential_1[2][0] \n",
"==================================================================================================\n",
"Total params: 652,674\n",
"Trainable params: 652,674\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "OSgG-kNCSDjr",
"colab_type": "code",
"colab": {},
"outputId": "b26a13e4-6093-4462-94ec-4d4d77139626"
},
"source": [
"img_1 = x_train[:, 0]\n",
"img2 = x_train[:, 1]\n",
"img_1.shape\n",
"history = model.fit([img_1, img2], y_train, validation_split=.20,\n",
" batch_size= batch_size, verbose=1, nb_epoch=epochs, callbacks=callback_early_stop_reduceLROnPlateau)\n",
"\n",
"# Option 1: Save Weights + Architecture\n",
"model.save_weights('model_weights.h5')\n",
"with open('model_architecture.json', 'w') as f:\n",
" f.write(model.to_json())\n",
"print('saved')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 13600 samples, validate on 3400 samples\n",
"Epoch 1/20\n",
"13600/13600 [==============================] - 11s 799us/step - loss: 0.2719 - accuracy: 0.5296 - val_loss: 0.3479 - val_accuracy: 0.5000\n",
"Epoch 2/20\n",
"13600/13600 [==============================] - 7s 479us/step - loss: 0.2403 - accuracy: 0.5948 - val_loss: 0.3254 - val_accuracy: 0.5047\n",
"Epoch 3/20\n",
"13600/13600 [==============================] - 7s 480us/step - loss: 0.2165 - accuracy: 0.6520 - val_loss: 0.2618 - val_accuracy: 0.5453\n",
"Epoch 4/20\n",
"13600/13600 [==============================] - 6s 471us/step - loss: 0.1923 - accuracy: 0.7093 - val_loss: 0.2363 - val_accuracy: 0.5912\n",
"Epoch 5/20\n",
"13600/13600 [==============================] - 7s 480us/step - loss: 0.1717 - accuracy: 0.7530 - val_loss: 0.1565 - val_accuracy: 0.7806\n",
"Epoch 6/20\n",
"13600/13600 [==============================] - 6s 478us/step - loss: 0.1508 - accuracy: 0.8001 - val_loss: 0.1903 - val_accuracy: 0.7056\n",
"Epoch 7/20\n",
"13600/13600 [==============================] - 7s 482us/step - loss: 0.1307 - accuracy: 0.8397 - val_loss: 0.1887 - val_accuracy: 0.7074\n",
"Epoch 8/20\n",
"13600/13600 [==============================] - 7s 479us/step - loss: 0.1179 - accuracy: 0.8629 - val_loss: 0.1277 - val_accuracy: 0.8226\n",
"Epoch 9/20\n",
"13600/13600 [==============================] - 7s 479us/step - loss: 0.1067 - accuracy: 0.8849 - val_loss: 0.0938 - val_accuracy: 0.8865\n",
"Epoch 10/20\n",
"13600/13600 [==============================] - 7s 481us/step - loss: 0.0960 - accuracy: 0.9025 - val_loss: 0.0857 - val_accuracy: 0.8965\n",
"Epoch 11/20\n",
"13600/13600 [==============================] - 7s 480us/step - loss: 0.0867 - accuracy: 0.9142 - val_loss: 0.0900 - val_accuracy: 0.9024\n",
"Epoch 12/20\n",
"13600/13600 [==============================] - 6s 471us/step - loss: 0.0796 - accuracy: 0.9282 - val_loss: 0.0940 - val_accuracy: 0.8944\n",
"Epoch 13/20\n",
"13600/13600 [==============================] - 6s 463us/step - loss: 0.0744 - accuracy: 0.9343 - val_loss: 0.0706 - val_accuracy: 0.9321\n",
"Epoch 14/20\n",
"13600/13600 [==============================] - 6s 433us/step - loss: 0.0690 - accuracy: 0.9419 - val_loss: 0.0612 - val_accuracy: 0.9303\n",
"Epoch 15/20\n",
"13600/13600 [==============================] - 7s 481us/step - loss: 0.0651 - accuracy: 0.9482 - val_loss: 0.0544 - val_accuracy: 0.9456\n",
"Epoch 16/20\n",
"13600/13600 [==============================] - 7s 482us/step - loss: 0.0613 - accuracy: 0.9518 - val_loss: 0.0468 - val_accuracy: 0.9500\n",
"Epoch 17/20\n",
"13600/13600 [==============================] - 7s 479us/step - loss: 0.0580 - accuracy: 0.9576 - val_loss: 0.0498 - val_accuracy: 0.9479\n",
"Epoch 18/20\n",
"13600/13600 [==============================] - 6s 472us/step - loss: 0.0564 - accuracy: 0.9550 - val_loss: 0.0415 - val_accuracy: 0.9559\n",
"Epoch 19/20\n",
"13600/13600 [==============================] - 7s 485us/step - loss: 0.0528 - accuracy: 0.9613 - val_loss: 0.0431 - val_accuracy: 0.9524\n",
"Epoch 20/20\n",
"13600/13600 [==============================] - 6s 473us/step - loss: 0.0507 - accuracy: 0.9655 - val_loss: 0.0385 - val_accuracy: 0.9621\n",
"saved\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uuG7DmtqSDjv",
"colab_type": "text"
},
"source": [
"Now, we make predictions with test data. Finally, we check our model accuracy."
]
},
{
"cell_type": "code",
"metadata": {
"id": "KLQsPZs4SDjw",
"colab_type": "code",
"colab": {},
"outputId": "c1cf1888-f514-4a71-aad8-2d347931d658"
},
"source": [
"pred = model.predict([x_test[:, 0], x_test[:, 1]])\n",
"\n",
"print('Accuracy on test set: %0.2f%%' % (100 * compute_accuracy(pred, y_test)))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy on test set: 92.81%\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_SlnQtocSDjz",
"colab_type": "code",
"colab": {},
"outputId": "d5230e26-542d-40e9-8cc4-047a9705465e"
},
"source": [
"pred = model.predict([x_train[:, 0], x_train[:, 1]])\n",
"\n",
"print('* Accuracy on training set: %0.2f%%' % (100 * compute_accuracy(pred, y_train)))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"* Accuracy on training set: 93.81%\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BN-ZowWqSDj2",
"colab_type": "code",
"colab": {},
"outputId": "e9a5d4ec-743a-4584-dcbc-72d6e4b070e9"
},
"source": [
"acc = history.history['accuracy']\n",
"val_acc = history.history['val_accuracy']\n",
"loss = history.history['loss']\n",
"val_loss = history.history['val_loss']\n",
"epochs = range(1, len(acc) + 1)\n",
"#Train and validation accuracy\n",
"plt.plot(epochs, acc, 'b', label='Training accurarcy')\n",
"plt.plot(epochs, val_acc, 'r', label='Validation accurarcy')\n",
"plt.title('Training and Validation accurarcy')\n",
"plt.legend()\n",
"\n",
"plt.figure()\n",
"#Train and validation loss\n",
"plt.plot(epochs, loss, 'b', label='Training loss')\n",
"plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
"plt.title('Training and Validation loss')\n",
"plt.legend()\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"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": "D1f5_cszSDj9",
"colab_type": "text"
},
"source": [
"# Test"
]
},
{
"cell_type": "code",
"metadata": {
"id": "o-uGlTuUSDj_",
"colab_type": "code",
"colab": {},
"outputId": "898a26cc-3af0-4d92-8d5f-662e03320861"
},
"source": [
"if resize==True:\n",
" selected_image_size = int(selected_image_size/2)\n",
" print('selected_image_size',selected_image_size)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"selected_image_size 112\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SoPCvb0wSDkE",
"colab_type": "code",
"colab": {},
"outputId": "37e59e2b-23da-4051-92c6-0b4805ba3777"
},
"source": [
"target_label = 1\n",
"values = np.array(y_test[:,0])\n",
"\n",
"target_index = values.tolist().index(target_label)\n",
"print(target_index)\n",
"print('target_index value : ',y_test[target_index])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"1\n",
"target_index value : [1.]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nqY7zJXkSDkI",
"colab_type": "code",
"colab": {},
"outputId": "92d2ae39-ac40-47ee-a02b-24dbc248ce3c"
},
"source": [
"img1 = (x_test[target_index, 0] * 255).astype(np.uint8)\n",
"img1 = img1.reshape(selected_image_size,selected_image_size)\n",
"print(img1.shape)\n",
"img1\n",
"plt.imshow(img1)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"(112, 112)\n"
],
"name": "stdout"
},
{
"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": "W3UBGdsfSDkN",
"colab_type": "code",
"colab": {},
"outputId": "5e0d0f5e-e130-4f5f-c8cd-5a6277a4a6a3"
},
"source": [
"img2 = (x_test[target_index, 1] * 255).astype(np.uint8)\n",
"img2 = img2.reshape(selected_image_size,selected_image_size)\n",
"print(img2.shape)\n",
"img2\n",
"plt.imshow(img2)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"(112, 112)\n"
],
"name": "stdout"
},
{
"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": "ZPt9VsUSSDkS",
"colab_type": "code",
"colab": {},
"outputId": "c756fc38-ba40-41a4-edf6-3d090f04ae09"
},
"source": [
"x_test[target_index:target_index+1, 0].shape"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1, 1, 112, 112)"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "oIymL8gFSDkW",
"colab_type": "code",
"colab": {},
"outputId": "7759345b-4e02-46e9-a1c3-dce7d2d1f32d"
},
"source": [
"pred = model.predict([x_test[target_index:target_index+1, 0], x_test[target_index:target_index+1, 1]])\n",
"pred = pred < 0.5\n",
"print('y_test[target_index]:',y_test[target_index,0]==True,' pred :',pred)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"y_test[target_index]: True pred : [[ True]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "T2KkXa1ESDkY",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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bulentsiyah commented Jul 28, 2019

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