I'm attempting to modify this feedforward network taken from https://github.com/yunjey/pytorch-t.../01-basics/feedforward_neural_network/main.py to utilize my own dataset. I define a custom dataset of two 1 dim arrays as input and two scalars the corresponding output : x = torch.tensor([[5.5, 3,3,4] , [1 , 2,3,4], [9 , 2,3,4]]) print(x) y = torch.tensor([1,2,3]) print(y) import torch.utils.data as data_utils my_train = data_utils.TensorDataset(x, y) my_train_loader = data_utils.DataLoader(my_train, batch_size=50, shuffle=True) I've updated the hyperparameters to match new input_size (2) & num_classes (3). I've also changed images = images.reshape(-1, 28*28).to(device) to images = images.reshape(-1, 4).to(device) As the training set is minimal I've changed the batch_size to 1. Upon making these modifications I receive error when attempting to train : RuntimeError Traceback (most recent call > last) <ipython-input-52-9cdca58f3ef6> in <module>() > 51 > 52 # Forward pass > ---> 53 outputs = model(images) > 54 loss = criterion(outputs, labels) > 55 > > /home/.local/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) > 489 result = self._slow_forward(*input, **kwargs) > 490 else: > --> 491 result = self.forward(*input, **kwargs) > 492 for hook in self._forward_hooks.values(): > 493 hook_result = hook(self, input, result) > > <ipython-input-52-9cdca58f3ef6> in forward(self, x) > 31 > 32 def forward(self, x): > ---> 33 out = self.fc1(x) > 34 out = self.relu(out) > 35 out = self.fc2(out) > > /home/.local/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) > 489 result = self._slow_forward(*input, **kwargs) > 490 else: > --> 491 result = self.forward(*input, **kwargs) > 492 for hook in self._forward_hooks.values(): > 493 hook_result = hook(self, input, result) > > /home/.local/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input) > 53 > 54 def forward(self, input): > ---> 55 return F.linear(input, self.weight, self.bias) > 56 > 57 def extra_repr(self): > > /home/.local/lib/python3.6/site-packages/torch/nn/functional.py > in linear(input, weight, bias) > 990 if input.dim() == 2 and bias is not None: > 991 # fused op is marginally faster > --> 992 return torch.addmm(bias, input, weight.t()) > 993 > 994 output = input.matmul(weight.t()) > > RuntimeError: size mismatch, m1: [3 x 4], m2: [2 x 3] at > /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:249 How to amend code to match expected dimensionality ? I'm unsure what code to change as I've changed all parameters that require updating ? Source prior to changes : 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 input_size = 784 hidden_size = 500 num_classes = 10 num_epochs = 5 batch_size = 100 learning_rate = 0.001 # 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) # Fully connected neural network with one hidden layer class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(NeuralNet, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out model = NeuralNet(input_size, hidden_size, 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): # Move tensors to the configured device images = images.reshape(-1, 28*28).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 # In test phase, we don't need to compute gradients (for memory efficiency) with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: images = images.reshape(-1, 28*28).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('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total)) # Save the model checkpoint torch.save(model.state_dict(), 'model.ckpt') Source post changes : x = torch.tensor([[5.5, 3,3,4] , [1 , 2,3,4], [9 , 2,3,4]]) print(x) y = torch.tensor([1,2,3]) print(y) import torch.utils.data as data_utils my_train = data_utils.TensorDataset(x, y) my_train_loader = data_utils.DataLoader(my_train, batch_size=50, shuffle=True) print(my_train) print(my_train_loader) 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 input_size = 2 hidden_size = 3 num_classes = 3 num_epochs = 5 batch_size = 1 learning_rate = 0.001 # MNIST dataset train_dataset = my_train # Data loader train_loader = my_train_loader # Fully connected neural network with one hidden layer class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(NeuralNet, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out model = NeuralNet(input_size, hidden_size, 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): # Move tensors to the configured device images = images.reshape(-1, 4).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 # In test phase, we don't need to compute gradients (for memory efficiency) with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: images = images.reshape(-1, 4).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('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total)) # Save the model checkpoint torch.save(model.state_dict(), 'model.ckpt') Continue reading...
