{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard (Fully Connected) Neural Network" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Use in Markup cell type\n", "#![alt text](imagename.png \"Title\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementing Fully connected Neural Net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading Required packages and Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "###1. Load Data and Splot Data\n", "from keras.datasets import mnist\n", "from keras.models import Sequential \n", "from keras.layers.core import Dense, Activation\n", "from keras.utils import np_utils\n", "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preprocessing" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "n = 10 # how many digits we will display\n", "plt.figure(figsize=(20, 4))\n", "for i in range(n):\n", " # display original\n", " ax = plt.subplot(2, n, i + 1)\n", " plt.imshow(X_test[i].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Previous X_train shape: (60000, 28, 28) \n", "Previous Y_train shape:(60000,)\n", "New X_train shape: (60000, 784) \n", "New Y_train shape:(60000, 10)\n" ] } ], "source": [ "print(\"Previous X_train shape: {} \\nPrevious Y_train shape:{}\".format(X_train.shape, Y_train.shape))\n", "X_train = X_train.reshape(60000, 784) \n", "X_test = X_test.reshape(10000, 784)\n", "X_train = X_train.astype('float32') \n", "X_test = X_test.astype('float32') \n", "X_train /= 255 \n", "X_test /= 255\n", "classes = 10\n", "Y_train = np_utils.to_categorical(Y_train, classes) \n", "Y_test = np_utils.to_categorical(Y_test, classes)\n", "print(\"New X_train shape: {} \\nNew Y_train shape:{}\".format(X_train.shape, Y_train.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setting up parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "input_size = 784\n", "batch_size = 200 \n", "hidden1 = 400\n", "hidden2 = 20\n", "epochs = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Building the FCN Model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_1 (Dense) (None, 400) 314000 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 20) 8020 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 10) 210 \n", "=================================================================\n", "Total params: 322,230\n", "Trainable params: 322,230\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "###4.Build the model\n", "model = Sequential() \n", "model.add(Dense(hidden1, input_dim=input_size, activation='relu'))\n", "# output = relu (dot (W, input) + bias)\n", "model.add(Dense(hidden2, activation='relu'))\n", "model.add(Dense(classes, activation='softmax')) \n", "\n", "# Compilation\n", "model.compile(loss='categorical_crossentropy', \n", " metrics=['accuracy'], optimizer='sgd')\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Training The Model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", " - 12s - loss: 1.4482 - acc: 0.6251\n", "Epoch 2/10\n", " - 3s - loss: 0.6239 - acc: 0.8482\n", "Epoch 3/10\n", " - 3s - loss: 0.4582 - acc: 0.8798\n", "Epoch 4/10\n", " - 3s - loss: 0.3941 - acc: 0.8936\n", "Epoch 5/10\n", " - 3s - loss: 0.3579 - acc: 0.9011\n", "Epoch 6/10\n", " - 4s - loss: 0.3328 - acc: 0.9070\n", "Epoch 7/10\n", " - 3s - loss: 0.3138 - acc: 0.9118\n", "Epoch 8/10\n", " - 3s - loss: 0.2980 - acc: 0.9157\n", "Epoch 9/10\n", " - 3s - loss: 0.2849 - acc: 0.9191\n", "Epoch 10/10\n", " - 3s - loss: 0.2733 - acc: 0.9223\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fitting on Data\n", "model.fit(X_train, Y_train, batch_size=batch_size, epochs=10, verbose=2)\n", "###5.Test " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Testing The Model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000/10000 [==============================] - 1s 121us/step\n", "\n", "Test accuracy: 0.9257\n", "[0 6 9 0 1 5 9 7 3 4]\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "score = model.evaluate(X_test, Y_test, verbose=1)\n", "print('\\n''Test accuracy:', score[1])\n", "mask = range(10,20)\n", "X_valid = X_test[mask]\n", "y_pred = model.predict_classes(X_valid)\n", "print(y_pred)\n", "plt.figure(figsize=(20, 4))\n", "for i in range(n):\n", " # display original\n", " ax = plt.subplot(2, n, i + 1)\n", " plt.imshow(X_valid[i].reshape(28, 28))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }