问修改残差LSTM

EN

def make_residual_lstm_layers(input, rnn_width, rnn_depth, rnn_dropout):
    """
    The intermediate LSTM layers return sequences, while the last returns a single element.
    The input is also a sequence. In order to match the shape of input and output of the LSTM
    to sum them we can do it only for all layers but the last.
    """
    x = input
    for i in range(rnn_depth):
        return_sequences = i < rnn_depth - 1
        x_rnn = LSTM(rnn_width, recurrent_dropout=rnn_dropout, dropout=rnn_dropout, return_sequences=return_sequences)(x)
        if return_sequences:
            # Intermediate layers return sequences, input is also a sequence.
            if i > 0 or input.shape[-1] == rnn_width:
                x = add([x, x_rnn])
            else:
                # Note that the input size and RNN output has to match, due to the sum operation.
                # If we want different rnn_width, we'd have to perform the sum from layer 2 on.
                x = x_rnn
        else:
            # Last layer does not return sequences, just the last element
            # so we select only the last element of the previous output.
            def slice_last(x):
                return x[..., -1, :]

            x = add([Lambda(slice_last)(x), x_rnn])
    return x

input = Input(shape=(32, 10))
output = make_residual_lstm_layers(input, rnn_width=10, rnn_depth=8, rnn_dropout=0.2)
model = Model(inputs=input, outputs=output)
model.summary()

model = Model(inputs=input, outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam')
model.summary()

input = Input(shape=(32, 10))
output = make_residual_lstm_layers(input, rnn_width=10, rnn_depth=8, rnn_dropout=0.2)
newoutput = Dense(1, activation='sigmoid')(output)
model = Model(inputs=input, outputs=newoutput)
model.compile(loss='binary_crossentropy', optimizer='adam')
model.summary()