importtensorflowastf# 定义BILSTM模型classBILSTMModel(tf.keras.Model):def__init__(self,units):super(BILSTMModel,self).__init__()self.forward_lstm=tf.keras.layers.LSTM(units,return_sequences=True)self.backward_lstm=tf.keras.layers.LSTM(units,return_sequences=True,go_backwards=True)self.dense=...
A Bidirectional LSTM (BiLSTM) Model is an LSTM network that is a bidirectional RNN network. Context: It can be trained by a Bidirectional LSTM Training System (that implements a BiLSTM training algorithm). It can range from being a Shallow BiLSTM Network to being a Deep BiLSTM Network....
def test(model, dataset, args, data_part="test"): """ :param model: :param args: :param dataset: :param data_part: :return: """ tvt_set = dataset[data_part] tvt_set = yutils.YDataset(tvt_set["xIndexes"], tvt_set["yLabels"], to_pad=True, max_len=args....
importtorchimporttorch.nnasnnclassBiLSTMModel(nn.Module):def__init__(self,input_size,hidden_size,num_layers):super(BiLSTMModel,self).__init__()self.lstm=nn.LSTM(input_size,hidden_size,num_layers,bidirectional=True)defforward(self,x):output,(hn,cn)=self.lstm(x)# HN: hidden state, CN...
# 取出预测的最后一个时间步的输出作为下一步的输入 last_output = model_bilstm.predict(X_test)[-1] # 预测的时间步数 steps = 10 # 假设向后预测10个时间步 predicted = [] for i in range(steps): # 将最后一个输出加入X_test,继续向后预测 input_data = np.append(X_test[-1][1:], last...
optim.Adam(model.parameters(), lr=config.learning_rate) 通过这部分运行代码的结果我们可以看见我们的模型大致内容。 模型参数 然后将数据上传到GPU上,注意在上传时我们应该选择自己的设备,如果设备不支持GPU,就可以直接进行下一步了,如果支持进行这一步骤并选择设备id以提高速度。 代码语言:javascript 复制 # 将...
model.zero_grad() probs = model(sentences_, sentences_seqlen_, sentences_mask_) loss = criterion(probs.view(len(labels_), -1), labels_) loss.backward() optimizer.step() 3.3 模型测试 以下是进行模型测试的代码。 def test(model, dataset, args, data_part="test"): """ :param model:...
Computational Intelligence is among the most influential factors that may help to improve patient oriented and secure decision support model. In this article we present a model of IoT system, which combines BiLSTM deep learning with Decision Tree model and data balancing strategy used to help in ...
最后,取 LSTM 模型输出的最后一个时间步的隐藏状态作为模型输出,即outputs[-1],其维度为[batch_size, n_hidden * 2],然后通过全连接层self.fc进行分类,得到模型的输出model,其维度为[batch_size, n_class],即表示每个类别的得分。 针对 model= BiLSTM()criterion= nn.CrossEntropyLoss()optimizer= optim....
model.predict(MyDataset('test')) 1. 当然,由于用的是随机数,结果不具备评价意义。 4.2 进阶版本 有时,作为输入的特征不止x1,...,x7,还有历史价格。 换言之,模型由: 变成了: 这对于训练过程的影响倒是不大,只需要将create_sequence函数对应的输入特征和输入维度增加,在模型组网时修改输入的维度即可。