ex4

# Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy


# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)


# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.data.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='adam', metrics=['accuracy'])


# Fit the model
model.fit(X, Y, epochs=150, batch_size=10,  verbose=2)


# calculate predictions
predictions = model.predict(X)


# round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded)