import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import numpy as np
import torchvision.utils
from torchvision import datasets, transforms
from torch.autograd import Variable
import torch.utils.data
判断是否能用GPU，如果能就用GPU，不能就用CPU
use_gpu = torch.cuda.is_available()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
数据转换，Pytorch的底层是tensor(张量)，所有用来训练的图像均须要转换成tensor
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
下载数据集
data_train = datasets.MNIST(root="./data/", transform=transform, train=True, download=True)
data_test = datasets.MNIST(root="./data/", transform=transform, train=False)
加载数据集，批次大小为64，shuffle示意乱序
data_loader_train = torch.utils.data.DataLoader(dataset=data_train, batch_size=64, shuffle=True)
data_loader_test = torch.utils.data.DataLoader(dataset=data_test, batch_size=64, shuffle=True)
创立模型即网络架构
class Model(nn.Module):
def __init__(self): super(Model, self).__init__() #创立二维卷积 self.conv1 = nn.Sequential( #输出特色数量为1，输入特色数量为64，卷积核大小为3x3，步长为1，边缘填充为1，保障了卷积后的特色尺寸与原来一样 nn.Conv2d(1, 64, kernel_size=3, stride=1, c=1), nn.ReLU(), nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1), nn.ReLU(), #最大池化，特色数量不变，尺寸减半[(input-kernel_size)/stride + 1] nn.MaxPool2d(stride=2, kernel_size=2) ) #创立全连贯 self.dense = nn.Sequential( nn.Linear(14*14*128, 1024), nn.ReLU(), #随机抛弃局部结点，避免过拟合 nn.Dropout(p=0.5), nn.Linear(1024, 10) )#创立好网络结构后，建设前向流传def forward(self, x): #对数据进行卷积操作 x = self.conv1(x) #扭转特色形态 x = x.c(-1, 14*14*128) #对特色进行全连贯 x = self.dense(x) return x类实例化
model = Model()
指定数据训练次数
epochs = 5
设置学习率，即梯度降落的权重，其值越大收敛越快，越小收敛越慢
learning_rate = 0.0001
选用参数优化器，这里应用Adam
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
选用损失函数，利率期货这里应用穿插熵函数，来断定理论的输入与冀望的输入的靠近水平
criterion = nn.CrossEntropyLoss()
判断是否应用GPU训练
if(use_gpu):
model = model.cuda()loss_f = criterion.cuda()用for循环的形式实现数据的批次训练
for epoch in range(epochs):
#定义并初始化训练过程的损失以及正确率running_loss = 0running_correct = 0for data in data_loader_train: x_train, y_train = data x_train, y_train =x_train.cuda(), y_train.cuda() x_train = x_train.to(device) y_train = y_train.to(device) #将预处理好的数据加载到实例化好的model模型中，进行训练失去输入 outputs = model(x_train) _, pred = torch.max(outputs.data, 1) #每次循环中，梯度必须清零，避免梯度重叠 optimizer.zero_grad() #调用设定的损失 loss = criterion(outputs, y_train) #反向流传损失 loss.backward() #参数更新 optimizer.step() #更新损失 running_loss += loss.item() #更新正确率 running_correct += torch.sum(pred == y_train.data)testing_correct = 0#查看每轮训练后，测试数据集中的正确率for data in data_loader_test: x_test, y_test = data x_test, y_test = Variable(x_test), Variable(y_test) x_test = x_test.to(device) y_test = y_test.to(device) outputs = model(x_test) _, pred = torch.max(outputs.data, 1) testing_correct += torch.sum(pred == y_test.data) print("Loss is {}, Training Accuray is {}%, Test Accurray is {}".format(running_loss/len(data_train), 100*running_correct/len(data_train), 100*testing_correct/len(data_test)))测试训练好的模型
随机加载4个手写数字
data_loader_test = torch.utils.data.DataLoader(dataset=data_test, batch_size=4, shuffle=True)
函数next相干 https://www.runoob.com/python...
函数iter相干 https://www.runoob.com/python...
x_test,y_test = next(iter(data_loader_test))
inputs = Variable(x_test)
inputs = inputs.to(device)
pred = model(inputs)
_为输入的最大值，pred为最大值的索引值
_,pred = torch.max(pred, 1)
print('Predict Label is :', [i for i in pred.data])
print('Real Label is:', [ i for i in y_test] )
img = torchvision.utils.make_grid(x_test)
img = img.numpy().transpose(1, 2, 0)
std = [0.5]
mean = [0.5]
img = img*std+mean
plt.imshow(img)
plt.show()