pythonlogistic 회귀 알고리즘 실현

'''
logistic 
'''

from __future__ import  print_function

import tensorflow as tf

# MNIST 
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/",one_hot=True)

# 
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

#tf 
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])

# 
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

# 
pred = tf.nn.softmax(tf.matmul(x,W) + b)

# 
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred),reduction_indices=1))

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

# 
init = tf.global_variables_initializer()

# 
with tf.Session() as sess:

    # 
    sess.run(init)

    # 
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / batch_size)

        # 
        for i in range(total_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            # （backprop） （ ）
            _, c = sess.run([optimizer,cost],feed_dict={x:batch_xs,y:batch_ys})
            # 
            avg_cost += c / total_batch
        # 
        if (epoch + 1) % display_step == 0:
            print("Epoch:",'%04d' % (epoch + 1),"cost=","{:.9f}".format(avg_cost))
    print(" ")
    # 
    correct_prediction = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
    # 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    print("Accuracy:",accuracy.eval({x:mnist.test.images,y:mnist.test.labels}))