Keras RNN的两个输入
如果我想将两个网络的输入放入Keras中的RNN中,我该如何实现这一点?例如,假设我有两个RNNKeras RNN的两个输入,keras,Keras,如果我想将两个网络的输入放入Keras中的RNN中,我该如何实现这一点?例如,假设我有两个RNNA和B,它们的输出进入RNNC,需要使用。请参见下面的示例: def build_rnn(x_train, y_train, in_len): epochs = 100 batch_size = 300 hidden_units = 256 vec_dims = 1 in_shape = (in_len, vec_dims) inputs = [In
A
和B
,它们的输出进入RNNC
,需要使用。请参见下面的示例:
def build_rnn(x_train, y_train, in_len):
epochs = 100
batch_size = 300
hidden_units = 256
vec_dims = 1
in_shape = (in_len, vec_dims)
inputs = [Input(shape=in_shape, name="input_a"), Input(shape=in_shape, name="input_b")]
merge_outs = []
for inp in inputs:
# stack a few RNNs
net = SimpleRNN(hidden_units, return_sequences=True)(inp)
merge_outs.append(SimpleRNN(hidden_units, return_sequences=True)(net))
merged = Concatenate(axis=-1)(merge_outs)
merged = SimpleRNN(hidden_units, input_shape=(in_len, 2*vec_dims, ), return_sequences=False,
name="pre_out")(merged)
output = Dense(vec_dims, input_shape=(vec_dims,), name='output')(merged)
model = Model(inputs=inputs, outputs=[output])
return model