Why is a simple Binary classification failing in a feedforward neural network?
I am new to Pytorch. I was trying to model a binary classifier on the Kepler dataset. The following was my dataset class.

class KeplerDataset(Dataset):
    def __init__(self, test=False):
        self.dataframe_orig = pd.read_csv(koi_cumm_path)

        if (test == False):
            self.data = df_numeric[( df_numeric.koi_disposition == 1 ) | ( df_numeric.koi_disposition == 0 )].values
        else:
            self.data = df_numeric[~(( df_numeric.koi_disposition == 1 ) | ( df_numeric.koi_disposition == 0 ))].values

        self.X_data = torch.FloatTensor(self.data[:, 1:])
        self.y_data = torch.FloatTensor(self.data[:, 0])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        return self.X_data[index], self.y_data[index]


Here, I created a custom classifier class with one hidden layer and a single output unit that produces sigmoidal probability of being in class 1 (planet).

class KOIClassifier(nn.Module):
    def __init__(self, input_dim, out_dim):
        super(KOIClassifier, self).__init__()
        self.linear1 = nn.Linear(input_dim, 32)

        self.linear2 = nn.Linear(32, 32)

        self.linear3 = nn.Linear(32, out_dim)


    def forward(self, xb):
        out = self.linear1(xb)
        out = F.relu(out)
        out = self.linear2(out)
        out = F.relu(out)
        out = self.linear3(out)
        out = torch.sigmoid(out)

        return out


I then created a train_model function to optimize the loss using SGD.

def train_model(X, y):
    criterion = nn.BCELoss()

    optim = torch.optim.SGD(model.parameters(), lr=0.001)

    n_epochs = 100
    losses = []

    for epoch in range(n_epochs):
        y_pred = model.forward(X)
        loss = criterion(y_pred, y)
        losses.append(loss.item())
        optim.zero_grad()
        loss.backward()
        optim.step()

losses = []
for X, y in train_loader:
    losses.append(train_model(X, y))


But after performing the optimization over the train_loader, When I try predicting on the trainn_loader itself, the prediction values are so much worse. 

for features, y in train_loader:
    y_pred = model.predict(features)
    break

y_pred


&gt; tensor([[4.5436e-02],
        [1.5024e-02],
        [2.2579e-01],
        [4.2279e-01],
        [6.0811e-02],
        .....


Why is my model not working properly? Is it the problem with the dataset or am I doing something wrong with implementing the Neural net? I will link my Kaggle notebook because more context might be helpful. Please help.

You are optimizing many times (100 steps) on the first batch (first samples), then moving to the next samples. It means that your model will overfit your few samples before going to the next batch. Then, your training will be very non smooth, diverge and go far from your global optimum.

Usually, in a training loop you should:


go over all samples (this is one epoch)
shuffle your dataset in order to visit your samples in a different order (set your pytorch training loader accordingly)
go back to 1. until you reach the max number of epochs


Also you should not define your optimizer each time (nor your criterion).

Your training loop should look like this:

criterion = nn.BCELoss()
optim = torch.optim.SGD(model.parameters(), lr=0.001)
n_epochs = 100

def train_model():
    for X, y in train_loader:
        optim.zero_grad()
        y_pred = model.forward(X)
        loss = criterion(y_pred, y)
        loss.backward()
        optim.step()

for epoch in range(n_epochs):
    train_model()

