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Neural_Network.0.py 3.6 kB

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  1. import torch
  2. from torch import nn, optim
  3. from torch.autograd import Variable
  4. from torch.utils.data import DataLoader
  5. from torchvision import transforms
  6. from torchvision import datasets
  7. batch_size = 32
  8. learning_rate = 1e-2
  9. num_epoches = 50
  10. # 下载训练集 MNIST 手写数字训练集
  11. dataset_path = "../data/mnist"
  12. train_dataset = datasets.MNIST(
  13. root=dataset_path, train=True, transform=transforms.ToTensor(), download=True)
  14. test_dataset = datasets.MNIST(
  15. root=dataset_path, train=False, transform=transforms.ToTensor())
  16. train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
  17. test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
  18. # 定义简单的前馈神经网络
  19. class Neuralnetwork(nn.Module):
  20. def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
  21. super(Neuralnetwork, self).__init__()
  22. self.layer1 = nn.Linear(in_dim, n_hidden_1)
  23. self.layer2 = nn.Linear(n_hidden_1, n_hidden_2)
  24. self.layer3 = nn.Linear(n_hidden_2, out_dim)
  25. def forward(self, x):
  26. x = self.layer1(x)
  27. x = self.layer2(x)
  28. x = self.layer3(x)
  29. return x
  30. model = Neuralnetwork(28 * 28, 300, 100, 10)
  31. if torch.cuda.is_available():
  32. model = model.cuda()
  33. criterion = nn.CrossEntropyLoss()
  34. optimizer = optim.SGD(model.parameters(), lr=learning_rate)
  35. for epoch in range(num_epoches):
  36. print('epoch {}'.format(epoch + 1))
  37. print('*' * 10)
  38. running_loss = 0.0
  39. running_acc = 0.0
  40. for i, data in enumerate(train_loader, 1):
  41. # FIXME: label need to change one-hot coding
  42. img, label = data
  43. img = img.view(img.size(0), -1)
  44. target = torch.zeros(label.size(0), 10)
  45. target = target.scatter_(1, label.data, 1)
  46. if torch.cuda.is_available():
  47. img = Variable(img).cuda()
  48. label = Variable(label).cuda()
  49. else:
  50. img = Variable(img)
  51. label = Variable(label)
  52. # 向前传播
  53. out = model(img)
  54. loss = criterion(out, label)
  55. running_loss += loss.data[0] * label.size(0)
  56. _, pred = torch.max(out, 1)
  57. num_correct = (pred == label).sum()
  58. running_acc += num_correct.data[0]
  59. # 向后传播
  60. optimizer.zero_grad()
  61. loss.backward()
  62. optimizer.step()
  63. if i % 300 == 0:
  64. print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(
  65. epoch + 1, num_epoches, running_loss / (batch_size * i),
  66. running_acc / (batch_size * i)))
  67. print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(
  68. epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(
  69. train_dataset))))
  70. model.eval()
  71. eval_loss = 0.
  72. eval_acc = 0.
  73. for data in test_loader:
  74. img, label = data
  75. img = img.view(img.size(0), -1)
  76. if torch.cuda.is_available():
  77. img = Variable(img, volatile=True).cuda()
  78. label = Variable(label, volatile=True).cuda()
  79. else:
  80. img = Variable(img, volatile=True)
  81. label = Variable(label, volatile=True)
  82. out = model(img)
  83. loss = criterion(out, label)
  84. eval_loss += loss.data[0] * label.size(0)
  85. _, pred = torch.max(out, 1)
  86. num_correct = (pred == label).sum()
  87. eval_acc += num_correct.data[0]
  88. print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
  89. test_dataset)), eval_acc / (len(test_dataset))))
  90. print()
  91. # 保存模型
  92. torch.save(model.state_dict(), './model_Neural_Network.pth')

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