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Jetbrains全系列IDE稳定放心使用
# Created by Xky at 2019/11/29
import time
import torch
import torchvision
import torch.nn as nn
import sys
import torchvision.transforms as transforms
from torch.utils.data.dataloader import DataLoader
import torch.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#
class FlattenLayer(nn.Module): # 自己定义层Flattenlayer
def __init__(self):
super(FlattenLayer, self).__init__()
def forward(self, x): # x shape: (batch, *, *, ...)
return x.view(x.shape[0], -1)
def batch_norm(is_training, X, gamma, beta, moving_mean, moving_var, eps, momentum):
# 判断当前模式是训练模式还是预测模式
if not is_training:
# 如果是在预测模式下，直接使用传入的移动平均所得的均值和方差
X_hat = (X - moving_mean) / torch.sqrt(moving_var + eps)
else:
assert len(X.shape) in (2, 4)
if len(X.shape) == 2:
# 使用全连接层的情况，计算特征维上的均值和方差
mean = X.mean(dim=0)
var = ((X - mean) ** 2).mean(dim=0)
else:
# 使用二维卷积层的情况，计算通道维上（axis=1）的均值和方差。这里我们需要保持
# X的形状以便后面可以做广播运算
mean = X.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)
var = ((X - mean) ** 2).mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)
# 训练模式下用当前的均值和方差做标准化
X_hat = (X - mean) / torch.sqrt(var + eps)
# 更新移动平均的均值和方差
moving_mean = momentum * moving_mean + (1.0 - momentum) * mean
moving_var = momentum * moving_var + (1.0 - momentum) * var
Y = gamma * X_hat + beta # 拉伸和偏移
return Y, moving_mean, moving_var
class BatchNorm(nn.Module):
def __init__(self, num_features, num_dims):
super(BatchNorm, self).__init__()
if num_dims == 2:
shape = (1, num_features)
else:
shape = (1, num_features, 1, 1)
# 参与求梯度和迭代的拉伸和偏移参数，分别初始化成0和1
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
# 不参与求梯度和迭代的变量，全在内存上初始化成0
self.moving_mean = torch.zeros(shape)
self.moving_var = torch.zeros(shape)
def forward(self, X):
# 如果X不在内存上，将moving_mean和moving_var复制到X所在显存上
if self.moving_mean.device != X.device:
self.moving_mean = self.moving_mean.to(X.device)
self.moving_var = self.moving_var.to(X.device)
# 保存更新过的moving_mean和moving_var, Module实例的traning属性默认为true, 调用.eval()后设成false
Y, self.moving_mean, self.moving_var = batch_norm(self.training,
X, self.gamma, self.beta, self.moving_mean,
self.moving_var, eps=1e-5, momentum=0.9)
return Y
net = nn.Sequential(
nn.Conv2d(1, 6, 5), # in_channels, out_channels, kernel_size
BatchNorm(6, num_dims=4),
nn.Sigmoid(),
nn.MaxPool2d(2, 2), # kernel_size, stride
nn.Conv2d(6, 16, 5),
BatchNorm(16, num_dims=4),
nn.Sigmoid(),
nn.MaxPool2d(2, 2),
FlattenLayer(),
nn.Linear(16*4*4, 120),
BatchNorm(120, num_dims=2),
nn.Sigmoid(),
nn.Linear(120, 84),
BatchNorm(84, num_dims=2),
nn.Sigmoid(),
nn.Linear(84, 10)
)
net = net.to(device)
# 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)
# if sys.platform.startswith('win'):
# num_workers = 0 # 0表示不用额外的进程来加速读取数据
# else:
# num_workers = 4
# train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
# test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)
#
# return train_iter, test_iter
# batch_size = 256
# train_iter, test_iter = load_data_fashion_mnist(batch_size=batch_size)
#get Data
batch_size = 256
#transform = transforms.Compose([transforms.Resize(224), transforms.ToTensor()])
transform = transforms.Compose([transforms.ToTensor()])
train_set = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',
train=True, transform=transform)
test_set = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',
train=False, transform=transform)
train_iter = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0)
test_iter = DataLoader(test_set, batch_size=batch_size, shuffle=True, num_workers=0)
lr, num_epochs = 0.001, 5
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
# evaluate_accuracy
def evaluate_accuracy(test_iterator, net):
with torch.no_grad():
device = list(net.parameters())[0].device
test_acc_sum = 0.0
ncount = 0
for x_test, y_test in test_iterator:
if isinstance(net, torch.nn.Module):
net.eval()
x_test = x_test.to(device)
y_test = y_test.to(device)
y_hat = net(x_test)
test_acc_sum += (y_hat.argmax(dim=1) == y_test).sum().cpu().item()
ncount+=len(y_test)
net.train()
test_acc = test_acc_sum/ncount
return test_acc
def train(num_epoch):
for epoch in range(num_epoch):
l_sum, train_acc_sum, ncount, start = 0.0, 0.0, 0, time.time()
for x_train, y_train in train_iter:
x_train = x_train.to(device)
y_train = y_train.to(device)
y_hat = net(x_train)
l = loss(y_hat, y_train)
optimizer.zero_grad()
l.backward()
optimizer.step()
l_sum += l.cpu().item()
train_acc_sum += (y_hat.argmax(dim=1) == y_train).sum().cpu().item()
ncount += y_train.shape[0]
test_acc = evaluate_accuracy(test_iter, net)
print('epoch: %d, train_loss: %.4f, train_acc: %.4f, test_acc: %.4f , spend_time: %.4f' %
(epoch+1, l_sum/ncount,train_acc_sum/ncount, test_acc,time.time()-start))
if __name__ == "__main__":
train(5)
# train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)
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