{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"from keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import Flatten\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.utils import np_utils","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(X_train, y_train), (X_test, y_test) = mnist.load_data()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train.reshape((X_train.shape[0], 28, 28, 1)).astype('float32')\nX_test = X_test.reshape((X_test.shape[0], 28, 28, 1)).astype('float32')\n\nX_train = X_train / 255\nX_test = X_test / 255\n\ny_train = np_utils.to_categorical(y_train)\ny_test = np_utils.to_categorical(y_test)\nnum_classes = y_test.shape[1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(30, (5, 5), input_shape=(28, 28, 1), activation='relu'))\nmodel.add(MaxPooling2D())\nmodel.add(Conv2D(15, (3, 3), activation='relu'))\nmodel.add(MaxPooling2D())\nmodel.add(Dropout(0.2))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"scores = model.evaluate(X_test, y_test, verbose=0)\nprint(\"Large CNN Error: %.2f%%\" % (100-scores[1]*100))","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}