Example#1-9.py

import tensorflow as tf
x_data = [[1, 2, 1, 1], [2, 1, 3, 4], [3, 1, 3, 4], [4, 1, 5, 5], [1, 7, 5, 5], [1, 2, 5, 6], [1, 6, 6, 6], [1, 7, 7, 7]]
y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]

X = tf.placeholder("float", [None, 4])
Y = tf.placeholder("float", [None, 3])
nb_classes = 3 # number of y label

W = tf.Variable(tf.random_normal([4, nb_classes]), name='weight')
b = tf.Variable(tf.random_normal([nb_classes]), name='bias')

# tf.nn.softmax computes softmax activations
# softmax = exp(Logits) / reduce_sum(exp(Logits), dim)
hypothesis = tf.nn.softmax(tf.matmul(X, W)+b)

# Cross entropy cost/Loss
cost = tf. reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Launch graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for step in range(2001):
        sess.run(optimizer, feed_dict={X: x_data, Y: y_data})
        if step % 200 == 0:
            print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}))

    print('--------------')

    # Testing & One-hot encoding
    a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9]]})
    print(a, sess.run(tf.arg_max(a, 1)))

    print('--------------')

    b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]})
    print(b, sess.run(tf.arg_max(b, 1)))

    print('--------------')

    c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]})
    print(c, sess.run(tf.arg_max(c, 1)))

    print('--------------')

    all = sess.run(hypothesis, feed_dict={
                   X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]})
    print(all, sess.run(tf.arg_max(all, 1)))