On some sequence prediction problems, it can be beneficial to allow the LSTM model to learn the input sequenceboth forward and backwardsand concatenate both interpretations. This is called aBidirectional LSTM. We can implement a Bidirectional LSTM for univariate time series forecasting by wrapping the...
self.model.add(LSTM(neurons, input_shape=(input_timesteps, input_dim), return_sequences=return_seq)) if layer['type'] == 'dropout': self.model.add(Dropout(dropout_rate)) self.model.compile(loss=configs['model']['loss'], optimizer=configs['model']['optimizer']) print('[Model] Model...
model = Sequential() model.add(LSTM(units=72, activation='tanh', input_shape=(look_back, 1))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='Adam', metrics=['mape']) model.fit(x=X_train, y=y_train, epochs=500, batch_size=18, verbose=2) # Make predic...
history = model.train() 使用训练好的模型,我们可以预测值并将其与原始值进行比较。 # Comparing the forecasts with the actual values yhat = [x[0] for x in model.predict(Xval)] y = [y[0] for y in Yval] # Creating the frame to store both predictions days = d[‘dt’].values[-len(...
forecasts[i] = model.predict(X_test[i].reshape(1, look_back, 1)) if i != len(X_test)-1: X_test[i+1,look_back-1] = forecasts[i] for j in range(look_back-1): X_test[i+1,j] = X_test[i,j+1] 结果就变成了这样: ...
classNNMultistepModel():def__init__(self,X,Y,n_outputs,n_lag,n_ft,n_layer,batch,epochs,lr,Xval=None,Yval=None,mask_value=-999.0,min_delta=0.001,patience=5):lstm_input=Input(shape=(n_lag,n_ft))# Series signal lstm_layer=LSTM(n_layer,activation='relu')(lstm_input)x=Dense(n_ou...
# LSTM层数 output_size = 1 # 输出维度（预测目标维度） # 创建LSTM模型实例 model = LSTMModel...
classNNMultistepModel(): def__init__( self, X, Y, n_outputs, n_lag, n_ft, n_layer, batch, epochs, lr, Xval=None, Yval=None, mask_value=-999.0, min_delta=0.001, patience=5 ): lstm_input=Input(shape=(n_lag, n_ft)) #Seriessignallstm_layer=LSTM(n_layer, activation='relu'...
num_units: The number of units in the model's LSTMCell. num_features: The dimensionality of the time series (features per timestep). dtype: The floating point data type to use. """ super(_LSTMModel, self).__init__( # Pre-register the metrics we'll be outputting (just a mean here...
grid_search = GridSearchCV(estimator = grid_model,param_grid = parameters,cv =2) 如果你想为你的模型做更多的超参数调整,也可以添加更多的层。但是如果数据集非常大建议增加 LSTM 模型中的时期和单位。 在第一个 LSTM 层中看到输入形状为 (30,5)。它来自 trainX 形状。