Simple NN model design for a Tabular data (regression)

Hi, I am trying to solve a tabular data, regression problem.

Input has 200 features, and I am trying to predict a target variable which is boolean. (probability).

Here is my model.

def handcoded()-> nn.Sequential:    
    return nn.Sequential(
        nn.Linear(200, 1000),
        nn.Linear(500, 1),

model = handcoded()
learn = Learner(data, model, metrics=[accuracy])
learn.lr_find() //returns an error. "forward() takes 2 positional arguments but 3 were given"

Does my model definition look good?

Please show the full stack trace here so we can better understand what happened.

Here it is.

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
TypeError                                 Traceback (most recent call last)
<ipython-input-12-d81c6bd29d71> in <module>
----> 1 learn.lr_find()

/opt/anaconda3/lib/python3.7/site-packages/fastai/ in lr_find(learn, start_lr, end_lr, num_it, stop_div, **kwargs)
     30     cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
     31     a = int(np.ceil(num_it/len(
---> 32, start_lr, callbacks=[cb], **kwargs)
     34 def to_fp16(learn:Learner, loss_scale:float=512., flat_master:bool=False)->Learner:

/opt/anaconda3/lib/python3.7/site-packages/fastai/ in fit(self, epochs, lr, wd, callbacks)
    176         callbacks = [cb(self) for cb in self.callback_fns] + listify(callbacks)
    177         fit(epochs, self.model, self.loss_func, opt=self.opt,, metrics=self.metrics,
--> 178             callbacks=self.callbacks+callbacks)
    180     def create_opt(self, lr:Floats, wd:Floats=0.)->None:

/opt/anaconda3/lib/python3.7/site-packages/fastai/utils/ in wrapper(*args, **kwargs)
     84         try:
---> 85             return func(*args, **kwargs)
     86         except Exception as e:
     87             if "CUDA out of memory" in str(e) or tb_clear_frames=="1":

/opt/anaconda3/lib/python3.7/site-packages/fastai/ in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
     98     except Exception as e:
     99         exception = e
--> 100         raise e
    101     finally: cb_handler.on_train_end(exception)

/opt/anaconda3/lib/python3.7/site-packages/fastai/ in fit(epochs, model, loss_func, opt, data, callbacks, metrics)
     88             for xb,yb in progress_bar(data.train_dl, parent=pbar):
     89                 xb, yb = cb_handler.on_batch_begin(xb, yb)
---> 90                 loss = loss_batch(model, xb, yb, loss_func, opt, cb_handler)
     91                 if cb_handler.on_batch_end(loss): break

/opt/anaconda3/lib/python3.7/site-packages/fastai/ in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
     18     if not is_listy(xb): xb = [xb]
     19     if not is_listy(yb): yb = [yb]
---> 20     out = model(*xb)
     21     out = cb_handler.on_loss_begin(out)

/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/ in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--> 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

TypeError: forward() takes 2 positional arguments but 3 were given

Hi, did you find any solution to this?