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) & 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')
