def create_model(): model = Sequential() model.add(LSTM(50, input_shape=(train_x.shape[1], train_x.shape[2]))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') model.summary() return model model = create_model()
时间: 2024-04-17 09:29:52 浏览: 51
这是一个创建模型的函数。它使用了Keras库中的Sequential模型,并添加了一个LSTM层和一个Dense层。LSTM层的参数为50,输入形状为(train_x.shape[1], train_x.shape[2]),其中train_x是输入数据的训练集。Dense层的输出为1。模型使用均方误差(mae)作为损失函数,使用Adam优化器进行训练。最后,该函数打印出模型的摘要并返回该模型。
在你的代码中,你调用了create_model()函数来创建一个模型并赋值给变量model。这样你就可以在后续的代码中使用这个模型进行训练或预测。
相关问题
def create_LSTM_model(): # instantiate the model model = Sequential() X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) 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', input_shape=(n_steps, 1, n_length, n_features))) model.add(Flatten()) model.add(RepeatVector(n_outputs)) model.add(MaxPooling1D()) # 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UnboundLocalError Traceback (most recent call last) <ipython-input-21-b28a080066f3> in <module> 46 return model 47 # lstm network ---> 48 model = create_LSTM_model() 49 # summary 50 print(model.summary()) <ipython-input-21-b28a080066f3> in create_LSTM_model() 12 # instantiate the model 13 model = Sequential() ---> 14 X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) 15 model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) 16 UnboundLocalError: local variable 'X_train' referenced before assignment问题
这个错误是因为 `X_train` 变量在函数内部被重新定义,但在 `X_train.reshape()` 被调用时,该变量尚未被赋值。解决方法是将 `X_train` 作为参数传递给函数,并在调用函数时将其传递。修改后的代码如下:
```
def create_LSTM_model(X_train):
# instantiate the model
model = Sequential()
X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features))
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', input_shape=(n_steps, 1, n_length, n_features)))
model.add(Flatten())
model.add(RepeatVector(n_outputs))
model.add(MaxPooling1D())
# 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
X_train = ...
model = create_LSTM_model(X_train)
# summary
print(model.summary())
```
def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features)) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features))) model.add(Flatten()) model.add(RepeatVector(n_outputs)) model.add(MaxPooling1D()) # 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UnboundLocalError: local variable 'X_train' referenced before assignment问题
在函数中,我们应该在使用变量 `X_train` 之前先对其进行赋值。因此,我们需要将 `X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features))` 这一行代码的位置提前,放在 `model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))` 的前面即可解决问题。修改后的代码如下:
```
def create_LSTM_model():
# instantiate the model
model = Sequential()
X_train = X_train.reshape((X_train.shape[0], n_steps, 1, n_length, n_features))
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',
input_shape=(n_steps, 1, n_length, n_features)))
model.add(Flatten())
model.add(RepeatVector(n_outputs))
model.add(MaxPooling1D())
# 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())
```