text: """
  View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
  My Youtube Channel: https://www.youtube.com/user/MorvanZhou
  
  Dependencies:
  torch: 0.4
  matplotlib
  torchvision
  """
  import torch
  from torch import nn
  import torchvision.datasets as dsets
  import torchvision.transforms as transforms
  import matplotlib.pyplot as plt
  
  
  # torch.manual_seed(1)    # reproducible
  
  # Hyper Parameters
  EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch
  BATCH_SIZE = 64
  TIME_STEP = 28          # rnn time step / image height
  INPUT_SIZE = 28         # rnn input size / image width
  LR = 0.01               # learning rate
  DOWNLOAD_MNIST = True   # set to True if haven't download the data
  
  
  # 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
  )
  
  # 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()
  
  # 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
  
  
  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
  
  
  rnn = RNN()
  print(rnn)
  
  optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
  loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted
  
  # 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)
  
  # 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')
  
  