Dataset is 1.7 M data points, 1.36 M Train & 350 K Test , to perform binary classification.Initially i didn’t use batch size and directly fed the training and saw  Loss decreasing with increase in epochs (upto 775) for a suitable config as shown below

Post then , to cover variance ,introduced Data Loader & Tensor data and gave batch size of 128 in code shown below:
n_input_dim = X_train.shape[1]
# Layer size
n_hidden1 = 200  
n_hidden2 = 180
n_hidden3 = 150
n_output =  1 

model = nn.Sequential(
    nn.Linear(n_input_dim, n_hidden1),
    nn.ReLU(),
    nn.Linear(n_hidden1, n_hidden2),
    nn.ReLU(),
    nn.Linear(n_hidden2, n_hidden3),
    nn.ELU(),
    nn.Linear(n_hidden3, n_output),
    nn.Sigmoid())


scaler = preprocessing.MinMaxScaler()
xtrain_norm = scaler.fit_transform(X_train.values)
x_tensor =  torch.from_numpy(xtrain_norm).float()
y_tensor =  torch.from_numpy(Y_train).float()

batch_sz = 128
learning_rate = 0.01
loss_func = nn.BCELoss()

optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

train = data_utils.TensorDataset(x_tensor, y_tensor)
trainloader = data_utils.DataLoader(train, batch_size=batch_sz, shuffle=True)

epochs = 775

for i in range(epochs):

    for j,data in enumerate(trainloader,0):
        input,labels = data
        # wrap them in Variable
        input, labels = Variable(input), Variable(labels)
         y_pred = model(input)
        loss = loss_func(y_pred, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

However , post using batch size Loss doesn’t seem to decrease as depicted below:

Loss_120-80_bs128_rps775.png573×505 42.8 KB

What is going wrong ?  Shouldn’t with every iteration run and correspondingly every epoch run Loss should show decrease as was the case without batch size introduction ?