---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-70-78dabd654de9> in <module>()
37 kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
38
---> 39 results = cross_val_score(estimator, X, dummy_y, cv=kfold)
40 print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/model_selection/_validation.py 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.split(X, y, groups))
141 return np.array(scores)[:, 0]
142
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py 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:
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py 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
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py 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)
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
320 # Don't delay the application, to avoid keeping the input
321 # arguments in memory
--> 322 self.results = batch()
323
324 def get(self):
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py 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):
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/sklearn/model_selection/_validation.py 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:
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/keras/wrappers/scikit_learn.py 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
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/keras/models.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)
625 shuffle=shuffle,
626 class_weight=class_weight,
--> 627 sample_weight=sample_weight)
628
629 def evaluate(self, x, y, batch_size=32, verbose=1,
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)
1050 class_weight=class_weight,
1051 check_batch_dim=False,
-> 1052 batch_size=batch_size)
1053 # prepare validation data
1054 if validation_data:
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size)
977 self.internal_input_shapes,
978 check_batch_dim=False,
--> 979 exception_prefix='model input')
980 y = standardize_input_data(y, self.output_names,
981 output_shapes,
/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/keras/engine/training.py in standardize_input_data(data, names, shapes, check_batch_dim, exception_prefix)
109 ' to have shape ' + str(shapes[i]) +
110 ' but got array with shape ' +
--> 111 str(array.shape))
112 return arrays
113
Exception: Error when checking model input: expected dense_input_24 to have shape (None, 4) but got array with shape (135, 5)