test2.py

import numpy
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier

from keras.wrappers.scikit_learn import BaseWrapper
import copy
def custom_get_params(self, **params):
    res = copy.deepcopy(self.sk_params)
    res.update({'build_fn': self.build_fn})
    return res

BaseWrapper.get_params = custom_get_params


# Function to create model, required for KerasClassifier
def create_model(optimizer='adam'):
	# create model
	model = Sequential()
	model.add(Dense(12, input_dim=8, activation='relu'))
	model.add(Dense(1, activation='sigmoid'))
	# Compile model
	model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
	return model
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
dataset = numpy.loadtxt("0207.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = KerasClassifier(build_fn=create_model, nb_epoch=50, batch_size=10, verbose=0)
# define the grid search parameters
optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
param_grid = dict(optimizer=optimizer)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
grid_result = grid.fit(X, Y)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))