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tutorial-contents/502_GPU.py

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+"""
+Know more, visit 莫烦Python: https://morvanzhou.github.io/tutorials/
+My Youtube Channel: https://www.youtube.com/user/MorvanZhou
+
+Dependencies:
+torch: 0.1.11
+torchvision
+"""
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.utils.data as Data
+import torchvision
+
+torch.manual_seed(1)
+
+EPOCH = 1
+BATCH_SIZE = 50
+LR = 0.001
+DOWNLOAD_MNIST = False
+
+train_data = torchvision.datasets.MNIST(root='./mnist/', train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)
+train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
+
+test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
+
+# !!!!!!!! Change in here !!!!!!!!! #
+test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000].cuda()/255.   # Tensor on GPU
+test_y = test_data.test_labels[:2000]
+
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,),
+                                   nn.ReLU(), nn.MaxPool2d(kernel_size=2),)
+        self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2),)
+        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)
+        output = self.out(x)
+        return output
+
+cnn = CNN()
+
+# !!!!!!!! Change in here !!!!!!!!! #
+cnn.cuda()      # Moves all model parameters and buffers to the GPU.
+
+optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
+loss_func = nn.CrossEntropyLoss()
+
+for epoch in range(EPOCH):
+    for step, (x, y) in enumerate(train_loader):
+
+        # !!!!!!!! Change in here !!!!!!!!! #
+        b_x = Variable(x).cuda()    # Tensor on GPU
+        b_y = Variable(y).cuda()    # Tensor on GPU
+
+        output = cnn(b_x)
+        loss = loss_func(output, b_y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if step % 50 == 0:
+            test_output = cnn(test_x)
+
+            # !!!!!!!! Change in here !!!!!!!!! #
+            pred_y = torch.max(test_output, 1)[1].cup().data.squeeze()  # Move to CPU
+
+            accuracy = sum(pred_y == test_y) / test_y.size(0)
+            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)
+
+
+test_output = cnn(test_x[:10])
+
+# !!!!!!!! Change in here !!!!!!!!! #
+pred_y = torch.max(test_output, 1)[1].cup().data.numpy().squeeze()  # Move to CPU
+
+print(pred_y, 'prediction number')
+print(test_y[:10].numpy(), 'real number')