vgg16(pretrained=True) # 自建一层,用于提取LSTM层输出中的第一部分tensor class GetLSTMOutput(nn.Module): def forward(self, x): out, _ = x return out #在VGG16特征提取的最后,插入LSTM层,前后分别对特征做拉直和重组 model.features.add_module('31', nn.Flatten(2, 3)) model.features.add_...
lstm.flatten_parameters() outputs, (hidden, cell) = self.lstm(x) predictions = self.fc(hidden[-1]) return predictions # 超参数 input_size = 10 # 特征数量 hidden_size = 50 # LSTM隐藏层大小 num_layers = 2 # LSTM层数 output_size = 3 # 输出大小,对应3种资产的权重 sequence_length = ...
defforward(self, x): self.lstm.flatten_parameters outputs, (hidden, cell) = self.lstm(x) predictions = self.fc(hidden[-1]) returnpredictions # 超参数 input_size =10# 特征数量 hidden_size =50# LSTM隐藏层大小 num_layers =2# LSTM层数 output_size =3# 输出大小,对应3种资产的权重 sequence...
例如:optimizer = torch.optim.SGD(模型对象.parameters(), lr=0.01) # lr学习率 1. #-*-coding:GBK -*- from pickletools import optimize import torchvision import torch from torch import nn from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear from torch.utils.data import DataLoade...
hidden_state = hidden_state.to(device) cell_state = cell_state.to(device) self.hidden = (hidden_state, cell_state) # Forward Pass x, h = self.lstm(x, self.hidden) # LSTM x = self.dropout(x.contiguous().view(x.shape[0], -1)) # Flatten lstm out x = self....
LSTM自动编码器进行时间序列异常检测(Pytorch) 环境准备 本次数据集的格式.arff,需要用到arff2pandas模块读取。 #!nvidia-smi#!pip install -qq arff2pandas#!pip install -q -U watermark 导入相关模块 importtorchimportcopyimportnumpy as npimportpandas as pdimportseaborn as snsfrompylabimportrcParamsimport...
# 使用滑动窗口构造特征,注意这里不进行 flatten, # 使得每个样本的形状为 (window_size, 4),方便 LSTM 按时间步处理 def create_sliding_window_features(df, window_size=7): X, y = [], [] for i in range(window_size, len(df)): X.append(df.iloc[i - window_size:i][["temperature", "...
x = self.dropout(x.contiguous.view(x.shape[0],-1))# Flatten lstm out x = self.fc1(x)# First Dense returnself.dnn(x)# Pass forward through fully connected DNN. 我们设置了2个可以自由地调优的参数n_hidden和n_deep_players。更大的参数意味着模型更复杂和更长的训练时间,所以这里我们可以使用...
使用LSTM执行分类任务 使用RNN执行回归任务# importtorchfromtorchimportnnimportnumpyasnpimportmatplotlib.pyplotasplt# torch.manual_seed(1) # reproducible# Hyper ParametersTIME_STEP =10# rnn time stepINPUT_SIZE =1# rnn input sizeLR =0.02# learning rate# show datasteps = np.linspace(0, np.pi*2,...
self.LSTM.flatten_parameters()y= y.view(-1, hidden_size)y= self.reg1(y)y= y.view(seq_len, batch_size, -1) return y, (hn, cn)# 模型训练 device 设置# NPUuse = TrueNPUuse= FalseifNPUuse:device="npu:0"else:device= torch.device('cpu')iftorch.cuda.is_available(): ...