pytorch之 RNN classifier

 1 import torch
 2 from torch import nn
 3 import torchvision.datasets as dsets
 4 import torchvision.transforms as transforms
 5 import matplotlib.pyplot as plt
 6 
 7 
 8 # torch.manual_seed(1)    # reproducible
 9 
10 # Hyper Parameters
11 EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch
12 BATCH_SIZE = 64
13 TIME_STEP = 28          # rnn time step / image height
14 INPUT_SIZE = 28         # rnn input size / image width
15 LR = 0.01               # learning rate
16 DOWNLOAD_MNIST = True   # set to True if haven't download the data
17 
18 
19 # Mnist digital dataset
20 train_data = dsets.MNIST(
21     root='./mnist/',
22     train=True,                         # this is training data
23     transform=transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
24                                         # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
25     download=DOWNLOAD_MNIST,            # download it if you don't have it
26 )
27 
28 # # plot one example
29 # print(train_data.train_data.size())     # (60000, 28, 28)
30 # print(train_data.train_labels.size())   # (60000)
31 # plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
32 # plt.title('%i' % train_data.train_labels[0])
33 # plt.show()
34 
35 # Data Loader for easy mini-batch return in training
36 train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
37 
38 # convert test data into Variable, pick 2000 samples to speed up testing
39 test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
40 test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255.   # shape (2000, 28, 28) value in range(0,1)
41 test_y = test_data.test_labels.numpy()[:2000]    # covert to numpy array
42 
43 
44 class RNN(nn.Module):
45     def __init__(self):
46         super(RNN, self).__init__()
47 
48         self.rnn = nn.LSTM(         # if use nn.RNN(), it hardly learns
49             input_size=INPUT_SIZE,
50             hidden_size=64,         # rnn hidden unit
51             num_layers=1,           # number of rnn layer
52             batch_first=True,       # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
53         )
54 
55         self.out = nn.Linear(64, 10)
56 
57     def forward(self, x):
58         # x shape (batch, time_step, input_size)
59         # r_out shape (batch, time_step, output_size)
60         # h_n shape (n_layers, batch, hidden_size)
61         # h_c shape (n_layers, batch, hidden_size)
62         r_out, (h_n, h_c) = self.rnn(x, None)   # None represents zero initial hidden state
63 
64         # choose r_out at the last time step
65         out = self.out(r_out[:, -1, :])
66         return out
67 
68 
69 rnn = RNN()
70 print(rnn)
71 
72 optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
73 loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted
74 
75 # training and testing
76 for epoch in range(EPOCH):
77     for step, (b_x, b_y) in enumerate(train_loader):        # gives batch data
78         b_x = b_x.view(-1, 28, 28)              # reshape x to (batch, time_step, input_size)
79 
80         output = rnn(b_x)                               # rnn output
81         loss = loss_func(output, b_y)                   # cross entropy loss
82         optimizer.zero_grad()                           # clear gradients for this training step
83         loss.backward()                                 # backpropagation, compute gradients
84         optimizer.step()                                # apply gradients
85 
86         if step % 50 == 0:
87             test_output = rnn(test_x)                   # (samples, time_step, input_size)
88             pred_y = torch.max(test_output, 1)[1].data.numpy()
89             accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
90             print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
91 
92 # print 10 predictions from test data
93 test_output = rnn(test_x[:10].view(-1, 28, 28))
94 pred_y = torch.max(test_output, 1)[1].data.numpy()
95 print(pred_y, 'prediction number')
96 print(test_y[:10], 'real number')