L4 PID w Keras Wrappers.py

## using KerasClassifier to wrap deep learning models

#import libraries
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.cross_validation import StratifiedKFold
from sklearn.cross_validation import cross_val_score
import numpy
import pandas

#define a function which creates a model.
#KerasClassifier requires this function to be passed in as argument
def create_model():
    #create model
    model = Sequential()
    # 8 input - 12 neurons hidden layer 1 - 8 neurons in hidden layer 2 - 1 output
    model.add(Dense(12, input_dim = 8, init = 'uniform', activation = 'relu'))
    model.add(Dense(8, init = 'uniform', activation = 'relu'))
    model.add(Dense(1, init = 'uniform', activation = 'sigmoid'))
    #compile model
    model.compile(loss='binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
    return model

#fix seed
seed = 7
numpy.random.seed(seed)
#load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.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 = 150, batch_size = 10)

#evaluate using 10-fold cross validation
kfold = StratifiedKFold(y=Y, n_folds=10, shuffle=True, random_state=seed)
results = cross_val_score(model, X, Y ,cv = kfold)
print(results.mean())