Hi everyone, I try to convert my linear system to CNN. My linear NN class is
class Module(nn.Module):
def __init__(self, D_in, H1, H2,D_out):
super().__init__()
self.linear1 = nn.Linear(D_in, H1)
self.linear2 = nn.Linear(H1, H2)
self.linear3 = nn.Linear(H2, D_out)
self.dropout1 = nn.Dropout(0.2)
self.dropout2 = nn.Dropout(0.2)
def forward(self, x):
x = F.relu(self.linear1(x))
x = self.dropout1(x)
x = F.relu(self.linear2(x))
x = self.dropout2(x)
x = self.linear3(x)
return x
and my training set input shape is [2048,15,1]
and my interpreter loop is
for e in range(epochs):
running_loss = 0.0
running_corrects = 0.0
val_running_loss = 0.0
val_running_corrects = 0.0
for inputs,out in train_generator:
output=model(inputs)
print(inputs.size())
print(output.size())
loss = criterion(output,out)
preds,_=torch.max(output,1)
#
outputss.append(preds.max().detach().numpy())
losses.append(loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
with torch.no_grad():
for val_inputs, val_labels in valid_generator:
val_inputs=val_inputs.to(device)
val_labels=val_labels.to(device)
val_outputs = model(val_inputs)
val_loss = criterion(val_outputs, val_labels)
val_preds,_ = torch.max(val_outputs, 1)
val_running_loss_history.append(val_loss)
val_running_corrects_history.append(val_preds.max().detach().numpy())
and my CNN class :
class Module(nn.Module):
#def __init__(self,insize,hid,out):
def __init__(self,insize=15,hid=16,out=1):
super().__init__()
self.con1=nn.Conv1d(insize,hid,1)
self.con2=nn.Conv1d(hid,out,1)
self.fc1=nn.Linear(1*2048,1)
self.fc2=nn.Linear(1901,1)
self.drop=nn.Dropout(0.2)
def forward(self,x):
x=F.elu(self.con1(x))
x=F.max_pool1d(x,1)
x=F.elu(self.con2(x))
x=self.drop(x)
if x.size(0)==1901:
x=x.view(-1,1901)
x=self.fc2(-1,1901)
else:
x=x.view(-1,2048*1)
x=self.fc1(x)
return x
when I execute my code I got this error:
TypeError: forward() takes 2 positional arguments but 3 were given
How can I solve this?
Thank you
