train_model.py

def train_model(intercept_init, coef_init, X, y, epochs, lr, batch_size=None, randomise=True):
   if batch_size is None or batch_size <= 0:
       batch_size = X.shape[0]
   classes = np.unique(y)
   model = linear_model.SGDClassifier(loss='log', learning_rate='constant', eta0=lr, verbose=0)
   set_weights(intercept_init, coef_init, classes, model)
   batch_train(model, X, y, classes, epochs, batch_size, randomise)
   return model