安裝Keras

pip install keras

代碼

'''Trains a simple convnet on the MNIST dataset.Gets to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a GRID K520 GPU. '''from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as Kbatch_size = 128 num_classes = 10 epochs = 12# input image dimensions img_rows, img_cols = 28, 28# the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data()if K.image_data_format() == 'channels_first':x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)input_shape = (1, img_rows, img_cols) else:x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)input_shape = (img_rows, img_cols, 1)x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples')# convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes)model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax'))model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])

總結

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