dec = RepeatVector(sequence_length)(inp_z) dec = Concatenate()([dec] + cat_emb) dec = LSTM(64, return_sequences=True)(dec)
时间: 2024-05-21 20:11:28 浏览: 14
The code above defines a decoder network in Keras.
The first line uses the `RepeatVector` layer to repeat the input `inp_z` for `sequence_length` times. This is done to match the length of the output sequence.
The second line concatenates the repeated input `inp_z` with the categorical embedding `cat_emb`.
The third line uses the `LSTM` layer with 64 units and `return_sequences=True` to process the concatenated input and generate a sequence of outputs of the same length as the input sequence.
相关问题
batch_size = 64 input_sequence_length = 12 forecast_horizon = 3 multi_horizon = False
这是一个关于机器学习的问题,我可以回答。batch_size = 64 表示每次训练时使用的样本数量为 64,input_sequence_length = 12 表示输入序列的长度为 12,forecast_horizon = 3 表示预测的时间步长为 3,multi_horizon = False 表示只预测一个时间步长。
model.add(LSTM(units=32, input_shape=(sequence_length, 4))) model.add(Dropout(0.2)) model.add(LSTM(units=32, input_shape=(sequence_length, 4))) model.add(Dense(units=1, activation='sigmoid')) 错误在哪
这段代码中存在两个LSTM层,但是第二个LSTM层没有指定输入序列的长度,应该修改为:
```
model.add(LSTM(units=32, input_shape=(sequence_length, 4)))
model.add(Dropout(0.2))
model.add(LSTM(units=32))
model.add(Dense(units=1, activation='sigmoid'))
```
第二个LSTM层的输入序列长度会自动从第一个LSTM层继承。