Keras Project Example

Download pima indian diabetes data file first.

Python code:

from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8] #input data - except last column
Y = dataset[:,8] # last column is real results
# 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 model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model - train
model.fit(X, Y, epochs=150, batch_size=10, verbose=2)

# evaluate the model
#scores = model.evaluate(X, Y)
#print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

# calculate predictions
predictions = model.predict(X)
rounded = [round(x[0],2) for x in predictions]
print(rounded)

Tested on: python-3.6.3-amd64, Keras API 2

Advertisements

Matrix Operation using Tensorflow

Python code:

import tensorflow as tf

const1 = tf.constant([[1,2,3], [1,2,3]]);
const2 = tf.constant([[3,4,5], [3,4,5]]);

result = tf.add(const1, const2);

with tf.Session() as sess:
output = sess.run(result)
print(output)