AI Convergence

import tensorflow as tf

import numpy as np

tf.set_random_seed(777)  # for reproducibility

xy = np.loadtxt('data-03-diabetes.csv', delimiter=',', dtype=np.float32)

x_data = xy[:, 0:-1]

y_data = xy[:, [-1]]

print(x_data.shape, y_data.shape)

# placeholders for a tensor that will be always fed.

X = tf.placeholder(tf.float32, shape=[None, 8])

Y = tf.placeholder(tf.float32, shape=[None, 1])

W = tf.Variable(tf.random_normal([8, 1]), name='weight')

b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(-tf.matmul(X, W)))

hypothesis = tf.sigmoid(tf.matmul(X, W) + b)

# cost/loss function

cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *

                       tf.log(1 - hypothesis))

train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

# Accuracy computation

# True if hypothesis>0.5 else False

predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)

accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))

# Launch graph

with tf.Session() as sess:

    # Initialize TensorFlow variables

    sess.run(tf.global_variables_initializer())

    for step in range(10001):

        cost_val, _ = sess.run([cost, train], feed_dict={X: x_data, Y: y_data})

        if step % 200 == 0:

            print(step, cost_val)

    # Accuracy report

    h, c, a = sess.run([hypothesis, predicted, accuracy],

                       feed_dict={X: x_data, Y: y_data})

    print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)