PyTorch 深度学习实践 - CNN

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Convolutional Layer

import torch
in_channels, out_channels = 5, 10
width, height = 100, 100 
kernel_size = 3 
batch_size = 1
input = torch.randn(batch_size, in_channels, width, height)
conv_layer = torch.nn.Conv2d(in_channels, 
                             out_channels, 
                             kernel_size=kernel_size)
output = conv_layer(input)
print(input.shape)
print(output.shape) 
print(conv_layer.weight.shape)

Padding

import torch 
input = [3,4,6,5,7, 
         2,4,6,8,2, 
         1,6,7,8,4, 
         9,7,4,6,2, 
         3,7,5,4,1]
input = torch.Tensor(input).view(1, 1, 5, 5)
conv_layer = torch.nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False)
kernel = torch.Tensor([1,2,3,4,5,6,7,8,9]).view(1, 1, 3, 3)
conv_layer.weight.data = kernel.data
output = conv_layer(input) 
print(output)

Stride

conv_layer = torch.nn.Conv2d(1, 1, kernel_size=3, stride=2, bias=False)

Max Pooling Layer

import torch
input = [3,4,6,5, 
         2,4,6,8, 
         1,6,7,8, 
         9,7,4,6,]
input = torch.Tensor(input).view(1, 1, 4, 4) 
maxpooling_layer = torch.nn.MaxPool2d(kernel_size=2)
output = maxpooling_layer(input) 
print(output)

A Simple Convolutional Neural Network

class Net(torch.nn.Module): 
    def __init__(self): 
        super(Net, self).__init__() 
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5) 
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5) 
        self.pooling = torch.nn.MaxPool2d(2) 
        self.fc = torch.nn.Linear(320, 10)
        
    def forward(self, x):  
        batch_size = x.size(0) 
        x = F.relu(self.pooling(self.conv1(x))) 
        x = F.relu(self.pooling(self.conv2(x))) 
        x = x.view(batch_size, -1)  
        x = self.fc(x) 
        return x
    
model = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device) # Convert parameters and buffers of all modules to CUDA Tensor.

def train(epoch): 
    running_loss = 0.0 
    for batch_idx, data in enumerate(train_loader, 0): 
        inputs, target = data 
        inputs, target = inputs.to(device), target.to(device) # Send the inputs and targets at every step to the GPU.
        optimizer.zero_grad()
        outputs = model(inputs) 
        loss = criterion(outputs, target) 
        loss.backward() optimizer.step()
        running_loss += loss.item() 
        if batch_idx % 300 == 299: 
            print(‘[%d, %5d] loss: %.3f‘ % (epoch + 1, batch_idx + 1, running_loss / 2000)) 
            running_loss = 0.0

def test(): 
    correct = 0 
    total = 0 
    with torch.no_grad(): 
        for data in test_loader: 
            inputs, target = data 
            inputs, target = inputs.to(device), target.to(device) # Send the inputs and targets at every step to the GPU.
            outputs = model(inputs) 
            _, predicted = torch.max(outputs.data, dim=1) 
            total += target.size(0) 
            correct += (predicted == target).sum().item()
	print(‘Accuracy on test set: %d %% [%d/%d]‘ % (100 * correct / total, correct, total))

PyTorch 深度学习实践 - CNN

标签：mic   enum   send   super   _id   rac   forward   ide   layer   

原文地址：https://www.cnblogs.com/wang-haoran/p/13298449.html