# Predictions are all zeros in binary classification problem

Sorry I’m a newbie.
Trying to solve a classification problem with pytorch. But the predictions always turns into all zeroes.

``````num_classes = 2
input_size = 3
hidden_size = 4
num_epochs = 5

class Net(Module):
def __init__(self, input_size, hidden_size, num_classes):
super().__init__()

self.l1 = Linear(input_size, hidden_size)
self.l2 = Linear(hidden_size, num_classes)
self.sig = Tanh()

def forward(self, x):
out = self.l1(x)
out = self.sig(out)
out = self.l2(out)
return F.log_softmax(out)

nn = Net(input_size, hidden_size, num_classes)

for epoch in range(100):
output = nn(x)
_, y_pred = output.max(1)

print('target',str(y[:4].view(1,-1)).split('\n')[1])
print('predct',str(y_pred[:4].view(1,-1)).split('\n')[1])

loss = F.nll_loss(output,y)
print(loss.data[0])

loss.backward()
optimizer.step()

for i,j in zip(y[:20],y_pred[:20]):
print(i.data[0],j.data[0])``````

That’s strange. I ran your code with random input and targets and the model seems to learn.

After some epochs I get:

``````('target', ' 1  1  0  0')
('predct', ' 1  1  0  0')
Loss: 0.496161192656
``````

What kind of input data are you using? Is the loss moving at all?

1 Like

Im extracting data from a csv file with pandas and converted it to pytorch tensors.

``````Y = torch.from_numpy(np.array(cdf[‘class’][:4000], dtype=np.long))
X = torch.from_numpy(np.column_stack((np.array(cdf[‘cap-shape’][:4000], dtype=np.float), np.array(cdf[‘cap-surface’][:4000], dtype=np.float), np.array(cdf[‘cap-color’][:4000], dtype=np.float))))
y = Variable(Y)``````

Looks fine. Usually you don’t need `requires_grad=True` for your input, but that shouldn’t make a difference.
Could you post the data file?
What does your loss do? Is it constant or is it shaking a bit?

The Loss is decreasing slowly. But the predictions doesn’t change.

``````[Command: python3 -u /root/TorchTest/nn.py]
/root/TorchTest/nn.py:56: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
return F.log_softmax(out)
target  1  0  0  1
predct  1  1  1  1
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``````

And I’m using this data set This one.

Also this is how I’m turning it into categorical data.

``````dataset = pd.read_csv("mushrooms.csv")

cols = list(range(4,23))

dataset.drop(dataset.columns[cols],axis=1,inplace=True)

cdf = pd.DataFrame()

for i in list(dataset.columns):
dataset[i] = pd.Categorical(dataset[i])
cdf[i] = dataset[i].cat.codes
``````

Thank you so much for your help Sir.

I looked at the code and think the dataset might be the issue, since it’s unnormalized and imbalanced.

There are several ways to deal with imbalanced datasets.
A good way is to use the WeightedRandomSampler.

Alternatively, since you are slicing the first `4000` samples, let’s just get `2000` samples of both classes.

``````y_one_idx = np.where(np.array(cdf['class']==1))[0][:2000]
y_zero_idx = np.where(np.array(cdf['class']==0))[0][:2000]
y_idx = np.hstack((y_one_idx, y_zero_idx))
np.random.shuffle(y_idx)

Y = torch.from_numpy(np.array(cdf['class'][y_idx], dtype=np.int64))
X = torch.from_numpy(np.column_stack((np.array(cdf['cap-shape'][y_idx], dtype=np.float32),
np.array(cdf['cap-surface'][y_idx], dtype=np.float32),
np.array(cdf['cap-color'][y_idx], dtype=np.float))))
``````

Also we should try to normalize the data, because categorical data could also cause some problems in your model. Add these lines after loading the data:

``````X = X - torch.mean(X, dim=0)
X = X / torch.std(X, dim=0)
``````

After some epochs, the model should start to learn. Printing the amount of `1`s and `0`s shows, that the output is also balanced.
However, the accuracy is not really good, so there is some work to do regarding the architecture.

2 Likes

Hi ptrblck sir,
Its me again. Can you please explain why after normalizing the data the network works so well.
And can you please tell me from where I can learn more about this amazing technique of “normalizing the data”. And also why should imbalanced dataset be a problem? shouldn’t the model learn the mapping regardless of the sequence of the data?
I’m sorry I’m a newbie, would love if you helped.
Thank you.

When the input values are roughly in the range (-1, 1) the gradients are more meaningful and training goes much faster.

In vague theory almost any model should be able to adjust to the data and make decent predictions, but it often doesn’t work that well. The building blocks of neural nets are designed to work best for inputs roughly in the range (-1, 1). Most building blocks work best when the weights are initialised carefully. Now if your inputs are in the range (80, 120), then your model will first have to learn to adjust to the data, and when it has finished adjusting then there is no guarantee that the model weights are still in a state conducive to further learning.

In the end your model might have an accurcy of `99%` just by predicting one class.