# instantiate the model # model = LogisticReg(indim=???, outdim=???)根据注释补全代码
时间: 2024-02-02 11:10:58 浏览: 18
根据注释,代码中的`LogisticReg`是一个类,需要实例化一个对象。在实例化对象时,需要提供两个参数`indim`和`outdim`,分别表示输入维度和输出维度。
具体的代码如下所示:
```
# instantiate the model
model = LogisticReg(indim=???, outdim=???)
```
你需要根据具体的需求,填写`indim`和`outdim`的值。这两个参数的值应该根据你的具体问题来确定,因为它们表示模型的输入和输出的维度。请根据你的实际情况来填写这两个参数的值。
相关问题
def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) 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 "conv_lstm2d_14" is incompatible with the layer: expected ndim=5, found ndim=3. Full shape received: (None, 10, 1)问题
该错误是由于输入数据的维度不匹配导致的。ConvLSTM2D层期望输入的数据维度为5,而当前的输入数据维度为3。因此,需要将输入数据的维度转换为正确的形状。
可以通过在输入层之前添加一个Reshape层来实现这一点,将原始输入从(None,10,1)转换为(None,10,1,1,1)。
修改代码如下:
``` python
def create_LSTM_model():
# instantiate the model
model = Sequential()
model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
# reshape the input to match the expected input shape of the ConvLSTM2D 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(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())
```
这样就可以避免ValueError错误了。
vdef create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1],1,1, 5))) # 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)) model.add(RepeatVector(1)) # 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 "sequential_90" is incompatible with the layer: expected shape=(None, 10, 1, 1, 5), found shape=(None, 10, 5)问题
根据报错信息,可以看到模型期望的输入维度是`(None, 10, 1, 1, 5)`,但实际输入的维度是`(None, 10, 5)`。因此,需要修改输入层的维度。
可以使用`Reshape`层将输入数据的维度从`(None, 10, 5)`转换为`(None, 10, 1, 1, 5)`,代码如下:
```python
def create_LSTM_model():
# instantiate the model
model = Sequential()
model.add(Input(shape=(X_train.shape[1], 5)))
model.add(Reshape((X_train.shape[1], 1, 1, 5))) # 添加Reshape层,将输入数据的维度转换为(10, 1, 1, 5)
# 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))
model.add(RepeatVector(1))
# 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())
```
修改后,卷积层的输入维度为`(None, 10, 1, 1, 5)`,与模型期望的输入维度相符合。