'''
logistic迴歸函式
'''

from __future__ import  print_function

import tensorflow as tf

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

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

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

#設定模型權重
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

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

#交叉熵最小化誤差
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred),reduction_indices=1))

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

#初始化變數
init = tf.global_variables_initializer()

#開始訓練
with tf.Session() as sess:

    #開始初始化
    sess.run(init)

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

        #迴圈
        for i in range(total_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            #執行優化操作(backprop)和成本操作(獲取損失值)
            _, c = sess.run([optimizer,cost],feed_dict={x:batch_xs,y:batch_ys})
            #計算平均損失
            avg_cost += c / total_batch
        #顯示每步日誌
        if (epoch + 1) % display_step == 0:
            print("Epoch:",'%04d' % (epoch + 1),"cost=","{:.9f}".format(avg_cost))
    print("優化完成")
    #測試模型
    correct_prediction = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
    #計算準確度
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    print("Accuracy:",accuracy.eval({x:mnist.test.images,y:mnist.test.labels}))
.