So I have the following model:

```
model = Net(n_x, n_h, n_y)
optim = torch.optim.ASGD(model.parameters(),lr=0.005)
loss_function = nn.BCELoss()
```

and this is the way I am training it:

I am basically trying to count the times my algorithm is correctly predicting the output.

```
train_losses = []
accuracy = []
for epoch in range(epochs):
model.train()
train_loss = []
batch_accuracy = []
for idx in range(0, train_x.shape[0], batch_size):
batch_x = torch.from_numpy(train_x[idx : idx + batch_size]).float()
batch_y = torch.from_numpy(train_y[:,idx : idx + batch_size]).float()
model_output = model(batch_x)
batch_accuracy=[]
loss = loss_function(model_output, batch_y)
train_loss.append(loss.item())
labels_normalized=list()
count=0
#Here I am checking the output of my label against the ground truth
for i in range(0,len(model_output)):
if(model_output[:,i]>0.5 and batch_y[:,i]>0):
count+=1
elif((model_output[:,i]<0.5) and (batch_y[:,i]==0)):
count+=1
else:
continue
Needless to say,
optim.zero_grad()
loss.backward()
optim.step()
if epoch % 100 == 1:
print("Iteration : {}, Training loss: {} ".format(epoch,np.mean(train_loss)))
train_losses.append(train_loss)
#Trying to print the count here
print(count)
plt.plot(np.squeeze(train_losses))
plt.ylabel('loss')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
```

As you can see above I am trying to â€ścountâ€ť the number of times the label is the same as the predicted label. However, in the print statement, I keep getting 1, in other words, my accuracy in training is not improving. Needless to say, my loss is improving and getting lower quite significantly so I donâ€™t think it is that issue. Also just trying to measure my training accuracy here.