5-1-softmax-classifier.py

import tensorflow as _tf
import matplotlib.pyplot as plt
import numpy as np

tf = _tf.compat.v1
tf.disable_v2_behavior()

x_data = [
    [1, 2, 1, 1],
    [2, 1, 3, 2],
    [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

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

logits = tf.matmul(X, W) + b
hypothesis = tf.nn.softmax(logits)

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

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}))

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