#!/usr/bin/python
# -*- coding: UTF-8 -*-
import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
class Batch_Net(torch.nn.Module):
def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
super(Batch_Net, self).__init__() //固定格式
self.layer1 = torch.nn.Sequential( //Sequential爲順序進行參數中的操作
torch.nn.Linear(in_dim, n_hidden_1), //第一層全連接層
torch.nn.BatchNorm1d(n_hidden_1), torch.nn.ReLU(True) //將輸出非線型化,並添加激活函數
)
self.layer2 = torch.nn.Sequential(
torch.nn.Linear(n_hidden_1, n_hidden_2), //第二層全連接層
torch.nn.BatchNorm1d(n_hidden_2), torch.nn.ReLU(True)
)
self.layer3 = torch.nn.Sequential(torch.nn.Linear(n_hidden_2, out_dim)) //輸出層,輸出層不能非線形化
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
return x
batch_size = 32 //每次訓練以32張圖片爲一組,需要總數量整除
learning_rate = 1e-2 //學習效率
num_epoches = 20 //每次訓練的批次
data_tf = transforms.Compose( #Compose 把多個步驟合到一起
[transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])] #將圖片轉化到-1~1之間
)
train_dataset = datasets.MNIST( //下載訓練集,由於我已經下載了,所以download爲False
root='./data', train=True, transform=data_tf, download=False
)
test_dataset = datasets.MNIST(root='./data', train=False, transform=data_tf, download=False)//訓練集
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)//加載訓練集 #shuffle表示每次迭代的時候是否將數據打亂
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)//加載測試集
model = Batch_Net(28 * 28, 300, 100, 10)//圖片爲28*28的灰度圖,因爲數字爲0-9所以輸出層爲10維
if(torch.cuda.is_available()):
model = model.cuda()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
for epoch in range(num_epoches):
print('epoch {}'.format(epoch + 1))
#training----------------------
train_loss = 0.
train_acc = 0.
for img, label in train_loader:
img = Variable(img)
img = img.view(batch_size, 28 * 28) //將圖片序列化成二維矩陣
label = Variable(label)
if(torch.cuda.is_available()):
img = img.cuda()
label = label.cuda()
out = model(img)
loss = criterion(out, label)
train_loss += loss.item()
pred = torch.max(out, 1)[1]
train_correct = (pred.data.cpu() == label.data.cpu()).sum()
train_acc += train_correct.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(train_loader)), train_acc / (len(train_loader))))