model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1))) model.add(LSTM(50, return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1))这段代码中的参数,如50,1等是什么意思
时间: 2023-06-09 15:02:25 浏览: 111
这段代码是用来创建一个基于LSTM的神经网络模型。其中,LSTM代表长短期记忆网络,是一种特殊类型的循环神经网络,用于处理时序数据。
参数50表示LSTM层中包含50个神经元,return_sequences=True表示LSTM层返回一个序列,input_shape=(time_step, 1)表示输入数据是一个维度为(time_step, 1)的二维张量,其中time_step是时间步长,1是特征数。另外,model.add(Dense(1))表示在LSTM层后添加一个全连接层,其中Dense(1)表示该层包含一个输出神经元。
相关问题
model = Sequential() # 顺序模型 # 参数说明: # units: 输出空间的维度 # input_shape: (timestep, input_dim),timestep可以设置为None,由输入决定,input_dime根据具体情况 # activation: 激活函数,默认tanh # return_sequences: 布尔值。是否返回最后的输出。在输出序列或完整序列中。 model.add(LSTM(256, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True)) model.add(Dropout(0.4)) # 抛弃一些参数防止过拟合,X可以取0--1之间,代表百分比抛弃数据 model.add(LSTM(256, return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(128, return_sequences=True)) model.add(LSTM(32, return_sequences=True))
这是一个使用Keras库实现的LSTM模型,该模型包含四个LSTM层和三个Dropout层。其中,LSTM层的输出空间维度为256、256、128和32,输入形状为(timestep, input_dim),其中timestep可以设置为None,根据输入数据自动确定,input_dim则根据具体情况确定。Dropout层用于防止过拟合,第一个Dropout层抛弃了40%的参数,第二个和第三个Dropout层分别抛弃了30%的参数。由于所有LSTM层的return_sequences参数均设置为True,因此该模型会返回完整的输出序列。
def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]*5))) model.add(Reshape((X_train.shape[1], 1,X_train.shape[2], 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())修改代码,解决ValueError: in user code: File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function * return step_function(self, iterator) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step ** outputs = model.train_step(data) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\input_spec.py", line 298, in assert_input_compatibility raise ValueError( ValueError: Input 0 of layer "sequential_71" is incompatible with the layer: expected shape=(None, 10, 5), found shape=(None, 10, 1, 1, 5)
根据报错信息,可以看出是输入数据的shape出现了问题。在模型中,第一层输入的shape是(X_train.shape[1], X_train.shape[2]*5),即(None, 10, 5),但是实际传入的数据的shape为(None, 10, 1, 1, 5),其中多了两维,因此需要修改代码中的Reshape层的参数,将其改为Reshape((X_train.shape[1], 1, X_train.shape[2]*5)),这样就可以将输入的数据reshape成正确的shape了。修改后的代码如下:
```
def create_LSTM_model():
# instantiate the model
model = Sequential()
model.add(Input(shape=(X_train.shape[1], X_train.shape[2]*5)))
model.add(Reshape((X_train.shape[1], 1, X_train.shape[2]*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())
```