加载keras模块 from keras.models import Sequential from keras.layers import LSTM, Dense from keras.datasets import mnist from keras.utils import np_utils from keras import initializations

def init_weights(shape, name=None):return initializations.normal(shape, scale=0.01, name=name) 绘制模型，需要加载plot from keras.utils.visualize_util import plot 变量初始化

Hyper parameters

batch_size = 128 nb_epoch = 10

Parameters for MNIST dataset

img_rows, img_cols = 28, 28 nb_classes = 10

Parameters for LSTM network

nb_lstm_outputs = 30 nb_time_steps = img_rows dim_input_vector = img_cols 准备数据

Load MNIST dataset

(X_train, y_train), (X_test, y_test) = mnist.load_data() print('X_train original shape:', X_train.shape) input_shape = (nb_time_steps, dim_input_vector)

X_train = X_train.astype('float32') / 255. X_test = X_test.astype('float32') / 255. Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes)

print('X_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') 建立模型

Build LSTM network

model = Sequential() model.add(LSTM(nb_lstm_outputs, input_shape=input_shape)) model.add(Dense(nb_classes, activation='softmax', init=init_weights)) 打印模型 model.summary() 绘制模型结构图，并保存成图片 plot(model, to_file='lstm_model.png') 编译模型 model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) 迭代训练 history = model.fit(X_train, Y_train, nb_epoch=nb_epoch, batch_size=batch_size, shuffle=True, verbose=1) 模型评估 score = model.evaluate(X_test, Y_test, verbose=1) print('Test score:', score[0]) print('Test accuracy:', score[1])

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