In [2]:

    

import torch
from torch import nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt


    

In [4]:

    

EPOCH = 1
BATCH_SIZE = 64   
TIME_STEP = 28 # image height
INPUT_SIZE = 28 #image width
LR = 0.01
DOWNLOAD_MNIST = True # set True if need to down dataset


    

In [5]:

    

# Mnist digital dataset
train_data = dsets.MNIST(
    root='./mnist/',
    train=True,                         # this is training data
    transform=transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to
                                        # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
    download=DOWNLOAD_MNIST,            # download it if you don't have it
)


    

    




Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Processing...
Done!

In [6]:

    

# plot one example
print(train_data.train_data.size())     # (60000, 28, 28)
print(train_data.train_labels.size())   # (60000)
plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()


    

    




torch.Size([60000, 28, 28])
torch.Size([60000])






    

In [7]:

    

# Data Loader for easy mini-batch return in training
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# convert test data into Variable, pick 2000 samples to speed up testing
test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())
test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255.   # shape (2000, 28, 28) value in range(0,1)
test_y = test_data.test_labels.numpy()[:2000]    # covert to numpy array


    

In [8]:

    

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()

        self.rnn = nn.LSTM(         # if use nn.RNN(), it hardly learns
            input_size=INPUT_SIZE,
            hidden_size=64,         # rnn hidden unit
            num_layers=1,           # number of rnn layer
            batch_first=True,       # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
        )

        self.out = nn.Linear(64, 10)

    def forward(self, x):
        # x shape (batch, time_step, input_size)
        # r_out shape (batch, time_step, output_size)
        # h_n shape (n_layers, batch, hidden_size)
        # h_c shape (n_layers, batch, hidden_size)
        r_out, (h_n, h_c) = self.rnn(x, None)   # None represents zero initial hidden state

        # choose r_out at the last time step
        out = self.out(r_out[:, -1, :])
        return out


    

In [9]:

    

rnn = RNN()


    

In [10]:

    

print(rnn)


    

    




RNN(
  (rnn): LSTM(28, 64, batch_first=True)
  (out): Linear(in_features=64, out_features=10, bias=True)
)

In [11]:

    

optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted


    

In [12]:

    

# training and testing
for epoch in range(EPOCH):
    for step, (b_x, b_y) in enumerate(train_loader):        # gives batch data
        b_x = b_x.view(-1, 28, 28)              # reshape x to (batch, time_step, input_size)

        output = rnn(b_x)                               # rnn output
        loss = loss_func(output, b_y)                   # cross entropy loss
        optimizer.zero_grad()                           # clear gradients for this training step
        loss.backward()                                 # backpropagation, compute gradients
        optimizer.step()                                # apply gradients

        if step % 50 == 0:
            test_output = rnn(test_x)                   # (samples, time_step, input_size)
            pred_y = torch.max(test_output, 1)[1].data.numpy()
            accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)


    

    




Epoch:  0 | train loss: 2.2974 | test accuracy: 0.10
Epoch:  0 | train loss: 1.2886 | test accuracy: 0.58
Epoch:  0 | train loss: 0.7014 | test accuracy: 0.70
Epoch:  0 | train loss: 0.5275 | test accuracy: 0.84
Epoch:  0 | train loss: 0.5474 | test accuracy: 0.81
Epoch:  0 | train loss: 0.1786 | test accuracy: 0.89
Epoch:  0 | train loss: 0.3087 | test accuracy: 0.88
Epoch:  0 | train loss: 0.3303 | test accuracy: 0.92
Epoch:  0 | train loss: 0.3153 | test accuracy: 0.92
Epoch:  0 | train loss: 0.3021 | test accuracy: 0.94
Epoch:  0 | train loss: 0.1483 | test accuracy: 0.93
Epoch:  0 | train loss: 0.2142 | test accuracy: 0.94
Epoch:  0 | train loss: 0.2017 | test accuracy: 0.94
Epoch:  0 | train loss: 0.1233 | test accuracy: 0.94
Epoch:  0 | train loss: 0.1423 | test accuracy: 0.92
Epoch:  0 | train loss: 0.3253 | test accuracy: 0.94
Epoch:  0 | train loss: 0.0447 | test accuracy: 0.95
Epoch:  0 | train loss: 0.0302 | test accuracy: 0.95
Epoch:  0 | train loss: 0.1699 | test accuracy: 0.94

In [13]:

    

# print 10 predictions from test data
test_output = rnn(test_x[:10].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy()
print(pred_y, 'prediction number')
print(test_y[:10], 'real number')


    

    




[7 2 1 0 4 1 4 9 6 9] prediction number
[7 2 1 0 4 1 4 9 5 9] real number

In [ ]: