学习教材:
动手学深度学习 PYTORCH 版(DEMO)
(https://github.com/ShusenTang/Dive-into-DL-PyTorch)
PDF 制作by [Marcus Yang](https://github.com/chenyang1999)
直接代码:
import torch
from torch import nn
from torch.nn import init
import random
from IPython import display
from matplotlib import pyplot as plt
import torchvision
def load_data_fashion_mnist(batch_size, resize=None,root='./Datasets/FashionMNIST'):
"""
Download the fashion mnist dataset and then load into memory.
"""
trans = []
if resize:
trans.append(torchvision.transforms.Resize(size=resize))
trans.append(torchvision.transforms.ToTensor())
transform = torchvision.transforms.Compose(trans)
mnist_train = torchvision.datasets.FashionMNIST(root=root,
train=True, download=True, transform=transform)
mnist_test = torchvision.datasets.FashionMNIST(root=root,
train=False, download=True, transform=transform)
train_iter = torch.utils.data.DataLoader(mnist_train,
batch_size=batch_size, shuffle=True, num_workers=4)
test_iter = torch.utils.data.DataLoader(mnist_test,
batch_size=batch_size, shuffle=False, num_workers=4)
return train_iter, test_iter
# 以评价模型 net 在数据集 data_iter 上的准确率
def evaluate_accuracy(data_iter, net):
acc_sum, n = 0.0, 0
for X, y in data_iter:
acc_sum += (net(X).argmax(dim=1) == y).float().sum().item()
n += y.shape[0]
return acc_sum / n
# 对 x 的形状转换的这个功能⾃定义⼀个 FlattenLayer
class FlattenLayer(nn.Module):
def __init__(self):
super(FlattenLayer, self).__init__()
def forward(self, x): # x shape: (batch, *, *, ...)
return x.view(x.shape[0], -1)
# sgd算法:⾃动求梯度模块计算得来的梯度是⼀个批量样本的梯度和,
# 将它除以批量⼤⼩来得到平均值。
def sgd(params, lr, batch_size):
for param in params:
param.data -= lr * param.grad / batch_size # 注意这⾥更改param时⽤的param.data
#训练模型
def train_ch3(net, train_iter, test_iter, loss, num_epochs,batch_size,params=None, lr=None, optimizer=None):
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
for X, y in train_iter:
y_hat = net(X)
l = loss(y_hat, y).sum()
# 梯度清零
if optimizer is not None:
optimizer.zero_grad()
elif params is not None and params[0].grad is not None:
for param in params:
param.grad.data.zero_()
l.backward()
if optimizer is None:
sgd(params, lr, batch_size)
else:
optimizer.step() # “softmax回归的简洁实现”⼀节将⽤到
train_l_sum += l.item()
train_acc_sum += (y_hat.argmax(dim=1) ==
y).sum().item()
n += y.shape[0]
test_acc = evaluate_accuracy(test_iter, net)
print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
% (epoch + 1, train_l_sum / n, train_acc_sum / n,
test_acc))
'''
多层感知机(MLP)的实现
'''
'''
定义模型
'''
num_inputs, num_outputs, num_hiddens = 784, 10, 256
net = nn.Sequential()
net.add_module('flattenlayer',FlattenLayer())
net.add_module('linear1',nn.Linear(num_inputs,num_hiddens))
net.add_module('relu',nn.ReLU())
net.add_module('linear2',nn.Linear(num_hiddens,num_outputs))
for params in net.parameters():
init.normal_(params,mean=0,std=0.01)
'''
读取数据并训练模型
'''
batch_size = 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
num_epochs = 5
train_ch3(net, train_iter, test_iter, loss, num_epochs,
batch_size, None, None, optimizer)