rename

1 files changed, 0 insertions, 102 deletions

diff --git a/dl/tutorials/02_rnn_on_images.py b/dl/tutorials/02_rnn_on_images.py
deleted file mode 100644
index 93d2651..0000000
--- a/dl/tutorials/02_rnn_on_images.py
+++ /dev/null
@@ -1,102 +0,0 @@
-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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