Conclusion:

  1. Mask 是创造了一个 mask 矩阵，随着每一层的结果 tensor 一起逐层传递，如果之后某一层不能接受 mask 矩阵则会报错
  2. Embedding， mask_zero 有效
  3. Concatenate， Dense 层之前可以有 Masking 层， 虽然从 tensor output 输出来看似乎 mask 矩阵没有作用，但是相应 mask 矩阵会继续向下传递，影响后边的层
  4. Mask 主要作用于 RNN 层，会忽略掉相应的 timestep，在 tensor output 的表现为：被 mask 的 timestep 结果为 0 或者与之前时间步结果相同
  5. Concatenate 之前如果 一个输入矩阵的某个 timestep 被 mask 了，整个输出矩阵的那个 timestep 都会被 mask
  6. 不要重复调用 Masking 层，因为会重新定义 mask 矩阵。尤其是在 Embedding 层后 mask 的 timestep 并不为 0，会使 mask_value 不全部匹配

Experimental：

模型部分代码 （用无序编号代替缩进）：

def rnn_model(x_train, y_train):　　

# Inputs

num = Input(shape=(x_train[0].shape[1], x_train[0].shape[2]))

version = Input(shape=(x_train[1].shape[1], x_train[1].shape[2]))

missing = Input(shape=(x_train[2].shape[1], x_train[2].shape[2]))

inputs = [num, version, missing]

# Embedding for categorical variables

reshape_version = Reshape(target_shape=(-1,))(version)

embedding_version = Embedding(180, 2, input_length=x_train[1].shape[1] * x_train[1].shape[2], mask_zero=True, name='M_version')(reshape_version)

reshape_missing = Reshape(target_shape=(-1,))(missing)

embedding_missing = Embedding(4, 1, input_length=x_train[1].shape[1] * x_train[1].shape[2], mask_zero=True, name='M_missing')(reshape_missing)

num = Masking(mask_value=0, name='M_num')(num)

# # # concatenate layer

merge_ft = concatenate([num, embedding_version, embedding_missing], axis=-1, name='concate')

# GRU with various length

'''

Do not use anymore mask layer, as a new layer will overwrite the mask tensor.

As long as part of the timestep is masked, then the whole timestep is masked and won't be calculated

'''

# merge_ft = Dense(3, name='test')(merge_ft)

gru_1 = GRU(3, return_sequences=True, name='gru_1')(merge_ft)

gru_2 = GRU(3, return_sequences=True, name='gru_2')(gru_1)

gru_3 = GRU(3, name='gru_3')(gru_2)

dense_ft = Dense(2, name='dense_ft')(gru_3)

outputs = Lambda(lambda x: K.tf.nn.softmax(x), name='outputs')(dense_ft)

model = Model(inputs=inputs, outputs=outputs)

adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=1e-6)

model.compile(loss='categorical_crossentropy', optimizer=adam)

return model

测试部分代码
if __name__ == '__main__':

# for test mask

# fake num with size 1*5*3

num = [[[0,0,0],[1,2,3],[0,0,0],[1,2,3],[0,0,0]]]

num = np.array(num)

c1 = [[[0],[1],[0],[1],[0]]]

c1 = np.array(c1)

c2 = [[[0],[1],[0],[1],[0]]]

c2 = np.array(c2)

y = [[0, 1]]

y = np.array(y)

x = [num, c1, c2]

model = rnn_model(x, y)

layer_name = 'gru_1'

intermediate_model = Model(inputs = model.input, outputs = model.get_layer(layer_name).output)

print intermediate_model.predict(x)