ML Lab 05

import tensorflow as tf

x_data = [[1,2],[2,3],[3,1],[4,3],[5,3],[6,2]]
y_data =[[0],[0],[0],[1],[1],[1]]

X= tf.placeholder(tf.float32,shape= [None,2])
Y = tf.placeholder(tf.float32,shape =[None,1])
W = tf.Variable(tf.random_normal([2,1]), name = 'weight')
b = tf.Variable(tf.random_normal([1]),name = 'bias')

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

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