from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
# load pima indians dataset
dataset = np.loadtxt("pima.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
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='sgd', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=400, batch_size=10,  verbose=2)
# calculate predictions
predictions = model.predict(X)
# round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded)
(rounded == Y).sum()/len(Y)