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input_size = len(input_cols) | |
output_size = len(output_cols) | |
class CarsModel(nn.Module): | |
def __init__(self): | |
super().__init__() | |
self.linear = nn.Linear(input_size, output_size) # fill this (hint: use input_size & output_size defined above) | |
def forward(self, xb): | |
out = self.linear(xb) # fill this | |
return out | |
def training_step(self, batch): | |
inputs, targets = batch | |
# Generate predictions | |
out = self(inputs) | |
# Calcuate loss | |
loss = F.l1_loss(out, targets) # fill this | |
return loss | |
def validation_step(self, batch): | |
inputs, targets = batch | |
# Generate predictions | |
out = self(inputs) | |
# Calculate loss | |
loss = F.l1_loss(out, targets) # fill this | |
return {'val_loss': loss.detach()} | |
def validation_epoch_end(self, outputs): | |
batch_losses = [x['val_loss'] for x in outputs] | |
epoch_loss = torch.stack(batch_losses).mean() # Combine losses | |
return {'val_loss': epoch_loss.item()} | |
def epoch_end(self, epoch, result, num_epochs): | |
# Print result every 20th epoch | |
if (epoch+1) % 20 == 0 or epoch == num_epochs-1: | |
print("Epoch [{}], val_loss: {:.4f}".format(epoch+1, result['val_loss'])) | |
model = CarsModel() | |
list(model.parameters()) |
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