image_output_1.png
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=28 * 28))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer=RMSprop(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_images, train_labels, nb_epoch=15, batch_size=64, verbose=0)