# library
# standard library
import os
# third-party library
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import torchvision
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
# torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001 # learning rate
root = "./mnist/raw/"
pklName = '401.pkl'
def default_loader(path):
# return Image.open(path).convert('RGB')
return Image.open(path)
class MyDataset(Dataset):
def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
fh = open(txt, 'r')
imgs = []
for line in fh:
line = line.strip('\n')
line = line.rstrip()
words = line.split()
imgs.append((words[0], int(words[1])))
self.imgs = imgs
self.transform = transform
self.target_transform = target_transform
self.loader = loader
fh.close()
def __getitem__(self, index):
fn, label = self.imgs[index]
img = self.loader(fn)
img = Image.fromarray(np.array(img), mode='L')
if self.transform is not None:
img = self.transform(img)
return img,label
def __len__(self):
return len(self.imgs)
train_data = MyDataset(txt= root + 'train.txt', transform = torchvision.transforms.ToTensor())
train_loader = DataLoader(dataset = train_data, batch_size=BATCH_SIZE, shuffle=True)
test_data = MyDataset(txt= root + 'test.txt', transform = torchvision.transforms.ToTensor())
test_loader = DataLoader(dataset = test_data, batch_size=BATCH_SIZE)
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential( # input shape (1, 28, 28)
nn.Conv2d(
in_channels=1, # input height
out_channels=16, # n_filters
kernel_size=5, # filter size
stride=1, # filter movement/step
padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
), # output shape (16, 28, 28)
nn.ReLU(), # activation
nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
)
self.conv2 = nn.Sequential( # input shape (16, 14, 14)
nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
nn.ReLU(), # activation
nn.MaxPool2d(2), # output shape (32, 7, 7)
)
self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
output = self.out(x)
return output, x # return x for visualization
cnn = CNN()
print(cnn) # net architecture
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
if os.path.exists('401.pkl') is False:
# training and testing
for epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
b_x = Variable(x) # batch x
b_y = Variable(y) # batch y
output = cnn(b_x)[0] # cnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if step % 50 == 0:
print(step)
torch.save(cnn, pklName) # save entire net
if os.path.exists(pklName) is True:
cnn = torch.load(pklName)
cnn.eval()
eval_loss = 0.
eval_acc = 0.
for i, (tx, ty) in enumerate(test_loader):
t_x = Variable(tx)
t_y = Variable(ty)
output = cnn(t_x)[0]
loss = loss_func(output, t_y)
eval_loss += loss.data[0]
pred = torch.max(output, 1)[1]
num_correct = (pred == t_y).sum()
eval_acc += float(num_correct.data[0])
acc_rate = eval_acc / float(len(test_data))
print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(test_data)), acc_rate))
以pkl的文件方式保存整个网络，这样用来测试就只需要load一下就可以了，略过整个耗时的计算，为将来的客户端应用做准备
