def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) model.add(Reshape((X_train.shape[1], 1, X_train.shape[2], 1))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改该代码,解决ValueError: Input 0 of layer "lstm_22" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 640)问题
时间: 2023-11-29 21:04:21 浏览: 85
CIT_LSTM_TimeSeries:用于电力负荷预测的LSTM模型
根据错误信息,我们可以看出 "lstm_22" 层的输入维度(ndim)期望是3,但是实际上却是2。这个问题通常是由于输入数据形状(shape)不正确导致的。
在这个问题的代码中,我们可以看到在模型的第一层中添加了一个 Reshape 层,将输入数据的形状更改为 (batch_size, time_steps, features, 1, 1)。这是为了使其可以与 ConvLSTM2D 层兼容。
然而,在后续的层中,我们需要将数据形状转换回 (batch_size, time_steps, features)。因此,我们需要在 Reshape 层之后添加一个 Reshape 层,将数据形状从 (batch_size, time_steps, features, 1, 1) 转换为 (batch_size, time_steps, features)。
下面是修改过后的代码:
```python
def create_LSTM_model():
# instantiate the model
model = Sequential()
# add input layer
model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
# add reshape layer
model.add(Reshape((X_train.shape[1], 1, X_train.shape[2], 1)))
# cnn1d Layers
model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True))
model.add(Flatten())
model.add(Dropout(0.5))
# add reshape layer
model.add(Reshape((X_train.shape[1], 64)))
# add lstm layer
model.add(LSTM(64, activation='relu', return_sequences=True))
model.add(Dropout(0.5))
# add attention layer
model.add(LSTM(64, activation='relu', return_sequences=False))
# add dropout layer
model.add(Dropout(0.5))
# add dense layers
model.add(Dense(128))
model.add(Dense(1, name='Output'))
# compile the model
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
return model
# lstm network model
model = create_LSTM_model()
# summary
print(model.summary())
```
在上面的修改后的代码中,我们在 Reshape 层之后添加了一个新的 Reshape 层,将数据形状转换回 (batch_size, time_steps, features)。这样可以确保后续的 LSTM 层能够正确处理输入数据。
另外,我们还将 LSTM 层的 return_sequences 参数设置为 True,以确保输出的形状与输入形状相同。这样可以使得后续的层能够正确地处理输出数据。
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