From 11bfde75d1ff3ea60be732a521c945a148e25a99 Mon Sep 17 00:00:00 2001
From: tanvibattu <43849158+tanvibattu@users.noreply.github.com>
Date: Tue, 10 Mar 2020 02:48:20 +0530
Subject: [PATCH 1/4] Created using Colaboratory
---
CNN-practice/CNN.ipynb | 400 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 400 insertions(+)
create mode 100644 CNN-practice/CNN.ipynb
diff --git a/CNN-practice/CNN.ipynb b/CNN-practice/CNN.ipynb
new file mode 100644
index 0000000..1514410
--- /dev/null
+++ b/CNN-practice/CNN.ipynb
@@ -0,0 +1,400 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "CNN.ipynb",
+ "provenance": [],
+ "collapsed_sections": [],
+ "authorship_tag": "ABX9TyMyW/mQW5eRn80nw8IlTm8T",
+ "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": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VWMog0eH8s3J",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import tensorflow as tf\n",
+ "import keras\n",
+ "from keras.models import Sequential\n",
+ "from keras.datasets import mnist\n",
+ "from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\n",
+ "from keras.datasets import mnist\n",
+ "from keras.optimizers import RMSprop, adam"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kQ820FbfPcqT",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bGO_tNS5PnYd",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "3dbbf55b-1058-4f4b-da54-e8c93ff996fc"
+ },
+ "source": [
+ "x_train.shape"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(60000, 28, 28)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "owOL3pkCPoK4",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 69
+ },
+ "outputId": "b1d66ba1-81a5-4b14-a3b3-dacc8ecb1947"
+ },
+ "source": [
+ "#Reshaping the array to 4-dims so that it can work with the Keras API\n",
+ "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
+ "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
+ "input_shape = (28, 28, 1)\n",
+ "# Making sure that the values are float so that we can get decimal points after division\n",
+ "x_train = x_train.astype('float32')\n",
+ "x_test = x_test.astype('float32')\n",
+ "# Normalizing the RGB codes by dividing it to the max RGB value.\n",
+ "x_train /= 255\n",
+ "x_test /= 255\n",
+ "print('x_train shape:', x_train.shape)\n",
+ "print('Number of images in x_train', x_train.shape[0])\n",
+ "print('Number of images in x_test', x_test.shape[0])"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "x_train shape: (60000, 28, 28, 1)\n",
+ "Number of images in x_train 60000\n",
+ "Number of images in x_test 10000\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_VJjHQ0jQQLJ",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "#convert 0-9 labels into \"one-hot\" format \n",
+ "train_labels = keras.utils.to_categorical(x_train, 10)\n",
+ "test_labels = keras.utils.to_categorical(y_train, 10)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cUUz_ALHQblA",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 298
+ },
+ "outputId": "b95fc0de-051a-454d-e7ad-02ad85eb1b4a"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "def display_sample(num):\n",
+ " print(y_train[num])\n",
+ " label = y_train[num].argmax(axis=0)\n",
+ " image = x_train[num].reshape([28,28])\n",
+ " plt.title('sample: %d label: %d' %(num,label))\n",
+ " plt.imshow(image, cmap=plt.get_cmap('gray'))\n",
+ " plt.show()\n",
+ "display_sample(1270)"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "2\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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ZIhx2s0Q47GaJ8COb9wNLliwpbD/88MOrttW6F37ixIl11dSjVm0PPvhg3dPWepz0u+++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\n3SwR/w/BLO21cZDfKAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RN66oIEzQbqQ",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "model = Sequential()\n",
+ "model.add(Conv2D(32, kernel_size=(3,3), activation = 'relu', input_shape=input_shape))\n",
+ "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
+ "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+ "model.add(Dropout(0.2))\n",
+ "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n",
+ "model.add(Dense(128, activation='relu'))\n",
+ "model.add(Dropout(0.5))\n",
+ "model.add(Dense(10,activation='softmax'))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "u_fHpsDiRu9T",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 434
+ },
+ "outputId": "3794bd67-b724-48fd-835a-297b812c9af8"
+ },
+ "source": [
+ "model.summary()"
+ ],
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_4\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_7 (Conv2D) (None, 26, 26, 32) 320 \n",
+ "_________________________________________________________________\n",
+ "conv2d_8 (Conv2D) (None, 24, 24, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_7 (Dropout) (None, 12, 12, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_4 (Flatten) (None, 9216) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_7 (Dense) (None, 128) 1179776 \n",
+ "_________________________________________________________________\n",
+ "dropout_8 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_8 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 1,199,882\n",
+ "Trainable params: 1,199,882\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lfzeAbT1RElh",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 382
+ },
+ "outputId": "1eba83b0-f198-4b17-9a98-2f0c2da5d7f7"
+ },
+ "source": [
+ "model.compile(optimizer='adam', \n",
+ " loss='sparse_categorical_crossentropy', \n",
+ " metrics=['accuracy'])\n",
+ "history = model.fit(x=x_train,y=y_train, batch_size =32, epochs=10,verbose = 2, validation_data=(x_test, y_test))"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Train on 60000 samples, validate on 10000 samples\n",
+ "Epoch 1/10\n",
+ " - 12s - loss: 0.1047 - acc: 0.9688 - val_loss: 0.0405 - val_acc: 0.9873\n",
+ "Epoch 2/10\n",
+ " - 12s - loss: 0.0652 - acc: 0.9800 - val_loss: 0.0334 - val_acc: 0.9894\n",
+ "Epoch 3/10\n",
+ " - 12s - loss: 0.0513 - acc: 0.9844 - val_loss: 0.0374 - val_acc: 0.9884\n",
+ "Epoch 4/10\n",
+ " - 12s - loss: 0.0435 - acc: 0.9865 - val_loss: 0.0409 - val_acc: 0.9875\n",
+ "Epoch 5/10\n",
+ " - 12s - loss: 0.0375 - acc: 0.9887 - val_loss: 0.0330 - val_acc: 0.9900\n",
+ "Epoch 6/10\n",
+ " - 12s - loss: 0.0328 - acc: 0.9898 - val_loss: 0.0323 - val_acc: 0.9911\n",
+ "Epoch 7/10\n",
+ " - 12s - loss: 0.0281 - acc: 0.9916 - val_loss: 0.0281 - val_acc: 0.9920\n",
+ "Epoch 8/10\n",
+ " - 12s - loss: 0.0249 - acc: 0.9918 - val_loss: 0.0322 - val_acc: 0.9914\n",
+ "Epoch 9/10\n",
+ " - 12s - loss: 0.0249 - acc: 0.9923 - val_loss: 0.0384 - val_acc: 0.9907\n",
+ "Epoch 10/10\n",
+ " - 12s - loss: 0.0220 - acc: 0.9923 - val_loss: 0.0313 - val_acc: 0.9918\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lfIkrsbIUfiE",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86
+ },
+ "outputId": "725df237-8175-4bd3-ae3f-c862bf9b856c"
+ },
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "print(\"Loss: %.2f\"%(score[0]*100))\n",
+ "print(\"Accuracy: %.2f\"%(score[1]*100))\n",
+ "print(\"Training loss: %.2f\"%(history.history['loss'][9]*100))\n",
+ "print(\"Validation loss: %.2f\"%(history.history['val_loss'][9]*100))"
+ ],
+ "execution_count": 26,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Loss: 3.13\n",
+ "Accuracy: 99.18\n",
+ "Training loss: 2.20\n",
+ "Validation loss: 3.13\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Nr1zHdyCUglM",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 545
+ },
+ "outputId": "17e1e8b8-a7a7-4a1f-f4cd-4af1ec1b9b78"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "loss = history.history['loss']\n",
+ "val_loss = history.history['val_loss']\n",
+ "epochs = range(1,len(loss)+1)\n",
+ "plt.title(\"Training and Validation loss\")\n",
+ "plt.plot(epochs,loss,'y',label='Training Loss')\n",
+ "plt.plot(epochs,val_loss,'r',label='Validation Loss')\n",
+ "plt.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "acc = history.history['acc']\n",
+ "val_acc = history.history['val_acc']\n",
+ "epochs = range(1,len(loss)+1)\n",
+ "plt.title(\"Training and Validation Accuracy\")\n",
+ "plt.plot(epochs,acc,'y',label='Training Accuracy')\n",
+ "plt.plot(epochs,val_acc,'r',label='Validation Accuracy')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ],
+ "execution_count": 27,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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wKtXJePI/OgE44PY5zZ7mbiMwy34/E4gUkR7GmK+xEsJB+/WpMWa723oL7Oqf\ne6SR00oR+b2IrBORddnZ2R6Eq45Wfv5KfvzxJDZvPhuH4zCDB7/Or361mbi42Vr4K9UJeet/9a3A\nRBH5EauKJx1wishAYAiQiJU0pojIKfY6lxhjRgCn2K+5DW3YGPOiMWasMWZsz549vRSucldY+BUb\nNkxh48apVFQc4NhjX2DcuJ/o1WsuVg2gUqoz8qQKKB1wv50z0Z5WwxiTgX0FICIRwHnGmAIR+R3w\njTGm2J63DBgPrDXGpNvrFonIm1hVTa+38PuoZigq+oF9++4hL+9jgoPjGDjwKXr3/oM+dF0pP+HJ\nFcD3wCARSRaREGAO8KH7AiISK7V1BHcCr9jv92NdGQSJSDDW1cF2+3OsvW4wcDawpeVfR3mipGQb\nW7bMZv364zl8+GuSkx/hxBP3kph4sxb+SvmRI14BGGMcInID8CkQCLxijNkqIg8A64wxHwKTgEdE\nxABrgOvt1d8BpgCbsRqEPzHG/EdEugKf2oV/ILAc+Jd3v5pqSF7eZ2zefDYBAaH0738viYl/JDg4\nytdhKaV8QIwxvo7BY2PHjjXr1mmv0aN1+PC3bNgwlS5dBjBq1OeEhOitF0r5AxFZb4wZW3+6du3w\nEyUl29m06SxCQuIZOfITLfyVUpoA/EF5+QE2bToDkSBGjfqM0NDevg5JKdUO6FhAnVxVVS6bNp2B\nw1FISspqunQZ4OuQlFLthCaATszhKGbTprMoK9vLyJGfEBk52tchKaXaEU0AnZTLVcnWrbMpKvqe\nYcPeJTp6kq9DUkq1M5oAOiFjXPz00xXk53/Kcce9RM+e5/o6JKVUO6SNwJ2MMYbdu+eRlbWY5ORH\n6N37t74OSSnVTmkC6GR+/vlh0tPnk5j4R/r1u93X4Sil2jFNAJ1IRsYLpKbeQ3z8XAYMeFzH7VdK\nNUkTQCeRlfUOO3deS0zMmRx33Ms6fLNS6oi0lOgE8vNXsn37JXTrNp5hw94mICDY1yEppToATQAd\nXFHRerZsOYcuXQYxYsR/CAwM93VISqkOQhNAB1ZaupNNm35NUFAPRo36lODgGF+HpJTqQDQBdFAV\nFRls3Hg6YOzxfeo/pVMppZqmN4J1QFVV+fb4PrmkpKwmPPxYX4eklOqANAF0ME5nKZs3/4bS0p2M\nHLmMyMjjfR2SUqqD0gTQgbhcVWzdegGHD3/F0KFLiY6e4uuQlFIdmCaADsIYFzt2/Ja8vI8YNOg5\n4uJm+zokpVQHp43AHYAxhj17biMz8w2Skh4gIeEaX4eklOoENAF0AAcO/IO0tCdISLiR/v3v9nU4\nSqlOQhNAO3fw4Cvs3Xs7ceNSlP0AAByISURBVHEXMXDgUzq+j1LKazQBtGPZ2e+zY8fviI4+g8GD\nX9XxfZRSXqUlSjtVULCGbdvmEBn5K4YPf5eAgBBfh6SU6mQ0AbRDxcUb2bz5N3TpcgwjR35EYGBX\nX4eklOqENAG0M2Vle9m48QyCgroxcuSnBAf38HVISqlOSu8DaEcqKg6xceM0jHEwcuQqwsL6+jok\npVQnpgmgnXA4Ctm0aTqVlZmkpKyka9chvg5JKdXJaQJoB5zOcjZvnkFp6TZGjPgv3bqN83VISik/\noAnAx1wuB9u3X0Rh4VqGDFlETMzpvg5JKeUnNAH4kDGGnTuvISfnfQYOnE98/EW+Dkkp5Ue0F5AP\n7dv3Fw4depn+/e8lMfEGX4ejlPIzmgB85MCBJ9i//1H69LmGpKS/+jocpZQf0gTgA4cOvcGePX+i\nZ8/ZDBr0jI7vo5TyCY8SgIhMF5EdIrJbRO5oYH5/EVkhIptEZLWIJLrN+7uIbBWR7SLytNilnYgc\nLyKb7W3WTO/scnM/4qefriQqaipDhixEJNDXISml/NQRE4BYJdSzwK+BocBFIjK03mKPA68bY0YC\nDwCP2OtOAE4CRgLDgV8BE+11ngN+BwyyX9Nb+mXau8LC/7F16/lERKQwfPh7BASE+jokpZQf8+QK\nYByw2xiz1xhTCSwBzqm3zFBgpf1+ldt8A4QBIUAoEAxkikhvoJsx5htjjAFeB85t0Tdp51wuB1u3\nXkhoaCIjRy4jKCjS1yEppfycJwkgATjg9jnNnuZuIzDLfj8TiBSRHsaYr7ESwkH79akxZru9ftoR\ntgmAiPxeRNaJyLrs7GwPwm2fCgpWUlmZzjHHPEZISE9fh6OUUl5rBL4VmCgiP2JV8aQDThEZCAwB\nErEK+CkickpzNmyMedEYM9YYM7Znz45bcGZmLiQoKIoePc70dShKKQV4lgDSAfdRyRLtaTWMMRnG\nmFnGmNHAXfa0AqyrgW+MMcXGmGJgGTDeXj+xqW12Jk5nCTk579Gz5/la76+Uajc8SQDfA4NEJFlE\nQoA5wIfuC4hIrNQ+rupO4BX7/X6sK4MgEQnGujrYbow5CBwWkRPt3j+XAR944fu0Szk5H+J0FhMf\nf6mvQ1FKqRpHTADGGAdwA/ApsB1YaozZKiIPiMgMe7FJwA4R2QnEAw/b098B9gCbsdoJNhpj/mPP\nuw54CdhtL7PMK9+oHcrMXEhoaD+6dz/Z16EopVQNsTrhdAxjx44169at83UYzVJZmcVXX/WhX7/b\nOOaYR3wdjlLKD4nIemPM2PrT9U7gVpaVtRRwavWPUqrd0QTQyjIzFxIRkULXrsN8HYpSStWhCaAV\nlZbuoqjoWz37V0q1S5oAWlFm5iJAiIub4+tQlFLqFzQBtBJjDFlZi4iKmkJoaIM3OSullE9pAmgl\nRUXfUVa2W6t/lFLtliaAVpKZuZCAgDB69px15IWVUsoHNAG0AperiqysJfToMYOgoG6+DkcppRqk\nCaAV5Od/TlVVjlb/KKXaNU0ArcAa+bMHMTFn+DoUpZRqlCYAL3M4isjJeZ+4uAsJCAjxdThKKdUo\nTQBelpPzHi5XGfHxl/g6FKWUapImAC/LzFxEWFgy3bqN93UoSinVJE0AXlRRcZD8/OXEx1+K9ZgD\npZRqvzQBeFFW1hLApdU/SqkOQROAF2VmLiQycizh4cf5OhSllDoiTQBeUlKyneLiH7Tvv1Kqw9AE\n4CXWyJ+BOvKnUqrD0ATgBca4yMpaREzMNEJC4n0djlJKeUQTgBcUFn5FeXkqcXHa+KuU6jg0AXhB\nVtYiAgLCiY0919ehKKWUxzQBtJDLVUlW1lvExs4kKCjC1+EopZTHNAG0UF7eMhyOfO39o5TqcDQB\ntFBm5kKCg+OIjj7N16EopVSzaAJoAYejkJyc/xAXN4eAgCBfh6OUUs2iCaAFsrPfxZgKrf5RSnVI\nmgBaIDNzIV26HEtk5Fhfh6KUUs2mCeAolZenUVCwmvj4S3TkT6VUh6QJ4ChlZS0GjI78qZTqsDQB\nHKXMzIV06zaeLl0G+DoUpZQ6KpoAjkJx8SZKSjZp469SqkPTBHAUMjMXIRJEz54X+DoUpZQ6apoA\nmska+fNNYmKmExIS6+twlFLqqHmUAERkuojsEJHdInJHA/P7i8gKEdkkIqtFJNGePllENri9ykXk\nXHveqyKyz21eine/WusoKFhDRUWaVv8opTq8I96+KiKBwLPANCAN+F5EPjTGbHNb7HHgdWPMayIy\nBXgEmGuMWQWk2NuJAXYDn7mtd5sx5h3vfJW2kZm5kMDASHr0+I2vQ1FKqRbx5ApgHLDbGLPXGFMJ\nLAHOqbfMUGCl/X5VA/MBZgPLjDGlRxusrzmd5WRnv01s7CwCA8N9HY5SSrWIJwkgATjg9jnNnuZu\nIzDLfj8TiBSRHvWWmQMsrjftYbva6EkRCW1o5yLyexFZJyLrsrOzPQi39eTlfYTTeVirf5RSnYK3\nGoFvBSaKyI/ARCAdcFbPFJHewAjgU7d17gQGA78CYoDbG9qwMeZFY8xYY8zYnj17einco5OZuZCQ\nkN5ER0/2aRxKKeUNniSAdKCv2+dEe1oNY0yGMWaWMWY0cJc9rcBtkQuA94wxVW7rHDSWCmABVlVT\nu1VVlUdu7kfExV2M1SyilFIdmycJ4HtgkIgki0gIVlXOh+4LiEisiFRv607glXrbuIh61T/2VQFi\nDaRzLrCl+eG3nezstzGmSod+UEp1GkdMAMYYB3ADVvXNdmCpMWariDwgIjPsxSYBO0RkJxAPPFy9\nvogkYV1BfFFv04tEZDOwGYgFHmrRN2llmZmLCA8fSkREh+itqpRSR+TRU0yMMR8DH9ebdq/b+3eA\nBrtzGmNS+WWjMcaYKc0J1JfKylIpLFxLcvLfdORPpVSnoXcCeyAr600A4uMv9nEkSinlPZoAjsAY\nQ2bmQrp3P4WwsP6+DkcppbxGE8ARFBdvoLR0u/b9V0p1OpoAjiAzcyEiIfTseb6vQ1FKKa/SBNAE\nY5xkZb1Jjx5nERwc7etwlFLKqzQBNCE/fyWVlYe0779SqlPSBNCEzMxFBAZ2JybmLF+HopRSXqcJ\noBFOZyk5Oe8SF3c+gYFhvg5HKaW8ThNAI3JyPsTpLNbeP0qpTksTQCMyMxcSGtqX7t1P8XUoSinV\nKjQBNKCyMpv8/E/tkT/1ECmlOict3RqQnb0UYxxa/aOU6tQ0ATQgM3MhXbuOJCJiuK9DUUqpVqMJ\noJ7S0t0cPvyNnv0rpTo9TQD1WCN/CnFxF/k6FKWUalWaANxUj/wZFTWZsLBEX4ejlFKtShOAm6Ki\n7ykr26VDPyil/IImADfWyJ+h9Ox5nq9DUUqpVufRIyH9gctVRVbWEmJjZxAU1N3X4SjVqKqqKtLS\n0igvL/d1KKqdCQsLIzExkeDgYI+W1wRgy89fTlVVtvb+Ue1eWloakZGRJCUl6TOqVQ1jDLm5uaSl\npZGcnOzROloFZMvMXEhQUAw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MmDCBQ4cOMX36dGbMmMHYsWNJSUnh8ccfB+DWW2/lueee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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
From d3a94e784a4c35da87b88f0aae8e76b4160be3af Mon Sep 17 00:00:00 2001
From: tanvibattu <43849158+tanvibattu@users.noreply.github.com>
Date: Mon, 9 Mar 2020 21:18:44 +0000
Subject: [PATCH 2/4] Delete CNN.ipynb
---
CNN-practice/CNN.ipynb | 400 -----------------------------------------
1 file changed, 400 deletions(-)
delete mode 100644 CNN-practice/CNN.ipynb
diff --git a/CNN-practice/CNN.ipynb b/CNN-practice/CNN.ipynb
deleted file mode 100644
index 1514410..0000000
--- a/CNN-practice/CNN.ipynb
+++ /dev/null
@@ -1,400 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "CNN.ipynb",
- "provenance": [],
- "collapsed_sections": [],
- "authorship_tag": "ABX9TyMyW/mQW5eRn80nw8IlTm8T",
- "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": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "VWMog0eH8s3J",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "import tensorflow as tf\n",
- "import keras\n",
- "from keras.models import Sequential\n",
- "from keras.datasets import mnist\n",
- "from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\n",
- "from keras.datasets import mnist\n",
- "from keras.optimizers import RMSprop, adam"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "kQ820FbfPcqT",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "bGO_tNS5PnYd",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
- },
- "outputId": "3dbbf55b-1058-4f4b-da54-e8c93ff996fc"
- },
- "source": [
- "x_train.shape"
- ],
- "execution_count": 17,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "(60000, 28, 28)"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 17
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "owOL3pkCPoK4",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 69
- },
- "outputId": "b1d66ba1-81a5-4b14-a3b3-dacc8ecb1947"
- },
- "source": [
- "#Reshaping the array to 4-dims so that it can work with the Keras API\n",
- "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
- "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
- "input_shape = (28, 28, 1)\n",
- "# Making sure that the values are float so that we can get decimal points after division\n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "# Normalizing the RGB codes by dividing it to the max RGB value.\n",
- "x_train /= 255\n",
- "x_test /= 255\n",
- "print('x_train shape:', x_train.shape)\n",
- "print('Number of images in x_train', x_train.shape[0])\n",
- "print('Number of images in x_test', x_test.shape[0])"
- ],
- "execution_count": 18,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "x_train shape: (60000, 28, 28, 1)\n",
- "Number of images in x_train 60000\n",
- "Number of images in x_test 10000\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "_VJjHQ0jQQLJ",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "#convert 0-9 labels into \"one-hot\" format \n",
- "train_labels = keras.utils.to_categorical(x_train, 10)\n",
- "test_labels = keras.utils.to_categorical(y_train, 10)"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "cUUz_ALHQblA",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 298
- },
- "outputId": "b95fc0de-051a-454d-e7ad-02ad85eb1b4a"
- },
- "source": [
- "import matplotlib.pyplot as plt\n",
- "def display_sample(num):\n",
- " print(y_train[num])\n",
- " label = y_train[num].argmax(axis=0)\n",
- " image = x_train[num].reshape([28,28])\n",
- " plt.title('sample: %d label: %d' %(num,label))\n",
- " plt.imshow(image, cmap=plt.get_cmap('gray'))\n",
- " plt.show()\n",
- "display_sample(1270)"
- ],
- "execution_count": 20,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "2\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "tags": []
- }
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "RN66oIEzQbqQ",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "model = Sequential()\n",
- "model.add(Conv2D(32, kernel_size=(3,3), activation = 'relu', input_shape=input_shape))\n",
- "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
- "model.add(MaxPool2D(pool_size=(2, 2)))\n",
- "model.add(Dropout(0.2))\n",
- "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n",
- "model.add(Dense(128, activation='relu'))\n",
- "model.add(Dropout(0.5))\n",
- "model.add(Dense(10,activation='softmax'))"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "u_fHpsDiRu9T",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 434
- },
- "outputId": "3794bd67-b724-48fd-835a-297b812c9af8"
- },
- "source": [
- "model.summary()"
- ],
- "execution_count": 22,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Model: \"sequential_4\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d_7 (Conv2D) (None, 26, 26, 32) 320 \n",
- "_________________________________________________________________\n",
- "conv2d_8 (Conv2D) (None, 24, 24, 64) 18496 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 64) 0 \n",
- "_________________________________________________________________\n",
- "dropout_7 (Dropout) (None, 12, 12, 64) 0 \n",
- "_________________________________________________________________\n",
- "flatten_4 (Flatten) (None, 9216) 0 \n",
- "_________________________________________________________________\n",
- "dense_7 (Dense) (None, 128) 1179776 \n",
- "_________________________________________________________________\n",
- "dropout_8 (Dropout) (None, 128) 0 \n",
- "_________________________________________________________________\n",
- "dense_8 (Dense) (None, 10) 1290 \n",
- "=================================================================\n",
- "Total params: 1,199,882\n",
- "Trainable params: 1,199,882\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "lfzeAbT1RElh",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 382
- },
- "outputId": "1eba83b0-f198-4b17-9a98-2f0c2da5d7f7"
- },
- "source": [
- "model.compile(optimizer='adam', \n",
- " loss='sparse_categorical_crossentropy', \n",
- " metrics=['accuracy'])\n",
- "history = model.fit(x=x_train,y=y_train, batch_size =32, epochs=10,verbose = 2, validation_data=(x_test, y_test))"
- ],
- "execution_count": 25,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Train on 60000 samples, validate on 10000 samples\n",
- "Epoch 1/10\n",
- " - 12s - loss: 0.1047 - acc: 0.9688 - val_loss: 0.0405 - val_acc: 0.9873\n",
- "Epoch 2/10\n",
- " - 12s - loss: 0.0652 - acc: 0.9800 - val_loss: 0.0334 - val_acc: 0.9894\n",
- "Epoch 3/10\n",
- " - 12s - loss: 0.0513 - acc: 0.9844 - val_loss: 0.0374 - val_acc: 0.9884\n",
- "Epoch 4/10\n",
- " - 12s - loss: 0.0435 - acc: 0.9865 - val_loss: 0.0409 - val_acc: 0.9875\n",
- "Epoch 5/10\n",
- " - 12s - loss: 0.0375 - acc: 0.9887 - val_loss: 0.0330 - val_acc: 0.9900\n",
- "Epoch 6/10\n",
- " - 12s - loss: 0.0328 - acc: 0.9898 - val_loss: 0.0323 - val_acc: 0.9911\n",
- "Epoch 7/10\n",
- " - 12s - loss: 0.0281 - acc: 0.9916 - val_loss: 0.0281 - val_acc: 0.9920\n",
- "Epoch 8/10\n",
- " - 12s - loss: 0.0249 - acc: 0.9918 - val_loss: 0.0322 - val_acc: 0.9914\n",
- "Epoch 9/10\n",
- " - 12s - loss: 0.0249 - acc: 0.9923 - val_loss: 0.0384 - val_acc: 0.9907\n",
- "Epoch 10/10\n",
- " - 12s - loss: 0.0220 - acc: 0.9923 - val_loss: 0.0313 - val_acc: 0.9918\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "lfIkrsbIUfiE",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 86
- },
- "outputId": "725df237-8175-4bd3-ae3f-c862bf9b856c"
- },
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "print(\"Loss: %.2f\"%(score[0]*100))\n",
- "print(\"Accuracy: %.2f\"%(score[1]*100))\n",
- "print(\"Training loss: %.2f\"%(history.history['loss'][9]*100))\n",
- "print(\"Validation loss: %.2f\"%(history.history['val_loss'][9]*100))"
- ],
- "execution_count": 26,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Loss: 3.13\n",
- "Accuracy: 99.18\n",
- "Training loss: 2.20\n",
- "Validation loss: 3.13\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Nr1zHdyCUglM",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 545
- },
- "outputId": "17e1e8b8-a7a7-4a1f-f4cd-4af1ec1b9b78"
- },
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "loss = history.history['loss']\n",
- "val_loss = history.history['val_loss']\n",
- "epochs = range(1,len(loss)+1)\n",
- "plt.title(\"Training and Validation loss\")\n",
- "plt.plot(epochs,loss,'y',label='Training Loss')\n",
- "plt.plot(epochs,val_loss,'r',label='Validation Loss')\n",
- "plt.legend()\n",
- "plt.show()\n",
- "\n",
- "acc = history.history['acc']\n",
- "val_acc = history.history['val_acc']\n",
- "epochs = range(1,len(loss)+1)\n",
- "plt.title(\"Training and Validation Accuracy\")\n",
- "plt.plot(epochs,acc,'y',label='Training Accuracy')\n",
- "plt.plot(epochs,val_acc,'r',label='Validation Accuracy')\n",
- "plt.legend()\n",
- "plt.show()"
- ],
- "execution_count": 27,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "display_data",
- "data": {
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wKtXJePI/OgE44PY5zZ7mbiMwy34/E4gUkR7GmK+xEsJB+/WpMWa723oL7Oqf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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "tags": []
- }
- }
- ]
- }
- ]
-}
\ No newline at end of file
From e81c3b6dc56f8ade076bd0a95e0b4d92ddc5cd9d Mon Sep 17 00:00:00 2001
From: tanvibattu <43849158+tanvibattu@users.noreply.github.com>
Date: Tue, 10 Mar 2020 02:49:53 +0530
Subject: [PATCH 3/4] Created using Colaboratory
---
CNN-practice/CNN-mnist.ipynb | 400 +++++++++++++++++++++++++++++++++++
1 file changed, 400 insertions(+)
create mode 100644 CNN-practice/CNN-mnist.ipynb
diff --git a/CNN-practice/CNN-mnist.ipynb b/CNN-practice/CNN-mnist.ipynb
new file mode 100644
index 0000000..090f7ac
--- /dev/null
+++ b/CNN-practice/CNN-mnist.ipynb
@@ -0,0 +1,400 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "CNN.ipynb",
+ "provenance": [],
+ "collapsed_sections": [],
+ "authorship_tag": "ABX9TyMyW/mQW5eRn80nw8IlTm8T",
+ "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": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VWMog0eH8s3J",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import tensorflow as tf\n",
+ "import keras\n",
+ "from keras.models import Sequential\n",
+ "from keras.datasets import mnist\n",
+ "from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\n",
+ "from keras.datasets import mnist\n",
+ "from keras.optimizers import RMSprop, adam"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kQ820FbfPcqT",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bGO_tNS5PnYd",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "3dbbf55b-1058-4f4b-da54-e8c93ff996fc"
+ },
+ "source": [
+ "x_train.shape"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(60000, 28, 28)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "owOL3pkCPoK4",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 69
+ },
+ "outputId": "b1d66ba1-81a5-4b14-a3b3-dacc8ecb1947"
+ },
+ "source": [
+ "#Reshaping the array to 4-dims so that it can work with the Keras API\n",
+ "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
+ "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
+ "input_shape = (28, 28, 1)\n",
+ "# Making sure that the values are float so that we can get decimal points after division\n",
+ "x_train = x_train.astype('float32')\n",
+ "x_test = x_test.astype('float32')\n",
+ "# Normalizing the RGB codes by dividing it to the max RGB value.\n",
+ "x_train /= 255\n",
+ "x_test /= 255\n",
+ "print('x_train shape:', x_train.shape)\n",
+ "print('Number of images in x_train', x_train.shape[0])\n",
+ "print('Number of images in x_test', x_test.shape[0])"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "x_train shape: (60000, 28, 28, 1)\n",
+ "Number of images in x_train 60000\n",
+ "Number of images in x_test 10000\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_VJjHQ0jQQLJ",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "#convert 0-9 labels into \"one-hot\" format \n",
+ "train_labels = keras.utils.to_categorical(x_train, 10)\n",
+ "test_labels = keras.utils.to_categorical(y_train, 10)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cUUz_ALHQblA",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 298
+ },
+ "outputId": "b95fc0de-051a-454d-e7ad-02ad85eb1b4a"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "def display_sample(num):\n",
+ " print(y_train[num])\n",
+ " label = y_train[num].argmax(axis=0)\n",
+ " image = x_train[num].reshape([28,28])\n",
+ " plt.title('sample: %d label: %d' %(num,label))\n",
+ " plt.imshow(image, cmap=plt.get_cmap('gray'))\n",
+ " plt.show()\n",
+ "display_sample(1270)"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "2\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RN66oIEzQbqQ",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "model = Sequential()\n",
+ "model.add(Conv2D(32, kernel_size=(3,3), activation = 'relu', input_shape=input_shape))\n",
+ "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
+ "model.add(MaxPool2D(pool_size=(2, 2)))\n",
+ "model.add(Dropout(0.2))\n",
+ "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n",
+ "model.add(Dense(128, activation='relu'))\n",
+ "model.add(Dropout(0.5))\n",
+ "model.add(Dense(10,activation='softmax'))"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "u_fHpsDiRu9T",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 434
+ },
+ "outputId": "3794bd67-b724-48fd-835a-297b812c9af8"
+ },
+ "source": [
+ "model.summary()"
+ ],
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_4\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "conv2d_7 (Conv2D) (None, 26, 26, 32) 320 \n",
+ "_________________________________________________________________\n",
+ "conv2d_8 (Conv2D) (None, 24, 24, 64) 18496 \n",
+ "_________________________________________________________________\n",
+ "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_7 (Dropout) (None, 12, 12, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "flatten_4 (Flatten) (None, 9216) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_7 (Dense) (None, 128) 1179776 \n",
+ "_________________________________________________________________\n",
+ "dropout_8 (Dropout) (None, 128) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_8 (Dense) (None, 10) 1290 \n",
+ "=================================================================\n",
+ "Total params: 1,199,882\n",
+ "Trainable params: 1,199,882\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lfzeAbT1RElh",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 382
+ },
+ "outputId": "1eba83b0-f198-4b17-9a98-2f0c2da5d7f7"
+ },
+ "source": [
+ "model.compile(optimizer='adam', \n",
+ " loss='sparse_categorical_crossentropy', \n",
+ " metrics=['accuracy'])\n",
+ "history = model.fit(x=x_train,y=y_train, batch_size =32, epochs=10,verbose = 2, validation_data=(x_test, y_test))"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Train on 60000 samples, validate on 10000 samples\n",
+ "Epoch 1/10\n",
+ " - 12s - loss: 0.1047 - acc: 0.9688 - val_loss: 0.0405 - val_acc: 0.9873\n",
+ "Epoch 2/10\n",
+ " - 12s - loss: 0.0652 - acc: 0.9800 - val_loss: 0.0334 - val_acc: 0.9894\n",
+ "Epoch 3/10\n",
+ " - 12s - loss: 0.0513 - acc: 0.9844 - val_loss: 0.0374 - val_acc: 0.9884\n",
+ "Epoch 4/10\n",
+ " - 12s - loss: 0.0435 - acc: 0.9865 - val_loss: 0.0409 - val_acc: 0.9875\n",
+ "Epoch 5/10\n",
+ " - 12s - loss: 0.0375 - acc: 0.9887 - val_loss: 0.0330 - val_acc: 0.9900\n",
+ "Epoch 6/10\n",
+ " - 12s - loss: 0.0328 - acc: 0.9898 - val_loss: 0.0323 - val_acc: 0.9911\n",
+ "Epoch 7/10\n",
+ " - 12s - loss: 0.0281 - acc: 0.9916 - val_loss: 0.0281 - val_acc: 0.9920\n",
+ "Epoch 8/10\n",
+ " - 12s - loss: 0.0249 - acc: 0.9918 - val_loss: 0.0322 - val_acc: 0.9914\n",
+ "Epoch 9/10\n",
+ " - 12s - loss: 0.0249 - acc: 0.9923 - val_loss: 0.0384 - val_acc: 0.9907\n",
+ "Epoch 10/10\n",
+ " - 12s - loss: 0.0220 - acc: 0.9923 - val_loss: 0.0313 - val_acc: 0.9918\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lfIkrsbIUfiE",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86
+ },
+ "outputId": "725df237-8175-4bd3-ae3f-c862bf9b856c"
+ },
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "print(\"Loss: %.2f\"%(score[0]*100))\n",
+ "print(\"Accuracy: %.2f\"%(score[1]*100))\n",
+ "print(\"Training loss: %.2f\"%(history.history['loss'][9]*100))\n",
+ "print(\"Validation loss: %.2f\"%(history.history['val_loss'][9]*100))"
+ ],
+ "execution_count": 26,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Loss: 3.13\n",
+ "Accuracy: 99.18\n",
+ "Training loss: 2.20\n",
+ "Validation loss: 3.13\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Nr1zHdyCUglM",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 545
+ },
+ "outputId": "17e1e8b8-a7a7-4a1f-f4cd-4af1ec1b9b78"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "loss = history.history['loss']\n",
+ "val_loss = history.history['val_loss']\n",
+ "epochs = range(1,len(loss)+1)\n",
+ "plt.title(\"Training and Validation loss\")\n",
+ "plt.plot(epochs,loss,'y',label='Training Loss')\n",
+ "plt.plot(epochs,val_loss,'r',label='Validation Loss')\n",
+ "plt.legend()\n",
+ "plt.show()\n",
+ "\n",
+ "acc = history.history['acc']\n",
+ "val_acc = history.history['val_acc']\n",
+ "epochs = range(1,len(loss)+1)\n",
+ "plt.title(\"Training and Validation Accuracy\")\n",
+ "plt.plot(epochs,acc,'y',label='Training Accuracy')\n",
+ "plt.plot(epochs,val_acc,'r',label='Validation Accuracy')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ],
+ "execution_count": 27,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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wKtXJePI/OgE44PY5zZ7mbiMwy34/E4gUkR7GmK+xEsJB+/WpMWa723oL7Oqf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\npZGcnOzROloFZMvMXEhQUAw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MmDCBQ4cOMX36dGbMmMHYsWNJSUnh8ccfB+DWW2/lueee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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
From 54cbc7144751b305c5f2f07551ce04f57f8eda8c Mon Sep 17 00:00:00 2001
From: tanvibattu <43849158+tanvibattu@users.noreply.github.com>
Date: Mon, 9 Mar 2020 21:20:12 +0000
Subject: [PATCH 4/4] Delete CNN-mnist.ipynb
---
CNN-practice/CNN-mnist.ipynb | 400 -----------------------------------
1 file changed, 400 deletions(-)
delete mode 100644 CNN-practice/CNN-mnist.ipynb
diff --git a/CNN-practice/CNN-mnist.ipynb b/CNN-practice/CNN-mnist.ipynb
deleted file mode 100644
index 090f7ac..0000000
--- a/CNN-practice/CNN-mnist.ipynb
+++ /dev/null
@@ -1,400 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "CNN.ipynb",
- "provenance": [],
- "collapsed_sections": [],
- "authorship_tag": "ABX9TyMyW/mQW5eRn80nw8IlTm8T",
- "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": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "VWMog0eH8s3J",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "import tensorflow as tf\n",
- "import keras\n",
- "from keras.models import Sequential\n",
- "from keras.datasets import mnist\n",
- "from keras.layers import Dense, Dropout, Conv2D, MaxPool2D, Flatten\n",
- "from keras.datasets import mnist\n",
- "from keras.optimizers import RMSprop, adam"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "kQ820FbfPcqT",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "bGO_tNS5PnYd",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
- },
- "outputId": "3dbbf55b-1058-4f4b-da54-e8c93ff996fc"
- },
- "source": [
- "x_train.shape"
- ],
- "execution_count": 17,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "(60000, 28, 28)"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 17
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "owOL3pkCPoK4",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 69
- },
- "outputId": "b1d66ba1-81a5-4b14-a3b3-dacc8ecb1947"
- },
- "source": [
- "#Reshaping the array to 4-dims so that it can work with the Keras API\n",
- "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
- "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
- "input_shape = (28, 28, 1)\n",
- "# Making sure that the values are float so that we can get decimal points after division\n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "# Normalizing the RGB codes by dividing it to the max RGB value.\n",
- "x_train /= 255\n",
- "x_test /= 255\n",
- "print('x_train shape:', x_train.shape)\n",
- "print('Number of images in x_train', x_train.shape[0])\n",
- "print('Number of images in x_test', x_test.shape[0])"
- ],
- "execution_count": 18,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "x_train shape: (60000, 28, 28, 1)\n",
- "Number of images in x_train 60000\n",
- "Number of images in x_test 10000\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "_VJjHQ0jQQLJ",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "#convert 0-9 labels into \"one-hot\" format \n",
- "train_labels = keras.utils.to_categorical(x_train, 10)\n",
- "test_labels = keras.utils.to_categorical(y_train, 10)"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "cUUz_ALHQblA",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 298
- },
- "outputId": "b95fc0de-051a-454d-e7ad-02ad85eb1b4a"
- },
- "source": [
- "import matplotlib.pyplot as plt\n",
- "def display_sample(num):\n",
- " print(y_train[num])\n",
- " label = y_train[num].argmax(axis=0)\n",
- " image = x_train[num].reshape([28,28])\n",
- " plt.title('sample: %d label: %d' %(num,label))\n",
- " plt.imshow(image, cmap=plt.get_cmap('gray'))\n",
- " plt.show()\n",
- "display_sample(1270)"
- ],
- "execution_count": 20,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "2\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "tags": []
- }
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "RN66oIEzQbqQ",
- "colab_type": "code",
- "colab": {}
- },
- "source": [
- "model = Sequential()\n",
- "model.add(Conv2D(32, kernel_size=(3,3), activation = 'relu', input_shape=input_shape))\n",
- "model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))\n",
- "model.add(MaxPool2D(pool_size=(2, 2)))\n",
- "model.add(Dropout(0.2))\n",
- "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n",
- "model.add(Dense(128, activation='relu'))\n",
- "model.add(Dropout(0.5))\n",
- "model.add(Dense(10,activation='softmax'))"
- ],
- "execution_count": 0,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "u_fHpsDiRu9T",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 434
- },
- "outputId": "3794bd67-b724-48fd-835a-297b812c9af8"
- },
- "source": [
- "model.summary()"
- ],
- "execution_count": 22,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Model: \"sequential_4\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d_7 (Conv2D) (None, 26, 26, 32) 320 \n",
- "_________________________________________________________________\n",
- "conv2d_8 (Conv2D) (None, 24, 24, 64) 18496 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_4 (MaxPooling2 (None, 12, 12, 64) 0 \n",
- "_________________________________________________________________\n",
- "dropout_7 (Dropout) (None, 12, 12, 64) 0 \n",
- "_________________________________________________________________\n",
- "flatten_4 (Flatten) (None, 9216) 0 \n",
- "_________________________________________________________________\n",
- "dense_7 (Dense) (None, 128) 1179776 \n",
- "_________________________________________________________________\n",
- "dropout_8 (Dropout) (None, 128) 0 \n",
- "_________________________________________________________________\n",
- "dense_8 (Dense) (None, 10) 1290 \n",
- "=================================================================\n",
- "Total params: 1,199,882\n",
- "Trainable params: 1,199,882\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "lfzeAbT1RElh",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 382
- },
- "outputId": "1eba83b0-f198-4b17-9a98-2f0c2da5d7f7"
- },
- "source": [
- "model.compile(optimizer='adam', \n",
- " loss='sparse_categorical_crossentropy', \n",
- " metrics=['accuracy'])\n",
- "history = model.fit(x=x_train,y=y_train, batch_size =32, epochs=10,verbose = 2, validation_data=(x_test, y_test))"
- ],
- "execution_count": 25,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Train on 60000 samples, validate on 10000 samples\n",
- "Epoch 1/10\n",
- " - 12s - loss: 0.1047 - acc: 0.9688 - val_loss: 0.0405 - val_acc: 0.9873\n",
- "Epoch 2/10\n",
- " - 12s - loss: 0.0652 - acc: 0.9800 - val_loss: 0.0334 - val_acc: 0.9894\n",
- "Epoch 3/10\n",
- " - 12s - loss: 0.0513 - acc: 0.9844 - val_loss: 0.0374 - val_acc: 0.9884\n",
- "Epoch 4/10\n",
- " - 12s - loss: 0.0435 - acc: 0.9865 - val_loss: 0.0409 - val_acc: 0.9875\n",
- "Epoch 5/10\n",
- " - 12s - loss: 0.0375 - acc: 0.9887 - val_loss: 0.0330 - val_acc: 0.9900\n",
- "Epoch 6/10\n",
- " - 12s - loss: 0.0328 - acc: 0.9898 - val_loss: 0.0323 - val_acc: 0.9911\n",
- "Epoch 7/10\n",
- " - 12s - loss: 0.0281 - acc: 0.9916 - val_loss: 0.0281 - val_acc: 0.9920\n",
- "Epoch 8/10\n",
- " - 12s - loss: 0.0249 - acc: 0.9918 - val_loss: 0.0322 - val_acc: 0.9914\n",
- "Epoch 9/10\n",
- " - 12s - loss: 0.0249 - acc: 0.9923 - val_loss: 0.0384 - val_acc: 0.9907\n",
- "Epoch 10/10\n",
- " - 12s - loss: 0.0220 - acc: 0.9923 - val_loss: 0.0313 - val_acc: 0.9918\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "lfIkrsbIUfiE",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 86
- },
- "outputId": "725df237-8175-4bd3-ae3f-c862bf9b856c"
- },
- "source": [
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "print(\"Loss: %.2f\"%(score[0]*100))\n",
- "print(\"Accuracy: %.2f\"%(score[1]*100))\n",
- "print(\"Training loss: %.2f\"%(history.history['loss'][9]*100))\n",
- "print(\"Validation loss: %.2f\"%(history.history['val_loss'][9]*100))"
- ],
- "execution_count": 26,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Loss: 3.13\n",
- "Accuracy: 99.18\n",
- "Training loss: 2.20\n",
- "Validation loss: 3.13\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Nr1zHdyCUglM",
- "colab_type": "code",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 545
- },
- "outputId": "17e1e8b8-a7a7-4a1f-f4cd-4af1ec1b9b78"
- },
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "loss = history.history['loss']\n",
- "val_loss = history.history['val_loss']\n",
- "epochs = range(1,len(loss)+1)\n",
- "plt.title(\"Training and Validation loss\")\n",
- "plt.plot(epochs,loss,'y',label='Training Loss')\n",
- "plt.plot(epochs,val_loss,'r',label='Validation Loss')\n",
- "plt.legend()\n",
- "plt.show()\n",
- "\n",
- "acc = history.history['acc']\n",
- "val_acc = history.history['val_acc']\n",
- "epochs = range(1,len(loss)+1)\n",
- "plt.title(\"Training and Validation Accuracy\")\n",
- "plt.plot(epochs,acc,'y',label='Training Accuracy')\n",
- "plt.plot(epochs,val_acc,'r',label='Validation Accuracy')\n",
- "plt.legend()\n",
- "plt.show()"
- ],
- "execution_count": 27,
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "tags": []
- }
- },
- {
- "output_type": "display_data",
- "data": {
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wKtXJePI/OgE44PY5zZ7mbiMwy34/E4gUkR7GmK+xEsJB+/WpMWa723oL7Oqf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\npZGcnOzROloFZMvMXEhQUAw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\ No newline at end of file