tensorflow笔记----3---ANN对mnist数据集分类

tensorfllow实现两层MLP对mnist分类，第一层256个神经元，第二层128个神经元，输入784，输出10分类
#! /usr/bin/python
# -*-coding:utf-8 -*-
__author__ = "chunming"
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist= input_data.read_data_sets('data/', one_hot=True)
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print (trainimg.shape)
print (trainlabel.shape)
print (testimg.shape)
print (testlabel.shape)
print (trainimg)
print (trainlabel[0])

n_hidden_1 = 256
n_hidden_2 = 128
n_input    = 784
n_classes  = 10
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

stddev = 0.1
weights = {
    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)),#正太分布的标准差为0.1
    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def multilayer_perceptron(_X, _weights, _biases):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['w1']), _biases['b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['w2']), _biases['b2']))
    return (tf.matmul(layer_2, _weights['out']) + _biases['out'])

pred = multilayer_perceptron(x, weights, biases)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=pred,name="loss"))
optm = tf.train.GradientDescentOptimizer(0.001)
train=optm.minimize(loss)
corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accr = tf.reduce_mean(tf.cast(corr, "float"))

init = tf.global_variables_initializer()
training_epochs = 500
batch_size = 100
display_step = 5
sess = tf.Session()
sess.run(init)
for epoch in range(training_epochs):
    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)
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(train, feed_dict=feeds)
        avg_cost += sess.run(loss, feed_dict=feeds)
    avg_cost = avg_cost / total_batch
    if epoch % display_step == 0:
        print("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print("TRAIN ACCURACY: %.3f" % (train_acc))
        feedstest = {x: mnist.test.images, y: mnist.test.labels}
        test_acc = sess.run(accr, feed_dict=feedstest)
        print("TEST ACCURACY: %.3f" % (test_acc))