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91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
import torch
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import torch.nn as nn
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import torchvision.datasets as dsets
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import torchvision.transforms as transforms
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from torch.autograd import Variable
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# Hyper Parameters
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sequence_length = 28
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input_size = 28
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hidden_size = 128
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num_layers = 2
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num_classes = 10
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batch_size = 100
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num_epochs = 2
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learning_rate = 0.003
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# MNIST Dataset
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train_dataset = dsets.MNIST(root='./data/',
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train=True,
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transform=transforms.ToTensor(),
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download=True)
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test_dataset = dsets.MNIST(root='./data/',
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train=False,
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transform=transforms.ToTensor())
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# Data Loader (Input Pipeline)
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
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batch_size=batch_size,
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shuffle=True)
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test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
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batch_size=batch_size,
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shuffle=False)
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# BiRNN Model (Many-to-One)
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class BiRNN(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes):
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super(BiRNN, self).__init__()
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self.lstm = nn.LSTM(input_size, hidden_size, num_layers,
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batch_first=True, bidirectional=True)
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self.fc = nn.Linear(hidden_size*2, num_classes) # 2 for bidirection
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def forward(self, x):
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# Set initial states
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h0 = Variable(torch.zeros(num_layers*2, x.size(0), hidden_size)) # 2 for bidirection
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c0 = Variable(torch.zeros(num_layers*2, x.size(0), hidden_size))
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# Forward propagate RNN
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out, _ = self.lstm(x, (h0, c0))
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# Decode hidden state of last time step
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out = self.fc(out[:, -1, :])
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return out
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rnn = BiRNN(input_size, hidden_size, num_layers, num_classes)
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# Loss and Optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate)
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# Train the Model
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for epoch in range(num_epochs):
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for i, (images, labels) in enumerate(train_loader):
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images = Variable(images.view(-1, sequence_length, input_size))
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labels = Variable(labels)
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# Forward + Backward + Optimize
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optimizer.zero_grad()
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outputs = rnn(images)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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if (i+1) % 100 == 0:
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print ('Epoch [%d/%d], Step [%d/%d], Loss: %.4f'
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%(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data[0]))
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# Test the Model
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correct = 0
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total = 0
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for images, labels in test_loader:
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images = Variable(images.view(-1, sequence_length, input_size))
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outputs = rnn(images)
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum()
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print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total)) |