Closed AprilWang93 closed 4 years ago
(I‘m new to pytorch and dl.) I revised the original script(https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/recurrent_neural_network/main.py) a little bit (change batch_first=True to False). But the result: both loss(about 2) and accuracy(about 11.35%) is not good. What's the reason?
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 = 1 num_classes = 10 batch_size = 100 num_epochs = 2 learning_rate = 0.01 # 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) # Recurrent neural network (many-to-one) class RNN(nn.Module): def __init__(self, input_size, hidden_size, num_layers, num_classes): super(RNN, self).__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=False) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): # Set initial hidden and cell states h0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device) c0 = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device) # Forward propagate LSTM out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (seq_length, batch_size, hidden_size) # Decode the hidden state of the last time step out = self.fc(out[-1, :, :]) return out model = RNN(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): # print(images.shape) images = images.reshape(sequence_length, batch_size, input_size).to(device) # print(images.shape) 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, batch_size, 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')
Thanks in advance!
solved: https://discuss.pytorch.org/t/lstm-batch-first-causes-different-result/2838/3
(I‘m new to pytorch and dl.) I revised the original script(https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/recurrent_neural_network/main.py) a little bit (change batch_first=True to False). But the result: both loss(about 2) and accuracy(about 11.35%) is not good. What's the reason?
Thanks in advance!