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March 11, 2018 09:21
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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,268 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] } ], "source": [ "from keras.layers import Conv2D, Activation, MaxPool2D, Flatten, Dense\n", "from keras.models import Sequential\n", "\n", "# Images fed into this model are 512 x 512 pixels with 3 channels\n", "img_shape = (28,28,1)\n", "\n", "# Set up model\n", "model = Sequential()\n", "\n", "# Add convolutional layer with 3, 3 by 3 filters and a stride size of 1\n", "# Set padding so that input size equals output size\n", "model.add(Conv2D(6,2,input_shape=img_shape))\n", "# Add relu activation to the layer \n", "model.add(Activation('relu'))\n", "\n", "model.add(MaxPool2D(2))\n", "\n", "model.add(Flatten())\n", "\n", "model.add(Dense(10))\n", "\n", "model.add(Activation('softmax'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "conv2d_1 (Conv2D) (None, 27, 27, 6) 30 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 27, 27, 6) 0 \n", "_________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 6) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 1014) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 10) 10150 \n", "_________________________________________________________________\n", "activation_2 (Activation) (None, 10) 0 \n", "=================================================================\n", "Total params: 10,180\n", "Trainable params: 10,180\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model.compile(loss='sparse_categorical_crossentropy', optimizer = 'adam', metrics=['acc'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from keras.datasets import mnist" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "(x_train, y_train), (x_test, y_test) = mnist.load_data()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60000, 28, 28)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train.shape" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "x_train = np.expand_dims(x_train,-1)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "x_test = np.expand_dims(x_test,-1)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60000, 28, 28, 1)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train.shape" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60000,)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.shape" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/10\n", "60000/60000 [==============================] - 10s 175us/step - loss: 4.0330 - acc: 0.7424 - val_loss: 3.5352 - val_acc: 0.7746\n", "Epoch 2/10\n", "60000/60000 [==============================] - 10s 169us/step - loss: 3.5208 - acc: 0.7746 - val_loss: 3.4403 - val_acc: 0.7794\n", "Epoch 3/10\n", "60000/60000 [==============================] - 11s 176us/step - loss: 2.4443 - acc: 0.8372 - val_loss: 1.9846 - val_acc: 0.8645\n", "Epoch 4/10\n", "60000/60000 [==============================] - 10s 173us/step - loss: 1.8943 - acc: 0.8691 - val_loss: 1.8478 - val_acc: 0.8713\n", "Epoch 5/10\n", "60000/60000 [==============================] - 10s 174us/step - loss: 1.7726 - acc: 0.8735 - val_loss: 1.7595 - val_acc: 0.8718\n", "Epoch 6/10\n", "60000/60000 [==============================] - 10s 174us/step - loss: 1.6943 - acc: 0.8765 - val_loss: 1.7150 - val_acc: 0.8745\n", "Epoch 7/10\n", "60000/60000 [==============================] - 10s 173us/step - loss: 1.6765 - acc: 0.8777 - val_loss: 1.7268 - val_acc: 0.8688\n", "Epoch 8/10\n", "60000/60000 [==============================] - 10s 173us/step - loss: 1.6676 - acc: 0.8799 - val_loss: 1.7110 - val_acc: 0.8749\n", "Epoch 9/10\n", "60000/60000 [==============================] - 10s 172us/step - loss: 1.4759 - acc: 0.8888 - val_loss: 0.1346 - val_acc: 0.9597\n", "Epoch 10/10\n", "60000/60000 [==============================] - 11s 177us/step - loss: 0.1026 - acc: 0.9681 - val_loss: 0.1144 - val_acc: 0.9693\n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x124eec2b0>" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(x_train,y_train,batch_size=32,epochs=10,validation_data=(x_test,y_test))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }