From cde8479661541142dac67a758ea6b49a71e403f9 Mon Sep 17 00:00:00 2001 From: Girraj Jangid <34280160+Girrajjangid@users.noreply.github.com> Date: Wed, 10 Jul 2019 19:34:58 +0530 Subject: [PATCH] keras_task1 --- .../Girrajjangid_Modelwithlogs.ipynb | 2867 +++++++++++++++++ 1 file changed, 2867 insertions(+) create mode 100644 Framework/Keras/Keras Assingment/GirrajJangid_Task1/Girrajjangid_Modelwithlogs.ipynb diff --git a/Framework/Keras/Keras Assingment/GirrajJangid_Task1/Girrajjangid_Modelwithlogs.ipynb b/Framework/Keras/Keras Assingment/GirrajJangid_Task1/Girrajjangid_Modelwithlogs.ipynb new file mode 100644 index 0000000..3cbfbed --- /dev/null +++ b/Framework/Keras/Keras Assingment/GirrajJangid_Task1/Girrajjangid_Modelwithlogs.ipynb @@ -0,0 +1,2867 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from keras.layers import Dense\n", + "from keras.models import Sequential\n", + "from keras.datasets import mnist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocessing " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(60000, 28, 28)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " # data start downloading from amazon instance\n", + "(train_feature , train_label) , (test_feature , test_label ) = mnist.load_data()\n", + "train_feature.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "60,000 images with 28 ** 28 pixel or features total values \n", + "\n", + "Now we align 28 ** 28 into a single row \n", + "\n", + "Each row represent an Image" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(28, 28)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_feature[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(train_feature[0])\n", + "plt.show()\n", + "plt.imshow(train_feature[1])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### check labels of these images" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5, 0], dtype=uint8)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_label[[0,1]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lets check the values of images" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"train_feature = train_feature.reshape(train_feature.shape[0],z)\n", + "test_feature = test_feature.reshape(test_feature.shape[0],z) # image size is same in both data sets\n", + "pd.DataFrame(train_feature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Specification" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Dense(512,activation='relu',input_shape =(train_feature.shape[1], ) ))\n", + "model.add(Dense(10,activation='softmax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## since our target values are in decimal we need to convert them into one-hot encoded form " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.utils import to_categorical" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "train_label = to_categorical(train_label)\n", + "#test_label = to_categorical(test_label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compiling the model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\GirrajJangid\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "Epoch 1/5\n", + "60000/60000 [==============================] - 27s 446us/step - loss: 0.2013 - acc: 0.9406\n", + "Epoch 2/5\n", + "60000/60000 [==============================] - 26s 433us/step - loss: 0.0805 - acc: 0.9756\n", + "Epoch 3/5\n", + "60000/60000 [==============================] - 26s 427us/step - loss: 0.0528 - acc: 0.9836\n", + "Epoch 4/5\n", + "60000/60000 [==============================] - 25s 415us/step - loss: 0.0372 - acc: 0.98830s - loss: 0.037\n", + "Epoch 5/5\n", + "60000/60000 [==============================] - 25s 414us/step - loss: 0.0280 - acc: 0.9911\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(train_feature,train_label,epochs=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7, 2, 1, ..., 4, 5, 6], dtype=int64)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict_classes(test_feature)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 1s 80us/step\n", + "[0.06645988127904712, 0.9804]\n", + "Accuracy : 98.04 %\n" + ] + } + ], + "source": [ + "q = model.evaluate(test_feature,to_categorical(test_label))\n", + "print(q)\n", + "print('Accuracy : ',q[1]*100,'%')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Another way to check accuracy\n", + "pred = model.predict_classes(test_feature)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing Data Accuracy : 98.04 %\n" + ] + } + ], + "source": [ + "c = 0\n", + "for i,j in enumerate(test_label):\n", + " if j==pred[i]:\n", + " c+=1\n", + "print(\"Testing Data Accuracy : \",c/100,'%')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Data Accuracy : 99.335 %\n" + ] + } + ], + "source": [ + "pred = model.predict_classes(train_feature)\n", + "c = 0\n", + "for i,j in enumerate(train_label):\n", + " if j==pred[i]:\n", + " c+=1\n", + "print(\"Training Data Accuracy : \",c/600,'%')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}