def forward(self, x): return torch.cat( (torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1)

这是一个 PyTorch 模型的前向传播函数，它接受一个输入张量 x，并返回一个由两个张量拼接而成的张量。第一个张量是输入张量的每行中最大值的张量，第二个张量是输入张量的每行的平均值的张量。具体来说，torch.max(x, 1) 返回每行中的最大值及其索引，然后使用 unsqueeze(1) 将其转换为列向量；torch.mean(x, 1) 返回每行的平均值，并使用 unsqueeze(1) 将其转换为列向量。最后使用 torch.cat 将这两个张量沿着列维度拼接在一起。

相关问题

用tensorflow的layers.Layer模块改写 class SpatialAttention(nn.Module): def init(self): super(SpatialAttention, self).init() self.conv1 = nn.Conv2d(2, 1, 3, padding = 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim = 1, keepdim = True) max_out, _ = torch.max(x, dim = 1, keepdim = True) x = torch.cat([avg_out, max_out], dim = 1) x = self.conv1(x) return self.sigmoid(x)

以下是使用TensorFlow的layers.Layer模块改写的代码：

import tensorflow as tf

class SpatialAttention(tf.keras.layers.Layer):
    def __init__(self):
        super(SpatialAttention, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(1, 3, padding='same', use_bias=False)
        self.sigmoid = tf.keras.layers.Activation('sigmoid')

    def call(self, inputs):
        avg_out = tf.reduce_mean(inputs, axis=1, keepdims=True)
        max_out = tf.reduce_max(inputs, axis=1, keepdims=True)
        x = tf.concat([avg_out, max_out], axis=1)
        x = self.conv1(x)
        return self.sigmoid(x)

在TensorFlow的layers.Layer模块中，我们使用`__init__()`方法来初始化层的参数，使用`call()`方法来定义层的前向传播逻辑。`Conv2D`和`Activation`分别对应PyTorch中的`nn.Conv2d`和`nn.Sigmoid`。`reduce_mean`和`reduce_max`分别对应PyTorch中的`torch.mean`和`torch.max`。`concat`用于在给定维度上连接不同的张量。

import torch import os import torch.nn as nn import torch.optim as optim import numpy as np import random class Net(nn.Module): def init(self): super(Net, self).init() self.conv1 = nn.Conv2d(1, 16, kernel_size=3,stride=1) self.pool = nn.MaxPool2d(kernel_size=2,stride=2) self.conv2 = nn.Conv2d(16, 32, kernel_size=3,stride=1) self.fc1 = nn.Linear(32 * 9 * 9, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 2) def forward(self, x): x = self.pool(nn.functional.relu(self.conv1(x))) x = self.pool(nn.functional.relu(self.conv2(x))) x = x.view(-1, 32 * 9 * 9) x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) folder_path = 'random_matrices2' # 创建空的tensor x = torch.empty((40, 1, 42, 42)) # 遍历文件夹内的文件，将每个矩阵转化为tensor并存储 for j in range(40): for j in range(40): file_name = 'matrix_{}.npy'.format(j) file_path = os.path.join(folder_path, file_name) matrix = np.load(file_path) x[j] = torch.from_numpy(matrix).unsqueeze(0) #y = torch.cat((torch.zeros(20), torch.ones(20))) #y = torch.cat((torch.zeros(20, dtype=torch.long), torch.ones(20, dtype=torch.long))) y = torch.cat((torch.zeros(20, dtype=torch.long), torch.ones(20, dtype=torch.long)), dim=0) for epoch in range(10): running_loss = 0.0 for i in range(40): inputs = x[i] labels = y[i] optimizer.zero_grad() outputs = net(inputs) #loss = criterion(outputs, labels) loss = criterion(outputs.unsqueeze(0), labels.unsqueeze(0)) loss.backward() optimizer.step() running_loss += loss.item() print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) print('Finished Training')报错RuntimeError: Expected target size [1, 2], got [1]怎么修改？

可以将 labels.unsqueeze(0) 修改为 labels.unsqueeze(0).long()，即将标签张量转化为 long 类型。修改后的代码如下：

import torch 
import os 
import torch.nn as nn 
import torch.optim as optim 
import numpy as np 
import random 

class Net(nn.Module): 
    def __init__(self): 
        super(Net, self).__init__() 
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3,stride=1) 
        self.pool = nn.MaxPool2d(kernel_size=2,stride=2) 
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3,stride=1) 
        self.fc1 = nn.Linear(32 * 9 * 9, 120) 
        self.fc2 = nn.Linear(120, 84) 
        self.fc3 = nn.Linear(84, 2) 
        
    def forward(self, x): 
        x = self.pool(nn.functional.relu(self.conv1(x))) 
        x = self.pool(nn.functional.relu(self.conv2(x))) 
        x = x.view(-1, 32 * 9 * 9) 
        x = nn.functional.relu(self.fc1(x)) 
        x = nn.functional.relu(self.fc2(x)) 
        x = self.fc3(x) 
        return x 

net = Net() 
criterion = nn.CrossEntropyLoss() 
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) 
folder_path = 'random_matrices2' 

# 创建空的tensor 
x = torch.empty((40, 1, 42, 42)) 

# 遍历文件夹内的文件，将每个矩阵转化为tensor并存储 
for j in range(40): 
    file_name = 'matrix_{}.npy'.format(j) 
    file_path = os.path.join(folder_path, file_name) 
    matrix = np.load(file_path) 
    x[j] = torch.from_numpy(matrix).unsqueeze(0) 

y = torch.cat((torch.zeros(20, dtype=torch.long), torch.ones(20, dtype=torch.long)), dim=0) 

for epoch in range(10): 
    running_loss = 0.0 
    for i in range(40): 
        inputs = x[i] 
        labels = y[i] 
        optimizer.zero_grad() 
        outputs = net(inputs) 
        loss = criterion(outputs.unsqueeze(0), labels.unsqueeze(0).long()) 
        loss.backward() 
        optimizer.step() 
        running_loss += loss.item() 
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / 40)) 

print('Finished Training')