[GitHub] eaglecros commented on issue #8669: module.Module and CSVIter

[GitBox <gi...@apache.org>]

eaglecros commented on issue #8669: module.Module and CSVIter
URL: https://github.com/apache/incubator-mxnet/issues/8669#issuecomment-350672417
 
 
   Ok, so with your message, and another test I did, I believe I have come to the reason of the problem... Now I just need to understand how to solve it :)
   
   ```
   l,c,v = numpy.loadtxt(filename, delimiter='\t',dtype='int').T
   iter_train = mx.io.NDArrayIter({'user': l, 'item': c}, label=v, batch_size=args.bsize)
   print(iter_train.provide_data)
   ```
   returns
   
   ```
   [DataDesc[item,(100,),<class 'numpy.float32'>,NCHW], DataDesc[user,(100,),<class 'numpy.float32'>,NCHW]]
   ```
   
   However, 
   
   ```
   iter_train = mx.io.CSVIter(data_csv=f1, data_shape=(2,), label_csv=f2, label_shape=(1,), batch_size=1000, data_name=['user','item'])
   print(iter_train.provide_data)
   ```
   outputs
   
   ```
   [DataDesc[['user', 'item'],(1000, 2),<class 'numpy.float32'>,NCHW]]
   ```
   
   In addition, I tried commenting out lines 62-65 on `mxnet/module/base_module.py`, the ones that led to the error, i.e.:
   
   ```
   def _check_names_match(data_names, data_shapes, name, throw):
       """Check that input names matches input data descriptors."""
       actual = [x[0] for x in data_shapes]
       if sorted(data_names) != sorted(actual):
           msg = "Data provided by %s_shapes don't match names specified by %s_names (%s vs. %s)"%(
               name, name, str(data_shapes), str(data_names))
   #        if throw:
   #            raise ValueError(msg)
   #        else:
   #            warnings.warn(msg)
   ```
   and then I got this error, which I believe means that it is not a simple naming problem:
   
   ```
   Traceback (most recent call last):
     File "/home/german/trabajo/Sciling/git/recsys-sbcorporation/train2.py", line 172, in <module>
       model = train_model(ctx=mx.cpu(0), net=nn, data_names=('user','item'), train_iter=train_iter, val_iter=train_iter)
     File "/home/german/trabajo/Sciling/git/recsys-sbcorporation/train2.py", line 133, in train_model
       model.fit(train_iter, num_epoch=args.nepochs, optimizer='adam', optimizer_params=(('learning_rate', 0.001),), eval_metric='mse', eval_data=val_iter)
     File "/usr/local/lib/python3.5/dist-packages/mxnet/module/base_module.py", line 460, in fit
       for_training=True, force_rebind=force_rebind)
     File "/usr/local/lib/python3.5/dist-packages/mxnet/module/module.py", line 428, in bind
       state_names=self._state_names)
     File "/usr/local/lib/python3.5/dist-packages/mxnet/module/executor_group.py", line 237, in __init__
       self.bind_exec(data_shapes, label_shapes, shared_group)
     File "/usr/local/lib/python3.5/dist-packages/mxnet/module/executor_group.py", line 333, in bind_exec
       shared_group))
     File "/usr/local/lib/python3.5/dist-packages/mxnet/module/executor_group.py", line 598, in _bind_ith_exec
       input_shapes = dict(data_shapes)
   TypeError: unhashable type: 'list'
   ```
   
   adding both tests, I believe that the problem is that CSVIter is returning a (sort of) Nx2 matrix, whereas NDArrayIter is producing a 2xN matrix (i.e., CSVIter is producing the transposed version). However, I believe I need the 2xN version of NDArrayIter to produce the embeddings... but I might be wrong. 
   
   Again, and to keep the discussion contextualized, the core of my code currently looks like this:
   
   ```
   f1 = "tmp1"; f2 = "tmp2"
   iter_train = mx.io.CSVIter(data_csv=f1, data_shape=(2,), label_csv=f2, label_shape=(1,), batch_size=1000, data_name=['user','item'])
   user = mx.symbol.Embedding(data=user, input_dim=max_user, output_dim=factor_size)
   item = mx.symbol.Embedding(data=item, input_dim=max_item, output_dim=factor_size)
   pred = mx.symbol.concat(user, item)
   pred = mx.symbol.flatten(pred)
   pred = mx.symbol.FullyConnected(data=pred, num_hidden=64)
   pred = mx.symbol.Activation(data=pred, act_type='relu')
   pred = mx.symbol.FullyConnected(data=pred, num_hidden=1)
   pred = mx.symbol.LinearRegressionOutput(data=pred, label=score)
   model = mx.module.Module(context=mx.cpu(0), data_names=('user','item'), symbol=pred)
   model.fit(iter_train, num_epoch=10, optimizer='adam', optimizer_params=(('learning_rate', 0.001),), eval_metric='mse', eval_data=val_iter)
   ```

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