深度学习框架pytorch快速开发与实战chapter4

报错

第一个

这个错应该和我前面网络不行断网了有关，我删除了目录下的data文件夹重新跑一次就ok了

第二个

老问题就还是data[]改成item()

前馈神经网络

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
import torch.utils.data as Data
import matplotlib.pyplot as plt
# Hyper Parameters 
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

# MNIST Dataset 
train_dataset = dsets.MNIST(root='./data', 
                            train=True, 
                            transform=transforms.ToTensor(),  
                            download=True)

test_dataset = dsets.MNIST(root='./data', 
                           train=False, 
                           transform=transforms.ToTensor())

# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                          batch_size=batch_size, 
                                          shuffle=False)
test_y=test_dataset.test_labels

# Neural Network Model (1 hidden layer)
class Net(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size) 
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)  
    
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out
    
net = Net(input_size, hidden_size, num_classes)

    
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()  
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)  

# Train the Model
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):  
        # Convert torch tensor to Variable
        images = Variable(images.view(-1, 28*28))
        labels = Variable(labels)
        
        # Forward + Backward + Optimize
        optimizer.zero_grad()  # zero the gradient buffer
        outputs = net(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print ('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' 
                   %(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.item()))

# Test the Model
correct = 0
total = 0
for images, labels in test_loader:
    images = Variable(images.view(-1, 28*28))
    outputs = net(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

# Save the Model
for i in range(1,4):

    plt.imshow(train_dataset.train_data[i].numpy(), cmap='gray')  

    plt.title('%i' % train_dataset.train_labels[i])  

plt.show()  
torch.save(net.state_dict(), 'model.pkl')
test_output = net(images[:20])  

pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()  

print('prediction number',pred_y)  

print('real number',test_y[:20].numpy())  

• torch.max返回最大值和索引
• 要使用torch.optim必须先构造一个Optimizer对象
  （1）必须给他一个包含参数（必须是 Variable对象）进行优化

net.parameters()

（2）可以指定参数选项,也可以直接单独设置

• torchvision
  （1）torchvision.datasets包含数据集（p78）
  （2）torchvision.models包含预训练的模型结构

#加载预训练的
resnet18=models.resnet18(pretrained=True)
#具有随机权重的
resnet18=models.resnet18()

（3）图片转化

自定义ConvNet

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan  1 22:03:51 2018

@author: pc
"""

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class MNISTConvNet(nn.Module):
    def __init__(self):
        super(MNISTConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(10, 20, 5)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
    def forward(self, input):
        x = self.pool1(F.relu(self.conv1(input)))
        x = self.pool2(F.relu(self.conv2(x)))
        return x
net = MNISTConvNet()
print(net)
input = Variable(torch.randn(1, 1, 28, 28))
out = net(input)
print(out.size())

（2） torch.nn

层结构

（1）卷积
（2）池化

函数

位于torch.nn.functional包中（p86）