keras_first_network.py

# 

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]
print(X.shape)
print(Y.shape)

# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

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


# evluate the model
scores = model.evaluate(X, Y)
print("{}: {}".format(model.metrics_names[1], scores[1]*100))

print(X[-1])
# calculate predictions
predictions = model.predict(X)
# round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded)