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Hyperparameter tuning using GridSearchCV and KerasClassifier
  • Last Updated : 26 Nov, 2020

Hyperparameter tuning is done to increase the efficiency of a model by tuning the parameters of the neural network. Some scikit-learn APIs like GridSearchCV and RandomizedSearchCV are used to perform hyper parameter tuning. 

In this article, you’ll learn how to use GridSearchCV to tune Keras Neural Networks hyper parameters. 


  1. We will wrap Keras models for use in scikit-learn using KerasClassifier which is a wrapper.
  2. We will use cross validation using KerasClassifier and GridSearchCV
  3. Tune hyperparameters like number of epochs, number of neurons and batch size.

Implementation of the scikit-learn classifier API for Keras:


   build_fn=None, **sk_params



# import the libraries 
import tensorflow as tf
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler

The dataset can be downloaded from here
Import the dataset using which we’ll predict if a customer stays or leave. 


# The last column is a binary value
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:-1].values
y = dataset.iloc[:, -1].values

Code: Preprocess the data

le = LabelEncoder()
X[:, 2] = le.fit_transform(X[:, 2])
#perform one hot encoding 
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1])], remainder='passthrough')
X = np.array(ct.fit_transform(X))
# perform standardization of the data. 
sc = StandardScaler()
X = sc.fit_transform(X)

To use the KerasClassifier wrapper, we will need to build our model in a function which needs to be passed to the build_fn argument in the KerasClassifier constructor. 


def build_clf(unit):
  # creating the layers of the NN
  ann = tf.keras.models.Sequential()
  ann.add(tf.keras.layers.Dense(units=unit, activation='relu'))
  ann.add(tf.keras.layers.Dense(units=unit, activation='relu'))
  ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
  ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  return ann

Code: create the object of KerasClassifier class


Now we will create the dictionary of the parameters we want to tune and pass as an argument in GridSearchCV. 


params={'batch_size':[100, 20, 50, 25, 32], 
        'nb_epoch':[200, 100, 300, 400],
        'unit':[5,6, 10, 11, 12, 15],
gs=GridSearchCV(estimator=model, param_grid=params, cv=10)
# now fit the dataset to the GridSearchCV object. 
gs =, y)

The best_score_ member gives the best score observed during the optimization procedure and the best_params_ describes the combination of parameters that achieved the best results.




Accuracy:  0.80325

Best Params:  {‘batch_size’: 20, ‘nb_epoch’: 200, ‘unit’: 15}


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