lab 09.4 xor tensorboard

# lab 9.XOR
import tensorflow as tf
import numpy as np

tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.01

x_data = [[0, 0],
          [0, 1],
          [1, 0],
          [1, 1]]
y_data = [[0],
          [1],
          [1],
          [0]]
x_data = np.array(x_data, dtype=np.float32)
y_data = np.array(y_data, dtype=np.float32)

X = tf.placeholder(tf.float32, [None, 2], name='x-input')
Y = tf.placeholder(tf.float32, [None, 1], name='y-input')

with tf.name_scope("layer1"):
    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)

    w1_hist = tf.summary.histogram("weights1", W1)
    b1_hist = tf.summary.histogram("biases1", b1)
    layer1_hist = tf.summary.histogram("layer1", layer1)


with tf.name_scope("layer2"):
    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)

    w2_hist = tf.summary.histogram("weights2", W2)
    b2_hist = tf.summary.histogram("biases2", b2)
    hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)

# cost/loss function
with tf.name_scope("cost"):
    cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                           tf.log(1 - hypothesis))
    cost_summ = tf.summary.scalar("cost", cost)

with tf.name_scope("train"):
    train = tf.train.AdamOptimizer(learning_rate=learning_rate).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))
accuracy_summ = tf.summary.scalar("accuracy", accuracy)

# Launch graph
with tf.Session() as sess:
    # tensorboard --logdir=./logs/xor_logs
    merged_summary = tf.summary.merge_all()
    writer = tf.summary.FileWriter("./logs/xor_logs_r0_01")
    writer.add_graph(sess.graph)  # Show the graph

    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())

    for step in range(10001):
        summary, _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})
        writer.add_summary(summary, global_step=step)

        if step % 100 == 0:
            print(step, sess.run(cost, feed_dict={
                  X: x_data, Y: y_data}), sess.run([W1, W2]))

    # Accuracy report
    h, c, a = sess.run([hypothesis, predicted, accuracy],
                       feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)


'''
Hypothesis:  [[  6.13103184e-05]
 [  9.99936938e-01]
 [  9.99950767e-01]
 [  5.97514772e-05]]
Correct:  [[ 0.]
 [ 1.]
 [ 1.]
 [ 0.]]
Accuracy:  1.0
'''