参考：LSTM层
1. 语法
keras.layers.recurrent.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0)
2. 参数
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units：输出维度
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activation：激活函数，为预定义的激活函数名（参考激活函数）
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recurrent_activation: 为循环步施加的激活函数（参考激活函数）
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use_bias: 布尔值，是否使用偏置项
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kernel_initializer：权值初始化方法，为预定义初始化方法名的字符串，或用于初始化权重的初始化器。参考initializers
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recurrent_initializer：循环核的初始化方法，为预定义初始化方法名的字符串，或用于初始化权重的初始化器。参考initializers
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bias_initializer：权值初始化方法，为预定义初始化方法名的字符串，或用于初始化权重的初始化器。参考initializers
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kernel_regularizer：施加在权重上的正则项，为Regularizer对象
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bias_regularizer：施加在偏置向量上的正则项，为Regularizer对象
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recurrent_regularizer：施加在循环核上的正则项，为Regularizer对象
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activity_regularizer：施加在输出上的正则项，为Regularizer对象
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kernel_constraints：施加在权重上的约束项，为Constraints对象
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recurrent_constraints：施加在循环核上的约束项，为Constraints对象
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bias_constraints：施加在偏置上的约束项，为Constraints对象
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dropout：0~1之间的浮点数，控制输入线性变换的神经元断开比例
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recurrent_dropout：0~1之间的浮点数，控制循环状态的线性变换的神经元断开比例
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其他参数参考Recurrent的说明
3. 具体实现
3.1 加载数据
from keras.datasets import imdb
from keras.preprocessing import sequence
max_features = 10000 # number of words to consider as features
maxlen = 500 # cut texts after this number of words (among top max_features most common words)
batch_size = 32
print('Loading data...')
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)
print(len(input_train), 'train sequences')
print(len(input_test), 'test sequences')
print('Pad sequences (samples x time)')
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
print('input_train shape:', input_train.shape)
print('input_test shape:', input_test.shape)
output：
Loading data... 25000 train sequences 25000 test sequences Pad sequences (samples x time) input_train shape: (25000, 500) input_test shape: (25000, 500)
3.2 数据训练
from keras.layers import LSTM
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(input_train, y_train,
epochs=10,
batch_size=128,
validation_split=0.2)
outputs：
Train on 20000 samples, validate on 5000 samples Epoch 1/10 20000/20000 [==============================] - 108s - loss: 0.5038 - acc: 0.7574 - val_loss: 0.3853 - val_acc: 0.8346 Epoch 2/10 20000/20000 [==============================] - 108s - loss: 0.2917 - acc: 0.8866 - val_loss: 0.3020 - val_acc: 0.8794 Epoch 3/10 20000/20000 [==============================] - 107s - loss: 0.2305 - acc: 0.9105 - val_loss: 0.3125 - val_acc: 0.8688 Epoch 4/10 20000/20000 [==============================] - 107s - loss: 0.2033 - acc: 0.9261 - val_loss: 0.4013 - val_acc: 0.8574 Epoch 5/10 20000/20000 [==============================] - 107s - loss: 0.1749 - acc: 0.9385 - val_loss: 0.3273 - val_acc: 0.8912 Epoch 6/10 20000/20000 [==============================] - 107s - loss: 0.1543 - acc: 0.9457 - val_loss: 0.3505 - val_acc: 0.8774 Epoch 7/10 20000/20000 [==============================] - 107s - loss: 0.1417 - acc: 0.9493 - val_loss: 0.4485 - val_acc: 0.8396 Epoch 8/10 20000/20000 [==============================] - 106s - loss: 0.1331 - acc: 0.9522 - val_loss: 0.3242 - val_acc: 0.8928 Epoch 9/10 20000/20000 [==============================] - 106s - loss: 0.1147 - acc: 0.9618 - val_loss: 0.4216 - val_acc: 0.8746 Epoch 10/10 20000/20000 [==============================] - 106s - loss: 0.1092 - acc: 0.9628 - val_loss: 0.3972 - val_acc: 0.8758