Usage instructions - Githubissues

I'm on the current master (the release pip version failed on import), trying to define a callback. Trying to imitate the example in readme.md didn't work out:

hera = HeraCallback('bla', 'localhost', '4000')

...

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
    138                                               train, test, verbose, None,
    139                                               fit_params)
--> 140                       for train, test in cv_iter)
    141     return np.array(scores)[:, 0]
    142

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    756             # was dispatched. In particular this covers the edge
    757             # case of Parallel used with an exhausted iterator.
--> 758             while self.dispatch_one_batch(iterator):
    759                 self._iterating = True
    760             else:

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
    606                 return False
    607             else:
--> 608                 self._dispatch(tasks)
    609                 return True
    610

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
    569         dispatch_timestamp = time.time()
    570         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571         job = self._backend.apply_async(batch, callback=cb)
    572         self._jobs.append(job)
    573

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in apply_async(self, func, callback)
    107     def apply_async(self, func, callback=None):
    108         """Schedule a func to be run"""
--> 109         result = ImmediateResult(func)
    110         if callback:
    111             callback(result)

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in __init__(self, batch)
    324         # Don't delay the application, to avoid keeping the input
    325         # arguments in memory
--> 326         self.results = batch()
    327
    328     def get(self):

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
    129
    130     def __call__(self):
--> 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
    132
    133     def __len__(self):

/Users/olevinkr/anaconda/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
    236             estimator.fit(X_train, **fit_params)
    237         else:
--> 238             estimator.fit(X_train, y_train, **fit_params)
    239
    240     except Exception as e:

/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/wrappers/scikit_learn.pyc in fit(self, X, y, **kwargs)
    146         fit_args.update(kwargs)
    147
--> 148         history = self.model.fit(X, y, **fit_args)
    149
    150         return history

/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)
    650                               shuffle=shuffle,
    651                               class_weight=class_weight,
--> 652                               sample_weight=sample_weight)
    653
    654     def evaluate(self, x, y, batch_size=32, verbose=1,

/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch)
   1109                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1110                               callback_metrics=callback_metrics,
-> 1111                               initial_epoch=initial_epoch)
   1112
   1113     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in _fit_loop(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
    795             'metrics': callback_metrics,
    796         })
--> 797         callbacks.on_train_begin()
    798         callback_model.stop_training = False
    799         self.validation_data = val_ins

/Users/olevinkr/anaconda/lib/python2.7/site-packages/keras/callbacks.pyc in on_train_begin(self, logs)
     72     def on_train_begin(self, logs={}):
     73         for callback in self.callbacks:
---> 74             callback.on_train_begin(logs)
     75
     76     def on_train_end(self, logs={}):

/Users/olevinkr/anaconda/lib/python2.7/site-packages/heraspy/callback.pyc in on_train_begin(self, *args)
     37             {
     38                 'params': self.params,
---> 39                 'modelJson': json.loads(self.model.to_json()),
     40             }
     41         )

TypeError: 'str' object is not callable

keplr-io / hera

Usage instructions #15