二、实现一个常用类Flatten类 Flatten就是将2D的特征图压扁为1D的特征向量,用于全连接层的输入。 #Flatten继承ModuleclassFlatten(nn.Module):#构造函数,没有什么要做的def__init__(self):#调用父类构造函数super(Flatten, self).__init__()#实现forward函数defforward(sel
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 = ...
flatten(input, start_dim=0, end_dim=-1) → Tensor # Parameters:input (Tensor) – 输入为Tensor #start_dim (int) – 展平的开始维度 #end_dim (int) – 展平的最后维度 #example #一个3x2x2的三维张量 >>> t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9,...
使用flatten_parameters()把权重存成连续的形式,可以提高内存利用率。 """ self.rnn.flatten_parameters() # print("x shape After CNN", x.shape) # 分类隐藏状态,避免梯度爆炸 out, hn = self.rnn(x, h0.detach()) # out,hn = self.rnn(x) # print("out size", out.shape) """ 输出可以是Y...
FlattenLayer(), nn.Linear(num_inputs, num_hiddens1), nn.ReLU(), nn.Dropout(drop_prob1), nn.Linear(num_hiddens1, num_hiddens2), nn.ReLU(), nn.Dropout(drop_prob2), nn.Linear(num_hiddens2, 10) ) for param in net.parameters(): nn.init.normal_(param, mean=0, std=0.01) ...
closed this ascompletedon Aug 11, 2019 I encountered the same issue here, where in my application I have to flatten all layers' parameters, but after that tons of warnings throw out, how can I get rid of it and make all layer's parameters flattened and lstm layers' parameters pass the...
torch.manual_seed(0)lr = 0.003# model = models.resnet50()# model=model.to(device)vgg16=models.vgg16()vgg_layers_list=list(vgg16.children())[:-1]vgg_layers_list.append(nn.Flatten())vgg_layers_list.append(nn.Linear(25088,4096))vgg_layers_list.append(nn.ReLU())vgg_layers_list....
pytorch中flatten层 目录 基于FGSM/PGD算法的对抗样本的生成 中文数据集 运行环境 实验参数 实验代码 FGSM PGD 如下展示FGSM算法运行结果 结果 补充 代码 【参考】 基于FGSM/PGD算法的对抗样本的生成 在中文文本分类的场景下,以TextCNN(利用卷积神经网络对文本进行分类的算法)为基准模型,通过FGSM算法生成对抗样本进行...
named_parameters(): print(f"Layer: {name} | Size: {param.size()} | Values : {param[:2]} \n") 复制代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 Model structure: NeuralNetwork( (flatten): Flatten(start_dim=1, end_dim=-1) (linear_relu_stack): Sequential( (0): Linear(...
x = self.flatten(x) logits = self.linear_relu_stack(x) returnlogits 然后创建一个实例(对象),把它放到device上 model = NeuralNetwork().to(device) print(model) 跑一下的结果 Using cpu device NeuralNetwork( (flatten): Flatten(start_dim=1, end_dim=-1) ...