Adding dropout to prevent overfitting

Another popular method for regularization is dropout. A forces a neural network to learn multiple independent representations by randomly removing connections between neurons in the learning phase. For example, when using a dropout of 0.5, the network has to see each example twice before the connection is learned. Therefore, a network with dropout can be seen as an ensemble of networks. 

In the following recipe, we will improve a model that clearly overfits the training data by adding dropouts.

How to do it...

1. Import the as follows:

import numpy as np 
import pandas as pd
from sklearn.model_selection import train_test_split

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline


import numpy as np...