# Author : Hellcat

# Time   : 2017/12/7

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('../../../Mnist_data',one_hot=True)

sess = tf.InteractiveSession()

in_units = 784

h1_units = 300

# 对于ReLU激活函数，常用截断正态分布，避免0梯度和完全对称

# 对于Softmax分类（也就是sigmoid激活），由于对0附近最敏感，所以采用全0初始权重

W1 = tf.Variable(tf.truncated_normal([in_units, h1_units],stddev=0.1))

b1 = tf.Variable(tf.zeros([h1_units], dtype=tf.float32))

W2 = tf.Variable(tf.zeros([h1_units, 10], dtype=tf.float32))

b2 = tf.Variable(tf.zeros([10], dtype=tf.float32))

x = tf.placeholder(tf.float32, [None, in_units])

y_ = tf.placeholder(tf.float32, [None, 10])

keep_prob = tf.placeholder(tf.float32)

hidden1 = tf.nn.relu(tf.add(tf.matmul(x, W1), b1))

hidden1_drop = tf.nn.dropout(hidden1, keep_prob)

y = tf.nn.softmax(tf.add(tf.matmul(hidden1_drop, W2), b2))

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), axis=1))

# train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y,axis=1), tf.argmax(y_,axis=1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tf.global_variables_initializer().run()

for i in range(3000):

    batch_xs, batch_ys = mnist.train.next_batch(100)

    train_step.run({x:batch_xs, y_:batch_ys, keep_prob:0.5})

    if i % 100 == 0:

        print('当前迭代次数{0}，当前准确率{1:.3f}'.

              format(i,accuracy.eval({x:batch_xs, y_:batch_ys, keep_prob:1.0})))

print(accuracy.eval({x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0}))