ValueError when calling cv.fit() for optimising a neural network

[tbloch1]

Hi,

I am trying to optimise a neural network (Keras, TensorFlow), but I'm getting an error:
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

I have checked my input data for NaNs, infities and large or small values. There aren't any.
I have forced the input data to be np.float32 before passing it to .fit().

I've used this algorithm before without any problems or special data prep, so I'm not sure where there error is creeping in.

the relavent bit of the code is:
codetxt.txt

I should also say that when I manually try to just .fit() to my model, it works fine. The issue is something to do with how the cross valdation is working.

The full traceback is:

Traceback (most recent call last):
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/multiprocessing/pool.py", line 44, in mapstar
return list(map(*args))
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/evolutionary_search/cv.py", line 104, in _evalFunction
error_score=error_score)[0]
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 568, in _fit_and_score
test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 610, in _score
score = scorer(estimator, X_test, y_test)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 98, in call
**self._kwargs)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/metrics/regression.py", line 239, in mean_squared_error
y_true, y_pred, multioutput)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/metrics/regression.py", line 77, in _check_reg_targets
y_pred = check_array(y_pred, ensure_2d=False)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/utils/validation.py", line 573, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/sklearn/utils/validation.py", line 56, in _assert_all_finite
raise ValueError(msg_err.format(type_err, X.dtype))
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "NN_GSCV-DL2.py", line 308, in
grid_result = cv.fit(X_train, y_train)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/evolutionary_search/cv.py", line 363, in fit
self._fit(X, y, possible_params)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/evolutionary_search/cv.py", line 453, in _fit
halloffame=hof, verbose=self.verbose)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/site-packages/deap/algorithms.py", line 150, in eaSimple
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/multiprocessing/pool.py", line 266, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/home/users/hf832176/.conda/envs/tb_env6/lib/python3.6/multiprocessing/pool.py", line 644, in get
raise self._value
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').