06-1. Softmax classfier

■ Softmax 구현 (Multinomial Logistic Regression)

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

- hypothesis 에서 tf.matmul(X,W)+b 에 tf.nn.softmax 함수 적용

cost = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(hypothesis),axis=1))

- Cross Entropy 를 코스트 함수로 사용한다.

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

- learining_rate 를 0.1로 학습함.

# Lab06-1 Softmax Classifier
 
import tensorflow as tf
 
tf.set_random_seed(777)  # for reproducibility
 
 
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')
 
 
# 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.argmax(a, 1)))
 
 
    print('--------------')
 
 
    b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]})
 
    print(b, sess.run(tf.argmax(b, 1)))
 
 
    print('--------------')
 
 
    c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]})
 
    print(c, sess.run(tf.argmax(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.argmax(all, 1)))