在測試 mnist 數字辨識時
代碼來源
https://hackmd.io/@Maxlight/SkuYB0w6_#3-hyperparameter
- import torch
- from torch.utils import data as data_
- import torch.nn as nn
- from torch.autograd import Variable
- import matplotlib.pyplot as plt
- import torchvision
- import os
- 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)
- print(train_data.train_data.size())
- print(train_data.train_labels.size())
- plt.ion()
- for i in range(11):
- plt.imshow(train_data.train_data[i].numpy(), cmap = 'gray')
- plt.title('%i' % train_data.train_labels[i])
- plt.pause(0.5)
- plt.show()
- train_loader = data_.DataLoader(dataset = train_data, batch_size = BATCH_SIZE, shuffle = True,num_workers = 2)
- test_data = torchvision.datasets.MNIST(root = './mnist/', train = False)
- test_x = torch.unsqueeze(test_data.test_data, dim = 1).type(torch.FloatTensor)[:2000]/255.
- 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,),# stride = 1, padding = (kernel_size-1)/2 = (5-1)/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, x
- cnn = CNN()
- print(cnn)
- optimization = torch.optim.Adam(cnn.parameters(), lr = LR)
- loss_func = nn.CrossEntropyLoss()
- for epoch in range(EPOCH):
- for step, (batch_x, batch_y) in enumerate(train_loader):
- bx = Variable(batch_x)
- by = Variable(batch_y)
- output = cnn(bx)[0]
- loss = loss_func(output, by)
- optimization.zero_grad()
- loss.backward()
- optimization.step()
- if step % 50 == 0:
- test_output, last_layer = cnn(test_x)
- pred_y = torch.max(test_output, 1)[1].data.numpy()
- accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
- print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
- test_output, _ = cnn(test_x[:10])
- pred_y = torch.max(test_output, 1)[1].data.numpy()
- print(pred_y, 'prediction number')
- print(test_y[:10].numpy(), 'real number')
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