fromkl.py

import tensorflow as tf
import numpy as np

xTrain = np.array([[1,0,1],
                   [0,0,0],
                   [1,1,0],
                   [1,0,0],
                   [0,0,1]])
                   
yTrain = np.array([
                  [0,1],
                  [1,0],
                  [1,0],
                  [1,0],
                  [1,0]])

xTest = np.array([[0,1,1],
                  [1,1,1],
                  [0,1,0]])

yTest = np.array([[1,0],
                  [0,1],
                  [1,0]])

x = tf.placeholder(dtype=tf.float32,shape=[None,3])
y = tf.placeholder(dtype=tf.float32,shape=[None,2])

# Placeholder is just like a promise for the declaration of a datatype

w0 = tf.Variable(initial_value=tf.truncated_normal([3,2],stddev=0.5))
b0 = tf.Variable(tf.zeroes([2]))

mult = tf.matmul(x,w0) + b0

cross = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_test, logits = multi))
#Cross entropy or loss function means how poor the function is performing
#Reduce means the average of the elements of the array

mini = tf.train.GradientDescentOptimizer(0.1).minimize(cross)

# (0.1) is hyper parameter

sess = tf.Session()
sess.run(tf.global_variables_initializer())

#Initializer initializes all the vaeiables in the program

def accuracy():
    print('Testing on \n',x_test)
    print('Expected output :  \n',y_test)
    print('Predicted output :  \n',sess.run(tf.sigmoid(mult),feed_dict={x:x_test}))
 
print('Before running : ')
accuracy()

for i in range(10000):
    sess.run(mini, feed_dict={x:x_train,y:y_train})
    if i%500 == 0:
        print("Cross Entropy at {0:.3f} is {1:.3f}".format(i,sess.run(cross,feed_dict={x:x_train,y:y_train})))

accuracy()
sess.close()