#TensorFlow实现Logistic 回归
import tensorflow as tf

#导入手写数字集
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

#学习参数
learning_rate = 0.01
training_epoches = 25
batch_size = 100
display_step = 1

#构造图
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]))

prediction = tf.nn.softmax(tf.matmul(x,W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(prediction),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_epoches):
        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)
            _,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:","%0.4d" %(epoch+1),"cost","{:.9f}".format(avg_cost))
    print("训练结束")

    correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.int32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))