random_learn_test.py

import tensorflow as tf
import random


W = tf.get_variable('W', shape=[1, 4])
ph_y = tf.placeholder(tf.int32, [1])  # labels

prob = tf.nn.softmax(W)

loss = tf.reduce_mean(
           tf.nn.sparse_softmax_cross_entropy_with_logits(
               logits=W, labels=ph_y
           )
       )

# minimize = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
minimize = tf.train.AdamOptimizer(0.01).minimize(loss)

def get_train_data(n=1):
    ret = []

    for _ in range(n):
        r = random.random()

        if r < .60:
            ret.append(0)
        elif r < .70:
            ret.append(1)
        elif r < .85:
            ret.append(2)
        else:
            ret.append(3)

    return ret

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

for i in range(100000):
    ys = get_train_data(1)
    ret = sess.run([loss, minimize], feed_dict={ph_y: ys})

    if i % 1000 == 0:
        print(i, ret, sess.run(prob))