参考：SimpleRNN层
1. 语法
keras.layers.GRU(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', 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, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
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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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. 相关说明
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SimpleRNNtakes inputs of shape(batch_size, timesteps, input_features). -
Like all recurrent layers in Keras,
SimpleRNNcan be run in two different modes: it can return either the full sequences of successive outputs for each timestep (a 3D tensor of shape(batch_size, timesteps, output_features)), or it can return only the last output for each input sequence (a 2D tensor of shape(batch_size, output_features)). These two modes are controlled by thereturn_sequencesconstructor argument. Let's take a look at an example:
当将 SimpleRNN 叠加的时候，return_sequences 就有用了。
model = Sequential() model.add(Embedding(10000, 32)) model.add(SimpleRNN(32, return_sequences=True)) model.add(SimpleRNN(32, return_sequences=True)) model.add(SimpleRNN(32, return_sequences=True)) model.add(SimpleRNN(32)) # This last layer only returns the last outputs. model.summary()
outputs：
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_3 (Embedding) (None, None, 32) 320000 _________________________________________________________________ simple_rnn_3 (SimpleRNN) (None, None, 32) 2080 _________________________________________________________________ simple_rnn_4 (SimpleRNN) (None, None, 32) 2080 _________________________________________________________________ simple_rnn_5 (SimpleRNN) (None, None, 32) 2080 _________________________________________________________________ simple_rnn_6 (SimpleRNN) (None, 32) 2080 ================================================================= Total params: 328,320 Trainable params: 328,320 Non-trainable params: 0 _________________________________________________________________
4. 具体实现
4.1 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)
4.2 数据训练
from keras.layers import Dense
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(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 [==============================] - 22s - loss: 0.6455 - acc: 0.6210 - val_loss: 0.5293 - val_acc: 0.7758 Epoch 2/10 20000/20000 [==============================] - 20s - loss: 0.4005 - acc: 0.8362 - val_loss: 0.4752 - val_acc: 0.7742 Epoch 3/10 20000/20000 [==============================] - 19s - loss: 0.2739 - acc: 0.8920 - val_loss: 0.4947 - val_acc: 0.8064 Epoch 4/10 20000/20000 [==============================] - 19s - loss: 0.1916 - acc: 0.9290 - val_loss: 0.3783 - val_acc: 0.8460 Epoch 5/10 20000/20000 [==============================] - 19s - loss: 0.1308 - acc: 0.9528 - val_loss: 0.5755 - val_acc: 0.7376 Epoch 6/10 20000/20000 [==============================] - 19s - loss: 0.0924 - acc: 0.9675 - val_loss: 0.5829 - val_acc: 0.7634 Epoch 7/10 20000/20000 [==============================] - 19s - loss: 0.0726 - acc: 0.9768 - val_loss: 0.5541 - val_acc: 0.7932 Epoch 8/10 20000/20000 [==============================] - 19s - loss: 0.0426 - acc: 0.9862 - val_loss: 0.5551 - val_acc: 0.8292 Epoch 9/10 20000/20000 [==============================] - 20s - loss: 0.0300 - acc: 0.9918 - val_loss: 0.5962 - val_acc: 0.8312 Epoch 10/10 20000/20000 [==============================] - 19s - loss: 0.0256 - acc: 0.9925 - val_loss: 0.6707 - val_acc: 0.8054