def gru_features(self, x, predict=False): x_input = x out = None out_lis = [] out_weight_list = [] if ( self.model_type == 'AdaRNN') else None for i in range(self.num_layers): out, _ = self.features[i](x_input.float()) x_input = out out_lis.append(out) if self.model_type == 'AdaRNN' and predict == False: out_gate = self.process_gate_weight(x_input, i) out_weight_list.append(out_gate) return out, out_lis, out_weight_list

RNN模型的.ipynb_checkpoints文件解析与应用

随着研究的深入和技术的进步，更高级的变体模型，如长短时记忆网络（LSTM）和门控循环单元（GRU）也被开发出来，以解决传统 RNN 在处理长序列时遇到的梯度消失和梯度爆炸的问题。如果用户想要进一步了解和使用 RNN...

Linux/Unix平台下的GRU驱动编程解析

资源摘要信息: "Linux/Unix编程中关于GRUB Tables的实现与分析" 在Linux/Unix编程领域，GRUB（GRand Unified Bootloader）是一个广泛使用的引导加载程序，它允许多个操作系统共存，并允许用户在计算机启动时选择...

这段代码哪里错了 class my_BiGRU(torch.nn.Module): def init(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )

def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout, capsule_out_features): super(my_BiGRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_...

class my_BiGRU(torch.nn.Module): def init(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def forward(self, x): h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device) out, _ = self.gru(x, h0) out = self.fc(out[:, -1, :]) return out

这是一个使用双向GRU和全连接层的神经网络模型。该模型的输入大小为input_size，输出大小为output_size。...在forward函数中，它首先将输入x传递给双向GRU，然后将最后一个时间步的输出传递给全连接层以获取最终输出。

class GRU(nn.Module): def init(self, input_size, num_channels, num_classes, tcn_options, dropout, relu_type, dwpw=False): super(GRU, self).init() self.trunk = nn.GRU(512,1024, 3, batch_first=True, bidirectional=True, dropout=0.2) self.output = nn.Linear(21024, num_classes) def forward(self, x): # x needs to have dimension (N, C, L) in order to be passed into CNN gru,_ = self.trunk(x) if gru.shape[1]>2: gru=gru[:,0:2,:] elif gru.shape[1]<2: k=2-gru.shape[1] temp=gru[:,0:k,:] gru=torch.cat([gru,temp],dim=1) gru=gru.reshape(gru.shape[0],gru.shape[1]gru.shape[2]) out=self.output(gru) return out 修改这段代码

这是一个名为GRU的类，它继承了nn.Module。它的构造函数有6个参数：输入大小、通道数、类别数、tcn选项、dropout、ReLU类型和是否使用dwpw。在构造函数中，定义了一个名为trunk的GRU层，它有512个输入、1024个输出，...

def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

首先，通过self.gru_features对输入张量x进行特征提取，得到一个输出张量out。其中，out[0]保存着最后一个时间步的特征表示，赋值给变量fea。如果模型使用了瓶颈层（self.use_bottleneck=True），则将...

class FuturePrediction(torch.nn.Module): def init(self, in_channels, latent_dim, n_gru_blocks=3, n_res_layers=3): super().init() self.n_gru_blocks = n_gru_blocks # Convolutional recurrent model with z_t as an initial hidden state and inputs the sample # from the probabilistic model. The architecture of the model is: # [Spatial GRU - [Bottleneck] x n_res_layers] x n_gru_blocks self.spatial_grus = [] self.res_blocks = [] for i in range(self.n_gru_blocks): gru_in_channels = latent_dim if i == 0 else in_channels self.spatial_grus.append(SpatialGRU(gru_in_channels, in_channels)) self.res_blocks.append(torch.nn.Sequential([Bottleneck(in_channels) for _ in range(n_res_layers)])) self.spatial_grus = torch.nn.ModuleList(self.spatial_grus) self.res_blocks = torch.nn.ModuleList(self.res_blocks) def forward(self, x, hidden_state): # x has shape (b, n_future, c, h, w), hidden_state (b, c, h, w) for i in range(self.n_gru_blocks): x = self.spatial_grus[i](x, hidden_state, flow=None) b, n_future, c, h, w = x.shape x = self.res_blocks[i](x.view(b n_future, c, h, w)) x = x.view(b, n_future, c, h, w) return x是什么意思？

该模型包括若干个 SpatialGRU 模块和若干个 Bottleneck 模块，可以接受输入 x 和初始隐藏状态 hidden_state，输出预测结果 x。其中，x 的形状为 (b, n_future, c, h, w)，表示批次大小为 b，未来预测数为 n_future，...

class GRUNet(nn.Module): def init(self, input_dim, hidden_dim, output_dim, n_layers, drop_prob=0.2): super(GRUNet, self).init() self.hidden_dim = hidden_dim self.n_layers = n_layers self.gru = nn.GRU(input_dim, hidden_dim, n_layers, batch_first=True, dropout=drop_prob) self.fc = nn.Linear(hidden_dim, output_dim) self.relu = nn.ReLU() def forward(self, x, h): out, h = self.gru(x, h) out = self.fc(self.relu(out[:, -1])) return out, h def init_hidden(self, batch_size): weight = next(self.parameters()).data hidden = weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().to(device) return hidden

在前向传播函数forward()中，输入x和隐藏状态h，通过GRU层self.gru计算输出out和新的隐藏状态h。然后，将out的最后一个时刻的输出通过全连接层self.fc和ReLU激活函数self.relu计算得到最终的输出...

class GRUModel(nn.Module): def init(self, input_size, hidden_size, output_size, num_layers, dropout=0.5): super(GRUModel, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout) self.attention = Attention(hidden_size) self.fc = nn.Linear(hidden_size, output_size) # self.fc1=nn.Linear(hidden_size,256) # self.fc2=nn.Linear(256,1)#这两句是加的 self.dropout = nn.Dropout(dropout) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) out, hidden = self.gru(x, h0) out, attention_weights = self.attention(hidden[-1], out) out = self.dropout(out) out = self.fc(out) return out

在 forward 函数中，首先初始化隐藏状态 h0，然后将输入 x 和隐藏状态传入 GRU 层进行计算，得到输出 out 和最终的隐藏状态 hidden。接下来，将最终的隐藏状态和输出传入 Attention 层进行注意力计算，得到输出 out ...

解释这段话class GRUModel(nn.Module): def init(self, input_size, hidden_size, output_size, num_layers, dropout=0.5): super(GRUModel, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout) self.attention = Attention(hidden_size) self.fc = nn.Linear(hidden_size, output_size) self.fc1=nn.Linear(hidden_size,256) self.fc2=nn.Linear(256,1)#这两句是加的 self.dropout = nn.Dropout(dropout) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size) out, hidden = self.gru(x, h0) out, attention_weights = self.attention(hidden[-1], out) out = self.dropout(out) out = self.fc(out) return out def fit(epoch, model, trainloader, testloader): total = 0 running_loss = 0 train_bar = tqdm(train_dl) # 形成进度条（自己加的） model.train() #告诉模型处于训练状态，dropout层发挥作用 for x, y in trainloader: if torch.cuda.is_available(): x, y = x.to('cuda'), y.to('cuda') y_pred = model(x) #y的预测值 loss = loss_fn(y_pred, y) #计算损失，将预测值与真实值传进去，自动计算 optimizer.zero_grad() #将之前的梯度清零 loss.backward() #根据损失计算梯度，进行一次反向传播。 optimizer.step() #根据梯度进行优化 with torch.no_grad(): total += y.size(0) running_loss += loss.item() #计算所有批次的损失之和 exp_lr_scheduler.step() epoch_loss = running_loss / len(trainloader.dataset) test_total = 0 test_running_loss = 0 model.eval() #告诉模型处于预测状态，dropout层不发挥作用 with torch.no_grad(): for x, y in testloader: if torch.cuda.is_available(): x, y = x.to('cuda'), y.to('cuda') y_pred = model(x) loss = loss_fn(y_pred, y) test_total += y.size(0) test_running_loss += loss.item() epoch_test_loss = test_running_loss / len(testloader.dataset) print('epoch: ', epoch, #迭代次数 'loss： ', round(epoch_loss, 6), #保留小数点3位数 'test_loss： ', round(epoch_test_loss, 4) ) return epoch_loss,epoch_test_loss

首先创建一个全零张量作为初始隐藏状态（h0），然后将输入数据x和初始隐藏状态传入GRU层进行计算，得到输出张量out和最后一个时间步的隐藏状态hidden。接下来，将隐藏状态和输出张量传入注意力机制进行特征提取，...

class GRU(nn.Module): def init(self, feature_size, hidden_size, num_layers, output_size): super(GRU, self).init() self.hidden_size = hidden_size # 隐层大小 self.num_layers = num_layers # gru层数 # feature_size为特征维度，就是每个时间点对应的特征数量，这里为1 self.gru = nn.GRU(feature_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x, hidden=None): batch_size = x.shape[0] # 获取批次大小 # 初始化隐层状态 if hidden is None: h_0 = x.data.new(self.num_layers, batch_size, self.hidden_size).fill_(0).float() else: h_0 = hidden # GRU运算 output, h_0 = self.gru(x, h_0) # 获取GRU输出的维度信息 batch_size, timestep, hidden_size = output.shape # 将output变成 batch_size * timestep, hidden_dim output = output.reshape(-1, hidden_size) # 全连接层 output = self.fc(output) # 形状为batch_size * timestep, 1 # 转换维度，用于输出 output = output.reshape(timestep, batch_size, -1) # 我们只需要返回最后一个时间片的数据即可 return output[-1]解释一下

这段代码定义了一个 GRU 模型，继承自 PyTorch 的 nn.Module。该模型的输入为一个形状为 (batch_size, timestep, feature_size) 的张量 x，其中 batch_size 表示批次大小，timestep 表示时间步数，feature_size 表示...

def forward(self, x, state=None, flow=None, mode='bilinear'): # pylint: disable=unused-argument, arguments-differ # Check size assert len(x.size()) == 5, 'Input tensor must be BxTxCxHxW.' b, timesteps, c, h, w = x.size() assert c == self.input_size, f'feature sizes must match, got input {c} for layer with size {self.input_size}' # recurrent layers rnn_output = [] rnn_state = torch.zeros(b, self.hidden_size, h, w, device=x.device) if state is None else state for t in range(timesteps): x_t = x[:, t] if flow is not None: rnn_state = warp_features(rnn_state, flow[:, t], mode=mode) # propagate rnn state rnn_state = self.gru_cell(x_t, rnn_state) rnn_output.append(rnn_state) # reshape rnn output to batch tensor return torch.stack(rnn_output, dim=1)这段代码的计算过程

这段代码实现了一个带有 GRU 循环神经网络的前向传播过程。输入张量 x 的维度为 BxTxCxHxW，其中 B 是批大小，T 是时间步数，C 是通道数，H 和 W 分别是张量的高度和宽度。该函数首先会检查输入张量的维度是否正确。...

GRU_gru_GRU时间序列_

GRU（Gated Recurrent Unit）是门控循环单元，是一种在深度学习领域，特别是自然语言处理和时间序列预测中广泛使用的递归神经网络（RNN）变体。相较于标准的RNN，GRU克服了长序列训练时的梯度消失问题，使其更适合...

RNN-LSTM-GRU_Tensorflow-Hybrid-Model-Residual-GRU-ResNet-Toderici2017-CVPR:基于端到端学习的纸张高图像压缩比全分辨率有损图像压缩CVPR17

RNN LSTM / GRU-Tensorflow混合模型残差GRU和ResNet-Toderici2017-CVPR 基于端到端学习的纸张高图像压缩率实现全分辨率有损图像压缩CVPR17 这是一个模型，用于使用已训练的残差GRU模型对图像进行压缩和解压缩，如...

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