training_samples = TensorDataset(X_train, y_train)
test_samples = TensorDataset(X_test, y_test)
train_loader = DataLoader(training_samples, batch_size=64, shuffle=True)
valid_loader = DataLoader(test_samples, batch_size=64, shuffle=True)
class DynamicNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
super(DynamicNet, self).__init__()
self.input_linear = torch.nn.Linear(D_in, H)
self.middle_linear = torch.nn.Linear(H, H)
self.output_linear = torch.nn.Linear(H, D_out)
self.init_hidden()
self.init_weights()
def forward(self, x):
h_relu = self.input_linear(x).clamp(min=0)
for _ in range(random.randint(0, 3)):
h_relu = self.middle_linear(h_relu).clamp(min=0)
y_pred = self.output_linear(h_relu)
return y_pred
N, D_in, H, D_out = 64, 30, 100, 19
model = DynamicNet(D_in, H, D_out)
loss_fn = torch.nn.MSELoss(size_average=False)
learning_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
Hey, Im a beginner to deep learning, I have experience with scikit-learn
, shifting to pytorch for deep learning purposes, i tried running this code. but i’m getting this error. could anyone point me in how or what i should do?
TypeError: torch.addmm received an invalid combination of arguments - got (int, torch.FloatTensor, int, torch.DoubleTensor, torch.FloatTensor, out=torch.FloatTensor), but expected one of:
* (torch.FloatTensor source, torch.FloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
* (torch.FloatTensor source, torch.SparseFloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
* (float beta, torch.FloatTensor source, torch.FloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
* (torch.FloatTensor source, float alpha, torch.FloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
* (float beta, torch.FloatTensor source, torch.SparseFloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
* (torch.FloatTensor source, float alpha, torch.SparseFloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
* (float beta, torch.FloatTensor source, float alpha, torch.FloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
didn't match because some of the arguments have invalid types: (int, torch.FloatTensor, int, torch.DoubleTensor, torch.FloatTensor, out=torch.FloatTensor)
* (float beta, torch.FloatTensor source, float alpha, torch.SparseFloatTensor mat1, torch.FloatTensor mat2, *, torch.FloatTensor out)
didn't match because some of the arguments have invalid types: (int, torch.FloatTensor, int, torch.DoubleTensor, torch.FloatTensor, out=torch.FloatTensor)