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agrhn/alexnet_tensorflow.ipynb

Created January 19, 2021 19:59
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AlexNet_TensorFlow.ipynb
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alexnet_tensorflow.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "AlexNet_TensorFlow.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyMuNT7vejCVdWaBUL18rN5r",
"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/mrgrhn/f5d7420b2f93632c55552c5bbc8954cd/alexnet_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LiJMoYcAMbkz"
},
"source": [
"import tensorflow as tf\r\n",
"import matplotlib.pyplot as plt\r\n",
"from tensorflow.keras import datasets, layers, models, losses"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6prvle0yQEwT",
"outputId": "37abfca3-22b3-4e43-a3f8-7d3b63e22cee"
},
"source": [
"(x_train, y_train), (x_test, y_test)=tf.keras.datasets.mnist.load_data()\r\n",
"x_train = tf.pad(x_train, [[0, 0], [2,2], [2,2]])/255\r\n",
"x_test = tf.pad(x_test, [[0, 0], [2,2], [2,2]])/255\r\n",
"x_train = tf.expand_dims(x_train, axis=3, name=None)\r\n",
"x_test = tf.expand_dims(x_test, axis=3, name=None)\r\n",
"x_train = tf.repeat(x_train, 3, axis=3)\r\n",
"x_test = tf.repeat(x_test, 3, axis=3)\r\n",
"x_val = x_train[-2000:,:,:,:]\r\n",
"y_val = y_train[-2000:]\r\n",
"x_train = x_train[:-2000,:,:,:]\r\n",
"y_train = y_train[:-2000]"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
"11493376/11490434 [==============================] - 0s 0us/step\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "31AH4xs7QPZX",
"outputId": "769561dd-f7f8-414f-b45b-7992cec76219"
},
"source": [
"model = models.Sequential()\r\n",
"\r\n",
"model.add(layers.experimental.preprocessing.Resizing(224, 224, interpolation=\"bilinear\", input_shape=x_train.shape[1:]))\r\n",
"\r\n",
"model.add(layers.Conv2D(96, 11, strides=4, padding='same'))\r\n",
"model.add(layers.Lambda(tf.nn.local_response_normalization))\r\n",
"model.add(layers.Activation('relu'))\r\n",
"model.add(layers.MaxPooling2D(3, strides=2))\r\n",
"\r\n",
"model.add(layers.Conv2D(256, 5, strides=4, padding='same'))\r\n",
"model.add(layers.Lambda(tf.nn.local_response_normalization))\r\n",
"model.add(layers.Activation('relu'))\r\n",
"model.add(layers.MaxPooling2D(3, strides=2))\r\n",
"\r\n",
"model.add(layers.Conv2D(384, 3, strides=4, padding='same'))\r\n",
"model.add(layers.Activation('relu'))\r\n",
"\r\n",
"model.add(layers.Conv2D(384, 3, strides=4, padding='same'))\r\n",
"model.add(layers.Activation('relu'))\r\n",
"\r\n",
"model.add(layers.Conv2D(256, 3, strides=4, padding='same'))\r\n",
"model.add(layers.Activation('relu'))\r\n",
"\r\n",
"model.add(layers.Flatten())\r\n",
"model.add(layers.Dense(4096, activation='relu'))\r\n",
"model.add(layers.Dropout(0.5))\r\n",
"\r\n",
"model.add(layers.Dense(4096, activation='relu'))\r\n",
"model.add(layers.Dropout(0.5))\r\n",
"\r\n",
"model.add(layers.Dense(10, activation='softmax'))\r\n",
"model.summary()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"resizing (Resizing) (None, 224, 224, 3) 0 \n",
"_________________________________________________________________\n",
"conv2d (Conv2D) (None, 56, 56, 96) 34944 \n",
"_________________________________________________________________\n",
"lambda (Lambda) (None, 56, 56, 96) 0 \n",
"_________________________________________________________________\n",
"activation (Activation) (None, 56, 56, 96) 0 \n",
"_________________________________________________________________\n",
"max_pooling2d (MaxPooling2D) (None, 27, 27, 96) 0 \n",
"_________________________________________________________________\n",
"conv2d_1 (Conv2D) (None, 7, 7, 256) 614656 \n",
"_________________________________________________________________\n",
"lambda_1 (Lambda) (None, 7, 7, 256) 0 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 7, 7, 256) 0 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 3, 3, 256) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 1, 1, 384) 885120 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 1, 1, 384) 0 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 1, 1, 384) 1327488 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 1, 1, 384) 0 \n",
"_________________________________________________________________\n",
"conv2d_4 (Conv2D) (None, 1, 1, 256) 884992 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 1, 1, 256) 0 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 256) 0 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 4096) 1052672 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 4096) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 4096) 16781312 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 4096) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 10) 40970 \n",
"=================================================================\n",
"Total params: 21,622,154\n",
"Trainable params: 21,622,154\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8znNvPDicJB0"
},
"source": [
"model.compile(optimizer='adam', loss=losses.sparse_categorical_crossentropy, metrics=['accuracy'])"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Axu-2K0lcNZB",
"outputId": "c6ae9eff-3854-4593-dc02-45022cd49f40"
},
"source": [
"history = model.fit(x_train, y_train, batch_size=64, epochs=40, validation_data=(x_val, y_val))"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/40\n",
"907/907 [==============================] - 111s 114ms/step - loss: 1.0786 - accuracy: 0.5796 - val_loss: 0.1184 - val_accuracy: 0.9665\n",
"Epoch 2/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.1036 - accuracy: 0.9729 - val_loss: 0.0595 - val_accuracy: 0.9845\n",
"Epoch 3/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0813 - accuracy: 0.9796 - val_loss: 0.0629 - val_accuracy: 0.9860\n",
"Epoch 4/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0623 - accuracy: 0.9833 - val_loss: 0.0337 - val_accuracy: 0.9935\n",
"Epoch 5/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0537 - accuracy: 0.9863 - val_loss: 0.0477 - val_accuracy: 0.9900\n",
"Epoch 6/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0441 - accuracy: 0.9886 - val_loss: 0.0412 - val_accuracy: 0.9915\n",
"Epoch 7/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0384 - accuracy: 0.9905 - val_loss: 0.0364 - val_accuracy: 0.9935\n",
"Epoch 8/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0341 - accuracy: 0.9915 - val_loss: 0.0296 - val_accuracy: 0.9930\n",
"Epoch 9/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0334 - accuracy: 0.9915 - val_loss: 0.0428 - val_accuracy: 0.9930\n",
"Epoch 10/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0267 - accuracy: 0.9928 - val_loss: 0.0435 - val_accuracy: 0.9900\n",
"Epoch 11/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0247 - accuracy: 0.9942 - val_loss: 0.0604 - val_accuracy: 0.9895\n",
"Epoch 12/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0255 - accuracy: 0.9938 - val_loss: 0.0775 - val_accuracy: 0.9880\n",
"Epoch 13/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0212 - accuracy: 0.9948 - val_loss: 0.0538 - val_accuracy: 0.9900\n",
"Epoch 14/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0208 - accuracy: 0.9947 - val_loss: 0.0585 - val_accuracy: 0.9910\n",
"Epoch 15/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0183 - accuracy: 0.9960 - val_loss: 0.0572 - val_accuracy: 0.9920\n",
"Epoch 16/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0189 - accuracy: 0.9952 - val_loss: 0.0521 - val_accuracy: 0.9935\n",
"Epoch 17/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0138 - accuracy: 0.9965 - val_loss: 0.0809 - val_accuracy: 0.9920\n",
"Epoch 18/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0210 - accuracy: 0.9944 - val_loss: 0.0620 - val_accuracy: 0.9930\n",
"Epoch 19/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0152 - accuracy: 0.9965 - val_loss: 0.0451 - val_accuracy: 0.9935\n",
"Epoch 20/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0106 - accuracy: 0.9978 - val_loss: 0.0932 - val_accuracy: 0.9895\n",
"Epoch 21/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0168 - accuracy: 0.9962 - val_loss: 0.2151 - val_accuracy: 0.9855\n",
"Epoch 22/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0199 - accuracy: 0.9954 - val_loss: 0.0654 - val_accuracy: 0.9930\n",
"Epoch 23/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0124 - accuracy: 0.9969 - val_loss: 0.0823 - val_accuracy: 0.9915\n",
"Epoch 24/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0197 - accuracy: 0.9964 - val_loss: 0.1163 - val_accuracy: 0.9845\n",
"Epoch 25/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0127 - accuracy: 0.9972 - val_loss: 0.0681 - val_accuracy: 0.9935\n",
"Epoch 26/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0153 - accuracy: 0.9968 - val_loss: 0.0741 - val_accuracy: 0.9915\n",
"Epoch 27/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0196 - accuracy: 0.9966 - val_loss: 0.0482 - val_accuracy: 0.9940\n",
"Epoch 28/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0079 - accuracy: 0.9984 - val_loss: 0.0406 - val_accuracy: 0.9905\n",
"Epoch 29/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0101 - accuracy: 0.9978 - val_loss: 0.1008 - val_accuracy: 0.9905\n",
"Epoch 30/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0098 - accuracy: 0.9981 - val_loss: 0.0516 - val_accuracy: 0.9920\n",
"Epoch 31/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0229 - accuracy: 0.9959 - val_loss: 0.1060 - val_accuracy: 0.9885\n",
"Epoch 32/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0111 - accuracy: 0.9980 - val_loss: 0.0767 - val_accuracy: 0.9890\n",
"Epoch 33/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.0591 - val_accuracy: 0.9895\n",
"Epoch 34/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0139 - accuracy: 0.9969 - val_loss: 0.0836 - val_accuracy: 0.9945\n",
"Epoch 35/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0104 - accuracy: 0.9981 - val_loss: 0.0532 - val_accuracy: 0.9950\n",
"Epoch 36/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.0883 - val_accuracy: 0.9930\n",
"Epoch 37/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0082 - accuracy: 0.9985 - val_loss: 0.1144 - val_accuracy: 0.9925\n",
"Epoch 38/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0111 - accuracy: 0.9980 - val_loss: 0.0704 - val_accuracy: 0.9935\n",
"Epoch 39/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0092 - accuracy: 0.9981 - val_loss: 0.1139 - val_accuracy: 0.9930\n",
"Epoch 40/40\n",
"907/907 [==============================] - 103s 114ms/step - loss: 0.0051 - accuracy: 0.9990 - val_loss: 0.1677 - val_accuracy: 0.9930\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 910
},
"id": "6XpShvB1mdhT",
"outputId": "f20e7807-2219-4364-85b2-3078c7702f3c"
},
"source": [
"fig, axs = plt.subplots(2, 1, figsize=(15,15))\r\n",
"\r\n",
"axs[0].plot(history.history['loss'])\r\n",
"axs[0].plot(history.history['val_loss'])\r\n",
"axs[0].title.set_text('Training Loss vs Validation Loss')\r\n",
"axs[0].set_xlabel('Epochs')\r\n",
"axs[0].set_ylabel('Loss')\r\n",
"axs[0].legend(['Train', 'Val'])\r\n",
"\r\n",
"axs[1].plot(history.history['accuracy'])\r\n",
"axs[1].plot(history.history['val_accuracy'])\r\n",
"axs[1].title.set_text('Training Accuracy vs Validation Accuracy')\r\n",
"axs[1].set_xlabel('Epochs')\r\n",
"axs[1].set_ylabel('Accuracy')\r\n",
"axs[1].legend(['Train', 'Val'])\r\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f4dc0806a20>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r6b-q2TDmdjn",
"outputId": "b204b40d-453a-4255-e5a9-bc957169cb63"
},
"source": [
"model.evaluate(x_test, y_test)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"313/313 [==============================] - 2s 7ms/step - loss: 0.0809 - accuracy: 0.9879\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.08089840412139893, 0.9879000186920166]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
}
]
}
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