# Lab 9 XOR
import tensorflow as tf
import numpy as np
tf.set_random_seed(777) # for reproducibility
x_data = np.array([0, 0], [0, 1], [1, 0], [1, 1](/8BitsCoding/RobotMentor/wiki/0,-0],-[0,-1],-[1,-0],-[1,-1), dtype=np.float32)
y_data = np.array([0], [1], [1], [0](/8BitsCoding/RobotMentor/wiki/0],-[1],-[1],-[0), dtype=np.float32)
X = tf.placeholder(tf.float32, [None, 2])
Y = tf.placeholder(tf.float32, [None, 1])
W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
b1 = tf.Variable(tf.random_normal([2]), name='bias1')
layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)
W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')
b2 = tf.Variable(tf.random_normal([1]), name='bias2')
hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)
# cost/loss function
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).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([train, cost], feed_dict={X: x_data, Y: y_data})
if step % 100 == 0:
print(step, cost_val)
# Accuracy report
h, p, a = sess.run(
[hypothesis, predicted, accuracy], feed_dict={X: x_data, Y: y_data}
)
print(f"\nHypothesis:\n{h} \nPredicted:\n{p} \nAccuracy:\n{a}")