用Pytorch訓練CNN(資料集MNIST,使用GPU)
阿新 • • 發佈:2019-02-17
聽說pytorch使用比TensorFlow簡單,加之pytorch現已支援windows,所以今天裝了pytorch玩玩,第一件事還是寫了個簡單的CNN在MNIST上實驗,初步體驗的確比TensorFlow方便。
參考程式碼(在莫煩python的教程程式碼基礎上修改)如下:
import torch import torch.nn as nn from torch.autograd import Variable import torch.utils.data as Data import torchvision import time #import matplotlib.pyplot as plt torch.manual_seed(1) EPOCH = 1 BATCH_SIZE = 50 LR = 0.001 DOWNLOAD_MNIST = False if_use_gpu = 1 # 獲取訓練集dataset training_data = torchvision.datasets.MNIST( root='./mnist/', # dataset儲存路徑 train=True, # True表示是train訓練集,False表示test測試集 transform=torchvision.transforms.ToTensor(), # 將原資料規範化到(0,1)區間 download=DOWNLOAD_MNIST, ) # 列印MNIST資料集的訓練集及測試集的尺寸 print(training_data.train_data.size()) print(training_data.train_labels.size()) # torch.Size([60000, 28, 28]) # torch.Size([60000]) #plt.imshow(training_data.train_data[0].numpy(), cmap='gray') #plt.title('%i' % training_data.train_labels[0]) #plt.show() # 通過torchvision.datasets獲取的dataset格式可直接可置於DataLoader train_loader = Data.DataLoader(dataset=training_data, batch_size=BATCH_SIZE, shuffle=True) # 獲取測試集dataset test_data = torchvision.datasets.MNIST( root='./mnist/', # dataset儲存路徑 train=False, # True表示是train訓練集,False表示test測試集 transform=torchvision.transforms.ToTensor(), # 將原資料規範化到(0,1)區間 download=DOWNLOAD_MNIST, ) # 取前全部10000個測試集樣本 test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1).float(), requires_grad=False) #test_x = test_x.cuda() ## (~, 28, 28) to (~, 1, 28, 28), in range(0,1) test_y = test_data.test_labels #test_y = test_y.cuda() class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Sequential( # (1,28,28) nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2), # (16,28,28) # 想要con2d卷積出來的圖片尺寸沒有變化, padding=(kernel_size-1)/2 nn.ReLU(), nn.MaxPool2d(kernel_size=2) # (16,14,14) ) self.conv2 = nn.Sequential( # (16,14,14) nn.Conv2d(16, 32, 5, 1, 2), # (32,14,14) nn.ReLU(), nn.MaxPool2d(2) # (32,7,7) ) self.out = nn.Linear(32*7*7, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) # 將(batch,32,7,7)展平為(batch,32*7*7) output = self.out(x) return output cnn = CNN() if if_use_gpu: cnn = cnn.cuda() optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) loss_function = nn.CrossEntropyLoss() for epoch in range(EPOCH): start = time.time() for step, (x, y) in enumerate(train_loader): b_x = Variable(x, requires_grad=False) b_y = Variable(y, requires_grad=False) if if_use_gpu: b_x = b_x.cuda() b_y = b_y.cuda() output = cnn(b_x) loss = loss_function(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() if step % 100 == 0: print('Epoch:', epoch, '|Step:', step, '|train loss:%.4f'%loss.data[0]) duration = time.time() - start print('Training duation: %.4f'%duration) cnn = cnn.cpu() test_output = cnn(test_x) pred_y = torch.max(test_output, 1)[1].data.squeeze() accuracy = sum(pred_y == test_y) / test_y.size(0) print('Test Acc: %.4f'%accuracy)