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本文主要介绍了pytorch cnn 识别手写的字实现自建图片数据,分享给大家,具体如下:
# 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))
图片和label 见上一篇文章《pytorch 把MNIST数据集转换成图片和txt》
结果如下:
到此这篇关于pytorch cnn 识别手写的字实现自建图片数据就介绍到这了。你就是那个要搞垮永晟的丹尼尔沈,我才做到这个地步你就受不了了傅函君你太弱了,我们之间真的只有这一条路吗。更多相关pytorch cnn 识别手写的字实现自建图片数据内容请查看相关栏目,小编编辑不易,再次感谢大家的支持!