I trained a neural network. I did this to learn about my training and testing achievements as well as my error rates, do you think it is correct? can i trust my own code? I want to get an idea about bias and variance.
Sorry for my english I use google translation 
*
class ANN(nn.Module):
def init(self,input_dim,hidden_dim,output_dim):
super(ANN,self).init()
self.fc1=nn.Linear(input_dim,hidden_dim)
self.aktivasyon1=nn.SELU()
self.fc2=nn.Linear(hidden_dim,hidden_dim)
self.aktivasyon2=nn.SELU()
self.fc3=nn.Linear(hidden_dim,hidden_dim)
self.aktivasyon3=nn.SELU()
self.fc4=nn.Linear(hidden_dim,output_dim)
def forward(self,x):
x=self.fc1(x)
x=self.aktivasyon1(x)
x=self.fc2(x)
x=self.aktivasyon2(x)
x=self.fc3(x)
x=self.aktivasyon3(x)
x=self.fc4(x)
return x
input_dim=28*28
hidden_dim=150
output_dim=10
model=ANN(input_dim,hidden_dim,output_dim).to(device)
optimizer=torch.optim.SGD(model.parameters(),lr=0.001)
error=nn.CrossEntropyLoss()
loss_list = []
for epoch in range(num_epochs):
#lr.step()
for i, (images,labels) in enumerate(train_l):
images=images.to(device)
labels=labels.to(device)
images=Variable(images.view(-1,28*28))
labels=Variable(labels).to(device)
optimizer.zero_grad()
out=model(images.float())
loss=error(out,labels)
loss.backward()
optimizer.step()
def check_accuracy(loader, model):
total=0
correct=0
correct_error=0
model.eval()
with torch.no_grad():
for images,labels in loader:
images=images.to(device)
labels=labels.to(device)
images=Variable(images.view(-1,28*28))
out=model(images.float())
y_pred=torch.max(out.data,1)[1]
total+=len(labels)
correct += (y_pred == labels).sum()
accuracy = 100 * correct / float(total)
error= 100 - accuracy
print('Accuracy: {}% Error: {}%'.format(accuracy,error))
model.train()
print(“Train Result:”)
*****check_accuracy(train_l, model)
print("----------------------------------------------------")
print(“Test Result:”)
*****check_accuracy(test_l, model)
–>Train Result:
Accuracy: 89% Error: 11%
–>Test Result:
Accuracy: 88% Error: 12%