NUM, num_layers=1) # 16 hidden unitsprint('LSTM model:', lstm_model)print('model.parameters:', lstm_model.parameters)loss_function = nn.MSELoss()optimizer = torch.optim.Adam(lstm_model.parameters(), lr=1e-2)max_epochs = 10000for epoch in range(max_epochs):output = lstm_model(train...
#生成LSTM网络 model = Sequential() model.add(LSTM(4,input_shape =(1,previous))) model.fit(X_train,Y_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) testpred = model.predict(X_test) #将预测转换回正常值 trainpred = scaler.inverse_transfo...
model.add(LSTM(units=number_units, return_sequences=True)) model.add(Dropout(dropout_fraction)) # Layer 3 model.add(LSTM(units=number_units)) model.add(Dropout(dropout_fraction)) # Output layer model.add(Dense(1)) Python 代码片段概述了 LSTM 长短期记忆 RNN 循环神经网络模型的结构。LSTM 模型...
print('inputs:{}, inputs_size:{}'.format(inputs, np.shape(inputs))) print("mdoel:{}".format(model)) #model就是一个输入26维,输出128维的lstm print("mdoel(inputs):{}".format(model(inputs))) predict = model(inputs).data.max(1, keepdim=True)[1] # output = model(input_batch)...
)2、ht为(num_layers*num_directions,batch,hidden_size)格式的tensor,3、Ct为(num_layers*num_directions,batch,hidden_size)格式的tensor,""" #创建LSTM()类的对象,定义损失函数和优化器 model=LSTM()loss_function=nn.MSELoss()optimizer=torch.optim.Adam...
1#预测:2fut_pre = 1234test_inputs = train_data_normalized[-train_window:].tolist()5print(test_inputs)67model.eval()8foriinrange(fut_pre):9seq = torch.FloatTensor(test_inputs[-train_window:])10with torch.no_grad():11model.hidden = (torch.zeros(1,1,model.hidden_layer_size),12to...
#生成LSTM网络 model = Sequential() model.add(LSTM(4,input_shape =(1,previous))) model.fit(X\_train,Y\_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) #将标准化后的数据转换为原始数据 trainpred = scaler.inverse_transform(trainpred) #计算...
model.add(LSTM(4,input_shape =(1,previous))) model.fit(X_train,Y_train,epochs = 100,batch_size = 1,verbose = 2) #生成预测 trainpred = model.predict(X_train) #将标准化后的数据转换为原始数据 trainpred = scaler.inverse_transform(trainpred) ...
com/aialgorithm/Blog # 或“算法进阶”公众号文末阅读原文可见 model = tf.keras.Sequential([ # 不定长度的输入 tf.keras.layers.Input((None,)), # 词嵌入层 tf.keras.layers.Embedding(input_dim=tokenizer.vocab_size, output_dim=128), # 第一个LSTM层,返回序列作为下一层的输入 tf.keras.layers....