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Example Convolutional Neural Net with tf.keras
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Example Convolutional Neural Net with tf.keras",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyOZSxeQLCBNdoxuGScth9C7",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/hanneshapke/6dd59da01c5fcd47be2ee445fe2a77cb/example-convolutional-neural-net-with-tf-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": "yewhCLBhUL6e"
},
"source": [
"## Example Deep Learning Model to Classify Cats and Dogs\n",
"\n",
"Modeled after: https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-photos-of-dogs-and-cats/"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uPPuaZmZuvDR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9f2c1fff-6f3b-4415-f722-0e6f65ff6d71"
},
"source": [
"# demo data set\n",
"\n",
"!rm -rf /content/PetImages/\n",
"!rm *.zip\n",
"\n",
"!wget https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
"!unzip -q -d /content/ /content/kagglecatsanddogs_3367a.zip\n",
"\n",
"!echo \"Count images\"\n",
"!ls -U /content/PetImages/Cat | wc -l\n",
"!ls -U /content/PetImages/Dog | wc -l\n",
"\n",
"# !echo \"Reduce images for demo purposes\"\n",
"!cd /content/PetImages/Cat && ls -U | head -7000 | xargs rm \n",
"!cd /content/PetImages/Dog && ls -U | head -7000 | xargs rm \n",
"\n",
"!echo \"Count images after removal\"\n",
"!ls -U /content/PetImages/Cat | wc -l\n",
"!ls -U /content/PetImages/Dog | wc -l"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"rm: cannot remove '*.zip': No such file or directory\n",
"--2021-06-11 15:27:35-- https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
"Resolving download.microsoft.com (download.microsoft.com)... 104.73.0.105, 2600:1406:5800:2b8::e59, 2600:1406:5800:28f::e59\n",
"Connecting to download.microsoft.com (download.microsoft.com)|104.73.0.105|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 824894548 (787M) [application/octet-stream]\n",
"Saving to: ‘kagglecatsanddogs_3367a.zip’\n",
"\n",
"kagglecatsanddogs_3 100%[===================>] 786.68M 73.9MB/s in 11s \n",
"\n",
"2021-06-11 15:27:46 (71.9 MB/s) - ‘kagglecatsanddogs_3367a.zip’ saved [824894548/824894548]\n",
"\n",
"Count images\n",
"12501\n",
"12501\n",
"Count images after removal\n",
"5501\n",
"5501\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_co9t3wiucHo"
},
"source": [
"import tensorflow as tf"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "y3N3mjh5unzk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b9d426c6-c0ba-4894-c7be-7ca09fc51d88"
},
"source": [
"# TensorFlow checks\n",
"\n",
"print(tf.__version__)\n",
"print(tf.config.list_physical_devices('GPU'))"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"2.5.0\n",
"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "H3B-ays_v4SH",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d0f8a2d9-749a-4102-df6a-25b35b1a6993"
},
"source": [
"!ls PetImages/"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Cat Dog\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5CK6MI40wGnb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "8075cc14-a521-4b42-ff7b-9bea68689fa2"
},
"source": [
"# plot example images\n",
"\n",
"import glob \n",
"from matplotlib import pyplot\n",
"from matplotlib.image import imread\n",
"\n",
"\n",
"path = 'PetImages/Cat/*'\n",
"# plot first few images\n",
"for i, filename in enumerate(list(glob.glob(path))[:9]):\n",
" # define subplot\n",
" pyplot.subplot(330 + 1 + i)\n",
" # load image pixels\n",
" image = imread(filename)\n",
" # plot raw pixel data\n",
" pyplot.imshow(image)\n",
"# show the figure\n",
"pyplot.show()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 9 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "i0bJtm2WxS8L",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f26d974c-eb2b-4995-e9b9-a6193b66bc3f"
},
"source": [
"import numpy as np\n",
"import PIL\n",
"import random\n",
"from tensorflow.keras.preprocessing.image import load_img\n",
"from tensorflow.keras.preprocessing.image import img_to_array\n",
"\n",
"# define location of dataset\n",
"path = 'PetImages/*/*'\n",
"\n",
"images, labels = list(), list()\n",
"# enumerate files in the directory\n",
"samples = glob.glob(path)\n",
"random.shuffle(samples)\n",
"for i, filename in enumerate(samples):\n",
" label = 1.0 if 'Dog' in filename else 0.0\n",
" try:\n",
" image = load_img(filename, target_size=(100, 100))\n",
" except PIL.UnidentifiedImageError:\n",
" continue \n",
" # convert to numpy array\n",
" images.append(img_to_array(image) / 255.)\n",
" labels.append(label)\n",
"\n",
"# convert to a numpy arrays\n",
"images = np.asarray(images)\n",
"labels = np.asarray(labels)\n",
"print(images.shape, labels.shape)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 32 bytes but only got 0. Skipping tag 270\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 5 bytes but only got 0. Skipping tag 271\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 272\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 282\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 283\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 20 bytes but only got 0. Skipping tag 306\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 48 bytes but only got 0. Skipping tag 532\n",
" \" Skipping tag %s\" % (size, len(data), tag)\n",
"/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 2 bytes but only got 0. \n",
" warnings.warn(str(msg))\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"(10999, 100, 100, 3) (10999,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "A30RwGOd6QHX",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 285
},
"outputId": "3348a6c9-ed34-47ef-a80f-c36ebff7815f"
},
"source": [
"# check dataset\n",
"_id = 101\n",
"\n",
"pyplot.imshow(images[_id])\n",
"pyplot.show()\n",
"print(labels[_id])\n"
],
"execution_count": 7,
"outputs": [
{
"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"
}
},
{
"output_type": "stream",
"text": [
"0.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fXRkwyjK0NOW"
},
"source": [
"# define a training and validation set\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"test_size = 0.1\n",
"X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=test_size, random_state=42)"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hoqzL-2v7FOb"
},
"source": [
"# 1st model version"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FPJicuC605yv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 129
},
"outputId": "0d046197-86ad-4b91-f91a-0b9aa065a783"
},
"source": [
"# create ML model\n",
"\n",
"def get_model(lr=0.001):\n",
" image = \n",
" \n",
" output = \n",
" model = tf.keras.models.Model(inputs=[image], outputs=[output])\n",
"\n",
" # compile model\n",
" opt = tf.keras.optimizers.SGD(lr=lr)\n",
" model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"model = get_model()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "error",
"ename": "SyntaxError",
"evalue": "ignored",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-10-9631fac80f64>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m image =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ygKLHm-Q4yeh"
},
"source": [
"# print model summary\n",
"model.summary()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OJbeOc274ywE"
},
"source": [
"# plot model\n",
"tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gymLayTR3YtH"
},
"source": [
"model.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=3, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YgKusvU2_XG6"
},
"source": [
"model.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=10, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "urkXCYxM7Mff"
},
"source": [
"# version 2"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "L9AAoOyL4bRq"
},
"source": [
"# create ML model version 2 convolutional layers\n",
"\n",
"def get_model_v2(lr=0.001):\n",
" image = tf.keras.layers.Input(shape=(100, 100, 3))\n",
" convoluted_image = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(image)\n",
" pooled_image = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image)\n",
" convoluted_image_2 = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(pooled_image)\n",
" pooled_image_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image_2)\n",
" flatten_image = tf.keras.layers.Flatten()(pooled_image_2)\n",
" dense_vector = tf.keras.layers.Dense(128, activation='relu')(flatten_image)\n",
" output = tf.keras.layers.Dense(1, activation='sigmoid')(dense_vector)\n",
" model = tf.keras.models.Model(inputs=[image], outputs=[output])\n",
"\n",
" # compile model\n",
" opt = tf.keras.optimizers.SGD(lr=lr)\n",
" model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"model_v2 = get_model_v2()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VkU-8VjW7ja1"
},
"source": [
"# print model summary\n",
"model_v2.summary()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ulMuSBT77sgM"
},
"source": [
"model_v2.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=3, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "K1BKKXgXADlj"
},
"source": [
"model_v2.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=10, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1tY_VUTr77TM"
},
"source": [
"# 3rd version"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "K-M3M9L_8Qc8"
},
"source": [
"# create ML model version 3 convolutional layers\n",
"\n",
"def get_model_v3(lr=0.001):\n",
" image = tf.keras.layers.Input(shape=(100, 100, 3))\n",
" convoluted_image = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(image)\n",
" pooled_image = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image)\n",
" convoluted_image_2 = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(pooled_image)\n",
" pooled_image_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image_2)\n",
" convoluted_image_3 = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(pooled_image_2)\n",
" pooled_image_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image_3)\n",
" flatten_image = tf.keras.layers.Flatten()(pooled_image_3)\n",
" dense_vector = tf.keras.layers.Dense(128, activation='relu')(flatten_image)\n",
" output = tf.keras.layers.Dense(1, activation='sigmoid')(dense_vector)\n",
" model = tf.keras.models.Model(inputs=[image], outputs=[output])\n",
"\n",
" # compile model\n",
" opt = tf.keras.optimizers.SGD(lr=lr)\n",
" model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"model_v3 = get_model_v3()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "aO-w0BKG8dG_"
},
"source": [
"# print model summary\n",
"model_v3.summary()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "D9cAeM8d8dbb"
},
"source": [
"model_v3.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=3, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Yj9fNJHd8fis"
},
"source": [
"model_v3.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=10, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CFT3BBmJAvOG"
},
"source": [
"# final version with an extended flattened layer"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_eqmd4vYAvQ4"
},
"source": [
"# create ML model version 4 convolutional layers and extended dense layers\n",
"\n",
"def get_model_v4(lr=0.001):\n",
" image = tf.keras.layers.Input(shape=(100, 100, 3))\n",
" convoluted_image = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(image)\n",
" pooled_image = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image)\n",
" convoluted_image_2 = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(pooled_image)\n",
" pooled_image_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image_2)\n",
" convoluted_image_3 = tf.keras.layers.Conv2D(filters=32, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(pooled_image_2)\n",
" pooled_image_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image_3)\n",
" convoluted_image_4 = tf.keras.layers.Conv2D(filters=64, \n",
" kernel_size=(3, 3), \n",
" activation='relu', \n",
" padding='same')(pooled_image_3)\n",
" pooled_image_4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(convoluted_image_4)\n",
" flatten_image = tf.keras.layers.Flatten()(pooled_image_4)\n",
" dense_vector_512 = tf.keras.layers.Dense(512, activation='relu')(flatten_image)\n",
" drop_out_dense_vector = tf.keras.layers.Dropout(0.2)(dense_vector_512)\n",
" dense_vector_128 = tf.keras.layers.Dense(128, activation='relu')(drop_out_dense_vector)\n",
" output = tf.keras.layers.Dense(1, activation='sigmoid')(dense_vector_128)\n",
" model = tf.keras.models.Model(inputs=[image], outputs=[output])\n",
"\n",
" # compile model\n",
" opt = tf.keras.optimizers.SGD(lr=lr)\n",
" model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"model_v4 = get_model_v4()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qqC-7sE0AvTw"
},
"source": [
"# print model summary\n",
"model_v4.summary()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8E1S_y2yBjLh"
},
"source": [
"model_v4.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=3, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "owfhE7HSB0FW"
},
"source": [
"model_v4.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=10, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qffvN303CIUl"
},
"source": [
"######## tf.hub Example #########"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7PodPkbuFpzY",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b85baf7c-1dcf-42af-ae39-acd7cbd882e7"
},
"source": [
"import tensorflow_hub as hub\n",
"\n",
"def get_model_v5(lr=0.001):\n",
" image = tf.keras.layers.Input(shape=(100, 100, 3))\n",
" feature_vector = hub.KerasLayer(\"https://tfhub.dev/tensorflow/efficientnet/b3/feature-vector/1\", trainable=False)(image)\n",
" dense_vector_512 = tf.keras.layers.Dense(512, activation='relu')(feature_vector)\n",
" drop_out_dense_vector = tf.keras.layers.Dropout(0.2)(dense_vector_512)\n",
" dense_vector_128 = tf.keras.layers.Dense(128, activation='relu')(drop_out_dense_vector)\n",
" output = tf.keras.layers.Dense(1, activation='sigmoid')(dense_vector_128)\n",
" model = tf.keras.models.Model(inputs=[image], outputs=[output])\n",
"\n",
" # compile model\n",
" opt = tf.keras.optimizers.SGD(lr=lr)\n",
" model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"model_v5 = get_model_v5()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xalLKbTRGP2n",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "52a252c6-9b5b-4f42-e7d7-f9cfca72e9d6"
},
"source": [
"model_v5.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=3, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"310/310 [==============================] - 56s 49ms/step - loss: 0.6390 - accuracy: 0.6570 - val_loss: 0.5475 - val_accuracy: 0.8427\n",
"Epoch 2/3\n",
"310/310 [==============================] - 12s 39ms/step - loss: 0.4970 - accuracy: 0.8612 - val_loss: 0.4234 - val_accuracy: 0.9073\n",
"Epoch 3/3\n",
"310/310 [==============================] - 12s 39ms/step - loss: 0.3916 - accuracy: 0.9085 - val_loss: 0.3348 - val_accuracy: 0.9191\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f4044adca50>"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_v8QGSdNwLqi"
},
"source": [
"import tensorflow_probability as tfp\n",
"from tensorflow_probability.python.distributions import kullback_leibler as kl_lib\n",
"\n",
"tfd = tfp.distributions\n",
"\n",
"conversion_factor = 100\n",
"\n",
"def get_model_prob(lr=0.001):\n",
" image = tf.keras.layers.Input(shape=(100, 100, 3))\n",
" feature_vector = hub.KerasLayer(\"https://tfhub.dev/tensorflow/efficientnet/b3/feature-vector/1\", trainable=False)(image)\n",
" dense_vector_512 = tf.keras.layers.Dense(512, activation='relu')(feature_vector)\n",
" # drop_out_dense_vector = tf.keras.layers.Dropout(0.2)(dense_vector_512)\n",
" \n",
" drop_out_dense_vector = tfp.layers.DenseReparameterization(1,\n",
" kernel_divergence_fn=(lambda q, p, ignore: (1./conversion_factor) * kl_lib.kl_divergence(q, p)),\n",
" bias_divergence_fn=(lambda q, p, ignore: (1./conversion_factor) * kl_lib.kl_divergence(q, p))\n",
" )(dense_vector_512)\n",
" \n",
" dense_vector_128 = tf.keras.layers.Dense(128, activation='relu')(drop_out_dense_vector)\n",
" output = tf.keras.layers.Dense(1, activation='sigmoid')(dense_vector_128)\n",
" model = tf.keras.models.Model(inputs=[image], outputs=[output])\n",
"\n",
" # compile model\n",
" opt = tf.keras.optimizers.SGD(lr=lr)\n",
" model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
" return model"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JLatAtYaxFwO",
"outputId": "4aa255b0-74e8-4e63-fc7a-f2b04877bc4b"
},
"source": [
"model_prob = get_model_prob()\n",
"\n",
"model_prob.fit(x=X_train, \n",
" y=y_train, \n",
" batch_size=32,\n",
" epochs=10, \n",
" validation_data=(X_test, y_test))"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:2191: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
" warnings.warn('`layer.add_variable` is deprecated and '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"310/310 [==============================] - 25s 45ms/step - loss: 13.5838 - accuracy: 0.5418 - val_loss: 13.5749 - val_accuracy: 0.5627\n",
"Epoch 2/10\n",
"310/310 [==============================] - 13s 41ms/step - loss: 13.5651 - accuracy: 0.5874 - val_loss: 13.5548 - val_accuracy: 0.5945\n",
"Epoch 3/10\n",
"310/310 [==============================] - 13s 41ms/step - loss: 13.5376 - accuracy: 0.6643 - val_loss: 13.5200 - val_accuracy: 0.6982\n",
"Epoch 4/10\n",
"310/310 [==============================] - 13s 41ms/step - loss: 13.4904 - accuracy: 0.7472 - val_loss: 13.4575 - val_accuracy: 0.8018\n",
"Epoch 5/10\n",
"310/310 [==============================] - 12s 40ms/step - loss: 13.4097 - accuracy: 0.8343 - val_loss: 13.3580 - val_accuracy: 0.8618\n",
"Epoch 6/10\n",
"310/310 [==============================] - 12s 40ms/step - loss: 13.3008 - accuracy: 0.8801 - val_loss: 13.2428 - val_accuracy: 0.8945\n",
"Epoch 7/10\n",
"310/310 [==============================] - 12s 40ms/step - loss: 13.1902 - accuracy: 0.9035 - val_loss: 13.1378 - val_accuracy: 0.9173\n",
"Epoch 8/10\n",
"310/310 [==============================] - 12s 40ms/step - loss: 13.0981 - accuracy: 0.9179 - val_loss: 13.0574 - val_accuracy: 0.9245\n",
"Epoch 9/10\n",
"310/310 [==============================] - 13s 41ms/step - loss: 13.0312 - accuracy: 0.9237 - val_loss: 13.0005 - val_accuracy: 0.9318\n",
"Epoch 10/10\n",
"310/310 [==============================] - 13s 40ms/step - loss: 12.9809 - accuracy: 0.9272 - val_loss: 12.9611 - val_accuracy: 0.9300\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f3db6404f90>"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aSrtqEXLtuWh"
},
"source": [
"@tf.function\n",
"def process_image(raw_image):\n",
" raw_image = tf.reshape(raw_image, shape=())\n",
" img = tf.image.decode_png(raw_image, channels=3)\n",
" img = tf.image.convert_image_dtype(img, tf.float32)\n",
" resized_img = tf.image.resize(\n",
" img, [100, 100],\n",
" )\n",
" return tf.reshape(resized_img, [-1, 100, 100, 3])\n",
"\n",
"\n",
"image_string = tf.keras.layers.Input(shape=(None,), dtype=tf.string)\n",
"image = tf.keras.layers.Lambda(process_image)(image_string)\n",
"# raw_image = tf.reshape(image_string, shape=())\n",
"# image = tf.image.decode_jpeg(raw_image, channels=3)\n",
"# image = tf.image.convert_image_dtype(image, tf.float32)\n",
"# image = tf.reshape(shape=(-1,), tensor=image_string)\n",
"# image = tf.image.decode_image(image[0], channels=3)\n",
"# image.set_shape([None, None, None, 3])\n",
"# image = tf.reshape([None, None, None, 3], image)\n",
"# image = tf.image.resize(image, [100, 100])\n",
"# image = tf.image.resize(\n",
"# image,\n",
"# target_height=100,\n",
"# target_width=100,\n",
"# )\n",
"out = model_prob(image)\n",
"\n",
"deploy_model = tf.keras.models.Model(image_string, out)"
],
"execution_count": 61,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0rYBkbT6HBWv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117
},
"outputId": "3868d124-f2f4-4ef8-e3f8-32f7b71b53d9"
},
"source": [
"filename = 'jack.jpg'\n",
"load_img(filename, target_size=(100, 100))"
],
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=100x100 at 0x7F3DA91C5750>"
]
},
"metadata": {
"tags": []
},
"execution_count": 62
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IH4A_O_PIWMo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "86e3c1d5-696c-4f0a-ccb1-b462f950b3db"
},
"source": [
"image = img_to_array(load_img(filename, target_size=(100, 100))) / 255.\n",
"model_v5.predict(image.reshape(-1, 100, 100, 3))"
],
"execution_count": 63,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.78052616]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 63
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nhc6PyQ4yRH2",
"outputId": "30d09aaf-64fc-47c3-f766-f69f73d17215"
},
"source": [
"image = img_to_array(load_img(filename, target_size=(100, 100))) / 255.\n",
"# for _ in range(10):\n",
"# print(model_prod.predict(image.reshape(-1, 100, 100, 3)))\n",
"rs = np.zeros((10,))\n",
"for i in range(10):\n",
" rs[i] = model_prod.predict(image.reshape(-1, 100, 100, 3))[0][0]\n",
"\n",
"print(rs.mean())\n",
"print(rs.std())"
],
"execution_count": 64,
"outputs": [
{
"output_type": "stream",
"text": [
"0.9124027252197265\n",
"0.025557857275647553\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "opwEGXQhxA6P",
"outputId": "80b4547b-645e-4e16-a538-12995ca4d45f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"import base64\n",
"\n",
"with open(filename, \"rb\") as image_file:\n",
" # encoded_string = base64.b64encode(image_file.read())\n",
" # base64_image = encoded_string.decode('utf-8')\n",
" encoded_string = image_file.read()\n",
"\n",
"deploy_model.predict(tf.constant([encoded_string], dtype=tf.string))\n",
"# base64_image\n",
"# tf.constant(base64_image, dtype=tf.string)"
],
"execution_count": 69,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.9193907]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 69
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1d6u8hlP0xQn",
"outputId": "bfcb4467-9957-4d7f-c81b-4030d43d548a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"deploy_model.save(\"export_prod_model/\", save_format=\"tf\")"
],
"execution_count": 70,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: export_prod_model/assets\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: export_prod_model/assets\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ifgn92f90xfm",
"outputId": "570d53f8-9e4b-4fce-e9b4-ebd2ec09f0ba",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"!tar -czvf export_prod_model.tar.gz export_prod_model/ "
],
"execution_count": 74,
"outputs": [
{
"output_type": "stream",
"text": [
"export_prod_model/\n",
"export_prod_model/keras_metadata.pb\n",
"export_prod_model/variables/\n",
"export_prod_model/variables/variables.data-00000-of-00001\n",
"export_prod_model/variables/variables.index\n",
"export_prod_model/assets/\n",
"export_prod_model/saved_model.pb\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wz6kkTtF0xjB"
},
"source": [
"##### Test Code\n",
"\n",
"import base64\n",
"import requests\n",
"import json\n",
"image_path = '/Users/hannes/mydata/media/upload/jack.jpg'\n",
"image_base64 = base64.b64encode(open(image_path, 'rb').read()).decode(\"utf-8\")\n",
"data = json.dumps(\n",
" {'instances': [{'b64': image_base64}]})\n",
"\n",
"rs = requests.post(\"http://localhost:8501/v1/models/exported_prob_model:predict\", data=data)\n",
"rs.json()\n",
"rs = requests.post(\"http://localhost:8501/v1/models/exported_prob_model:predict\", data=data)\n",
"rs.json()\n",
"rs = requests.post(\"http://localhost:8501/v1/models/exported_prob_model:predict\", data=data)\n",
"rs.json()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qnJr1R620xl6"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "s3JK7xB1ywqp",
"outputId": "f3bed875-3e4b-41aa-8a15-ab953a062de5"
},
"source": [
"!wget https://animals.sandiegozoo.org/sites/default/files/2016-11/animals_hero_giraffe_1_0.jpg -O giraffe.jpg\n",
"filename = 'giraffe.jpg'\n",
"image = img_to_array(load_img(filename, target_size=(100, 100))) / 255.\n",
"# for _ in range(10):\n",
"# print(model_prod.predict(image.reshape(-1, 100, 100, 3)))\n",
"\n",
"rs = np.zeros((10,))\n",
"for i in range(10):\n",
" rs[i] = model_prod.predict(image.reshape(-1, 100, 100, 3))[0][0]\n",
"\n",
"print(rs.mean())\n",
"print(rs.std())\n",
"print(model_v5.predict(image.reshape(-1, 100, 100, 3)))"
],
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-06-09 22:12:19-- https://animals.sandiegozoo.org/sites/default/files/2016-11/animals_hero_giraffe_1_0.jpg\n",
"Resolving animals.sandiegozoo.org (animals.sandiegozoo.org)... 23.185.0.4, 2620:12a:8000::4, 2620:12a:8001::4\n",
"Connecting to animals.sandiegozoo.org (animals.sandiegozoo.org)|23.185.0.4|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1167458 (1.1M) [image/jpeg]\n",
"Saving to: ‘giraffe.jpg’\n",
"\n",
"\rgiraffe.jpg 0%[ ] 0 --.-KB/s \rgiraffe.jpg 100%[===================>] 1.11M --.-KB/s in 0.02s \n",
"\n",
"2021-06-09 22:12:19 (65.6 MB/s) - ‘giraffe.jpg’ saved [1167458/1167458]\n",
"\n",
"0.3566267341375351\n",
"0.0821622939153927\n",
"[[0.48128143]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IX_-6mY7JXCr",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117
},
"outputId": "3d1c7610-79aa-4128-9206-8a920ad0b8bf"
},
"source": [
"filename = 'ada.jpg'\n",
"load_img(filename, target_size=(100, 100))"
],
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=100x100 at 0x7F671BDA6090>"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YMMQhXJGJXly",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "97a290f5-4b83-4d8e-af0c-dbb1abb42bd6"
},
"source": [
"image = img_to_array(load_img(filename, target_size=(100, 100))) / 255.\n",
"# model_v5.predict(image.reshape(-1, 100, 100, 3))\n",
"# for _ in range(10):\n",
"# print(model_prod.predict(image.reshape(-1, 100, 100, 3)))\n",
"\n",
"rs = np.zeros((10,))\n",
"for i in range(10):\n",
" rs[i] = model_prod.predict(image.reshape(-1, 100, 100, 3))[0][0]\n",
"\n",
"print(rs.mean())\n",
"print(rs.std())"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
"0.0278713570907712\n",
"0.0068269542797126095\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UOyrkkqQJXo6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 117
},
"outputId": "dbc1c364-232b-4279-b8b9-8f4b5153eebe"
},
"source": [
"filename = 'cat_dog.jpg'\n",
"load_img(filename, target_size=(100, 100))"
],
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=100x100 at 0x7F671BCEE750>"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JuvqkWbXI5z5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "88da6f8d-a2bf-4159-b1f3-824b0d74e033"
},
"source": [
"image = img_to_array(load_img(filename, target_size=(100, 100))) / 255.\n",
"model_v5.predict(image.reshape(-1, 100, 100, 3))"
],
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.7625268]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B19vC3pe1b2J",
"outputId": "218d2e7f-7a77-4952-a090-eea8ef834baa"
},
"source": [
"rs = np.zeros((10,))\n",
"for i in range(10):\n",
" rs[i] = model_prod.predict(image.reshape(-1, 100, 100, 3))[0][0]\n",
"\n",
"print(rs.mean())\n",
"print(rs.std())\n",
"print(model_v5.predict(image.reshape(-1, 100, 100, 3)))"
],
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"text": [
"0.9563525497913361\n",
"0.011514218527836137\n",
"[[0.7625268]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yBe8zdlX-ktL",
"outputId": "3cbba4d6-0a57-497f-e51b-29ac23552072",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"!wget https://www.akcpetinsurance.com/res/akc/blog/2018/5-common-signs-that-your-dog-is-sick/header_5_signs_sick.jpg -O upside_down_dog.jpg\n",
"filename = 'upside_down_dog.jpg'\n",
"load_img(filename, target_size=(100, 100))\n",
"\n",
"rs = np.zeros((10,))\n",
"for i in range(10):\n",
" rs[i] = model_prod.predict(image.reshape(-1, 100, 100, 3))[0][0]\n",
"\n",
"print(rs.mean())\n",
"print(rs.std())\n",
"print(model_v5.predict(image.reshape(-1, 100, 100, 3)))"
],
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-06-09 22:53:36-- https://www.akcpetinsurance.com/res/akc/blog/2018/5-common-signs-that-your-dog-is-sick/header_5_signs_sick.jpg\n",
"Resolving www.akcpetinsurance.com (www.akcpetinsurance.com)... 128.136.180.199\n",
"Connecting to www.akcpetinsurance.com (www.akcpetinsurance.com)|128.136.180.199|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 83743 (82K) [image/jpeg]\n",
"Saving to: ‘upside_down_dog.jpg’\n",
"\n",
"\rupside_down_dog.jpg 0%[ ] 0 --.-KB/s \rupside_down_dog.jpg 100%[===================>] 81.78K --.-KB/s in 0.03s \n",
"\n",
"2021-06-09 22:53:36 (3.00 MB/s) - ‘upside_down_dog.jpg’ saved [83743/83743]\n",
"\n",
"0.946334844827652\n",
"0.013220365894552558\n",
"[[0.7625268]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VEKJk3qLJgcs"
},
"source": [
"model_v5.save(\"model_v5\")"
],
"execution_count": null,
"outputs": []
}
]
}
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