110 lines
4.4 KiB
Python
110 lines
4.4 KiB
Python
"""
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Know more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
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My Youtube Channel: https://www.youtube.com/user/MorvanZhou
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Dependencies:
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torch: 0.1.11
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torchvision
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matplotlib
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"""
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.utils.data as Data
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import torchvision
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import matplotlib.pyplot as plt
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torch.manual_seed(1) # reproducible
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# Hyper Parameters
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EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
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BATCH_SIZE = 50
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LR = 0.001 # learning rate
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DOWNLOAD_MNIST = False
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# Mnist digits dataset
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train_data = torchvision.datasets.MNIST(
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root='./mnist/',
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train=True, # this is training data
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transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
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# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
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download=DOWNLOAD_MNIST, # download it if you don't have it
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)
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# plot one example
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print(train_data.train_data.size()) # (60000, 28, 28)
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print(train_data.train_labels.size()) # (60000)
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plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
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plt.title('%i' % train_data.train_labels[0])
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plt.show()
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# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
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train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
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# convert test data into Variable, pick 2000 samples to speed up testing
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test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
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test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
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test_y = test_data.test_labels[:2000]
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class CNN(nn.Module):
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def __init__(self):
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super(CNN, self).__init__()
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self.conv1 = nn.Sequential( # input shape (1, 28, 28)
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nn.Conv2d(
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in_channels=1, # input height
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out_channels=16, # n_filters
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kernel_size=5, # filter size
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stride=1, # filter movement/step
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padding=2, # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1
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), # output shape (16, 28, 28)
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nn.ReLU(), # activation
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nn.MaxPool2d(kernel_size=2), # choose max value in 2x2 area, output shape (16, 14, 14)
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)
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self.conv2 = nn.Sequential( # input shape (1, 28, 28)
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nn.Conv2d(16, 32, 5, 1, 2), # output shape (32, 14, 14)
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nn.ReLU(), # activation
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nn.MaxPool2d(2), # output shape (32, 7, 7)
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)
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self.out = nn.Linear(32 * 7 * 7, 10) # fully connected layer, output 10 classes
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
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output = self.out(x)
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return output
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cnn = CNN()
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print(cnn) # net architecture
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optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters
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loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
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# training and testing
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for epoch in range(EPOCH):
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for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
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b_x = Variable(x) # batch x
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b_y = Variable(y) # batch y
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output = cnn(b_x) # cnn output
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loss = loss_func(output, b_y) # cross entropy loss
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optimizer.zero_grad() # clear gradients for this training step
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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if step % 50 == 0:
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test_output = cnn(test_x)
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pred_y = torch.max(test_output, 1)[1].data.squeeze()
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accuracy = sum(pred_y == test_y) / test_y.size(0)
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print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)
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# print 10 predictions from test data
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test_output = cnn(test_x[:10])
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pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
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print(pred_y, 'prediction number')
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print(test_y[:10].numpy(), 'real number')
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