我有以下网络：

name

这是摘要：

type

我有两个问题：

1- softmax层的输入为：num_values_week，并且输出具有相同的暗淡！那么在这一步发生了什么？当输入为2D时，softmax激活会赋予时间步长（第二次暗淡）不同的概率或权重，但是这种情况呢？

2-我想在5 days和inp_features = Input(shape=(segment_number,10),name='features_input') flow_features = Bidirectional(GRU(gru_size,activation='tanh',return_sequences=True,name='LSTM1'))(inp_features) features = Model(inp_features,flow_features) features.summary() sent_input = Input(shape=(segment_number,max_seg_len),dtype='float32',name='input_2') y = Dense(40,name='dense_2')(sent_input) y = concatenate([inp_features,y],axis=2) y = Dense((gru_size*2),name='dense_3')(y) y = activation('softmax')(y) y = keras.layers.dot([y,flow_features],axes=[2,2]) y = Dense(2,activation='softmax',name='final_softmax')(y) model = Model([inp_features,sent_input],y) model.summary() 之间做点积，为什么？我假设先前的softmax层对应用在第3个dim（64）上的时间步长（第2个dim，20）给出了不同的权重。因此，我想使用softmax输出矩阵为Bi-LSTM（Layer (type) Output Shape Param # ================================================================= features_input (InputLayer) (None,20,10) 0 _________________________________________________________________ bidirectional_1 (Bidirection (None,64) 8256 ================================================================= Total params: 8,256 Trainable params: 8,256 Non-trainable params: 0 _________________________________________________________________ __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_2 (InputLayer) (None,400) 0 __________________________________________________________________________________________________ features_input (InputLayer) (None,10) 0 __________________________________________________________________________________________________ dense_2 (Dense) (None,40) 16040 input_2[0][0] __________________________________________________________________________________________________ concatenate_1 (concatenate) (None,50) 0 features_input[0][0] dense_2[0][0] __________________________________________________________________________________________________ dense_3 (Dense) (None,64) 3264 concatenate_1[0][0] __________________________________________________________________________________________________ activation_1 (activation) (None,64) 0 dense_3[0][0] __________________________________________________________________________________________________ bidirectional_1 (Bidirectional) (None,64) 8256 features_input[0][0] __________________________________________________________________________________________________ dot_1 (Dot) (None,20) 0 activation_1[0][0] bidirectional_1[0][0] __________________________________________________________________________________________________ final_softmax (Dense) (None,2) 42 dot_1[0][0] ================================================================================================== Total params: 27,602 Trainable params: 27,602 Non-trainable params: 0 __________________________________________________________________________________________________ ）的输出做“加权平均”。我在两个矩阵上应用了点积（内部积）：

(None,64)

因此，activation_1匹配bidirectional_1暗淡。我做的对吗？