Implementing a custom dataset with PyTorch
I'm attempting to modify this feedforward network taken from https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/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) &amp; 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)  in ()
       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)
  
   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')


You need to change input_size to 4 (2*2), and not 2 as your modified code currently shows.
If you compare it to the original MNIST example, you'll see that input_size is set to 784 (28*28) and not just to 28.
