多层感知机识别手写体数字

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 @date 2018/08/09 20:08:45
"""

import sys
import numpy as np
import sklearn.preprocessing as prep
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
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)

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.float32,10])
cross_entropy = tf.reduce_mean(- tf.reduce_sum(y_ * tf.log(y),reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)

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}))

if __name__ == ‘__main__‘:
    pass