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Ging-Freecss64 May 7, 2022
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324 changes: 324 additions & 0 deletions Assignment/Assignment3/DL_Assignment_3.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "DL_Assignment_3.ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"Importing tensorflow"
],
"metadata": {
"id": "S07TSoqxDKjm"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kAeFpk-7Bz-q",
"outputId": "38d11f86-c811-4dad-e90b-41373f73dc7c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found GPU at: /device:GPU:0\n"
]
}
],
"source": [
"%tensorflow_version 2.x\n",
"import tensorflow as tf\n",
"device_name = tf.test.gpu_device_name()\n",
"if device_name != '/device:GPU:0':\n",
" raise SystemError('GPU device not found')\n",
"print('Found GPU at: {}'.format(device_name))"
]
},
{
"cell_type": "markdown",
"source": [
"Importing keras, layers and dataset"
],
"metadata": {
"id": "2lqBgJ62DN7v"
}
},
{
"cell_type": "code",
"source": [
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras.datasets import mnist"
],
"metadata": {
"id": "nvxO5FFVCF1k"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Splitting test and train datasets"
],
"metadata": {
"id": "GHrB6KeCDbaX"
}
},
{
"cell_type": "code",
"source": [
"(x_train,y_train), (x_test, y_test) = mnist.load_data()\n",
"print(x_train.shape)\n",
"#we have 60000 datasets of 28 * 28 images\n",
"#normalising the data\n",
"x_train = x_train.reshape([-1, 28, 28]).astype(\"float32\") / 255.0\n",
"x_test = x_test.reshape([-1, 28, 28]).astype(\"float32\") / 255.0"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pWX29ZZICmkb",
"outputId": "750e20ad-9b6c-4504-df87-64ac67df3a70"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(60000, 28, 28)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Building model LSTM (using sequential)"
],
"metadata": {
"id": "DbEoIEEMDin3"
}
},
{
"cell_type": "code",
"source": [
"model = keras.Sequential()\n",
"model.add(keras.Input(shape=(None, 28)))\n",
"model.add(layers.LSTM(256, return_sequences=True, activation=\"relu\"))\n",
"model.add(layers.LSTM(256))\n",
"model.add(layers.Dense(10))\n",
"\n",
"print(model.summary())\n",
"\n",
"model.compile(\n",
" loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
" optimizer = keras.optimizers.Adam(learning_rate = 0.003),\n",
" metrics = [\"accuracy\"],\n",
")\n",
"\n",
"model.fit(x_train, y_train, batch_size = 64, epochs = 10, verbose = 2)\n",
"model.evaluate(x_test, y_test, batch_size = 64, verbose = 2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HHgRpzX7DP0A",
"outputId": "4b14c427-059b-40d2-ee3b-f392df699ee8"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"WARNING:tensorflow:Layer lstm_6 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" lstm_6 (LSTM) (None, None, 256) 291840 \n",
" \n",
" lstm_7 (LSTM) (None, 256) 525312 \n",
" \n",
" dense_4 (Dense) (None, 10) 2570 \n",
" \n",
"=================================================================\n",
"Total params: 819,722\n",
"Trainable params: 819,722\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"Epoch 1/10\n",
"938/938 - 90s - loss: 0.2631 - accuracy: 0.9163 - 90s/epoch - 96ms/step\n",
"Epoch 2/10\n",
"938/938 - 46s - loss: 0.0754 - accuracy: 0.9781 - 46s/epoch - 49ms/step\n",
"Epoch 3/10\n",
"938/938 - 46s - loss: 0.0540 - accuracy: 0.9834 - 46s/epoch - 49ms/step\n",
"Epoch 4/10\n",
"938/938 - 45s - loss: 0.0428 - accuracy: 0.9863 - 45s/epoch - 48ms/step\n",
"Epoch 5/10\n",
"938/938 - 47s - loss: 0.0347 - accuracy: 0.9891 - 47s/epoch - 51ms/step\n",
"Epoch 6/10\n",
"938/938 - 45s - loss: 0.0295 - accuracy: 0.9907 - 45s/epoch - 48ms/step\n",
"Epoch 7/10\n",
"938/938 - 52s - loss: 0.0260 - accuracy: 0.9921 - 52s/epoch - 55ms/step\n",
"Epoch 8/10\n",
"938/938 - 58s - loss: 0.0236 - accuracy: 0.9924 - 58s/epoch - 62ms/step\n",
"Epoch 9/10\n",
"938/938 - 46s - loss: 0.0212 - accuracy: 0.9935 - 46s/epoch - 49ms/step\n",
"Epoch 10/10\n",
"938/938 - 48s - loss: 0.0191 - accuracy: 0.9939 - 48s/epoch - 51ms/step\n",
"157/157 - 2s - loss: 0.0351 - accuracy: 0.9907 - 2s/epoch - 11ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.03508976846933365, 0.9907000064849854]"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"source": [
"Testing it on some random image number (code used from previous assignments)"
],
"metadata": {
"id": "vnCxP5iYDpiU"
}
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"labels = '''0 1 2 3 4 5 6 7 8 9'''.split()\n",
"image_number = 0\n",
"plt.imshow(x_test[image_number])\n",
"n = np.array(x_test[image_number])\n",
"p = n.reshape(1, 28, 28, 1)\n",
"print(model.predict(p).argmax())\n",
"predicted_label = labels[model.predict(p).argmax()]\n",
"original_label = labels[y_test[image_number]]\n",
"print(\"Original label is {} and predicted label is {}\".format(\n",
" original_label, predicted_label))"
],
"metadata": {
"id": "MNBjQ41lGgd2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"outputId": "e8aa2fa6-6d4e-425e-b33c-ecd78e7ead9b"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"7\n",
"Original label is 7 and predicted label is 7\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"image_number = 3\n",
"plt.imshow(x_test[image_number])\n",
"n = np.array(x_test[image_number])\n",
"p = n.reshape(1, 28, 28, 1)\n",
"print(model.predict(p).argmax())\n",
"predicted_label = labels[model.predict(p).argmax()]\n",
"original_label = labels[y_test[image_number]]\n",
"print(\"Original label is {} and predicted label is {}\".format(\n",
" original_label, predicted_label))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"id": "1KwdcQXjCdn0",
"outputId": "de9567a2-38df-463f-ac29-ef8f83e557da"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"0\n",
"Original label is 0 and predicted label is 0\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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Jwg4kQdiBJAg7kMT/A5CpMGXJKJsHAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "ZOgTXyarDFOY"
},
"execution_count": null,
"outputs": []
}
]
}
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