多层感知器

#　coding=utf-8
# !/usr/bin/python


# 只含有一个隐层

#  数据集合来自ｍｎｉｓｔ

from  tensorflow.examples.tutorials.mnist  import input_data
import tensorflow as tf

mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
sess=tf.InteractiveSession()

# 输入节点

in_units = 784
#隐含层节点数
h1_units = 300

"""
初始化参数
"""
# 随机生成正太分布
#tf.truncated_normal(shape, mean, stddev) :shape表示生成张量的维度，mean是均值，stddev是标准差。
W1 = tf.Variable(tf.truncated_normal([in_units,h1_units],stddev = 0.1))
b1 = tf.Variable(tf.zeros([h1_units]))
W2 = tf.Variable(tf.zeros([h1_units,10]))
b2 = tf.Variable(tf.zeros([10]))
x =tf.placeholder(tf.float32,[None,in_units])
keep_prob = tf.placeholder(tf.float32)


# 定义模型

hidden1=tf.nn.relu(tf.matmul(x,W1)+b1)
hidden1_drop=tf.nn.dropout(hidden1,keep_prob)

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


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


cross_entroy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entroy)


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:0.75})


correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))