rnn on images

1 files changed, 102 insertions, 0 deletions

diff --git a/dl/tutorials/02_rnn_on_images.py b/dl/tutorials/02_rnn_on_images.py
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+++ b/dl/tutorials/02_rnn_on_images.py
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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters
+sequence_length = 28
+input_size = 28
+hidden_size = 128
+num_layers = 2
+num_classes = 10
+batch_size = 100
+num_epochs = 2
+learning_rate = 0.003
+
+# MNIST dataset
+train_dataset = torchvision.datasets.MNIST(root='../data/',
+                                           train=True,
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../data/',
+                                          train=False,
+                                          transform=transforms.ToTensor())
+
+# Data loader
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=batch_size,
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                          batch_size=batch_size,
+                                          shuffle=False)
+
+
+# Bidirectional recurrent neural network (many-to-one)
+class BiRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, num_classes):
+        super(BiRNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
+        self.fc = nn.Linear(hidden_size * 2, num_classes)  # 2 for bidirection
+
+    def forward(self, x):
+        # Set initial states
+        h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)  # 2 for bidirection
+        c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
+
+        # Forward propagate LSTM
+        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size*2)
+
+        # Decode the hidden state of the last time step
+        out = self.fc(out[:, -1, :])
+        return out
+
+
+model = BiRNN(input_size, hidden_size, num_layers, num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = images.reshape(-1, sequence_length, input_size).to(device)
+        labels = labels.to(device)
+
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if (i + 1) % 100 == 0:
+            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
+                  .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))
+
+# Test the model
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, sequence_length, input_size).to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')
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