pytorch cnn 识别手写的字实现自建图片数据

 # 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/" 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 # 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: 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))