tensorflow 神经网络入门例子

#encoding:utf-8
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/home/zhaohongjie/machine_learing_action/day3/MNIST_data", one_hot = True)
import tensorflow as tf

learning_rate = 0.001
training_epochs = 20
batch_size = 100
display_step  = 1
n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
n_classes = 10

x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

def multiplayer_perception(x, weigths, biases):
	layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
        layer_1 = tf.nn.relu(layer_1)
        layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
	layer_2 = tf.nn.relu(layer_2)
	
	out_layer = tf.matmul(layer_2,weights['out']) + biases['out']
	return out_layer

weights = {
	'h1' : tf.Variable(tf.random_normal([n_input, n_hidden_1])),
        'h2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
	'out' : tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}

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]))
}

pred = multiplayer_perception(x, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits =  pred, labels = y))
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)

init = tf.global_variables_initializer()
with tf.Session() as sess:
	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_x, batch_y = mnist.train.next_batch(batch_size)
			_, c = sess.run([optimizer, cost],feed_dict = {x : batch_x, y: batch_y})
			avg_cost += c / total_batch
		if epoch % display_step == 0:
			print 'The round %d:' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)
	print "END"

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

该示例来自于公司培训的老师，有些许改动，使程序可以跑得通，是4层神经网络。数据是经典的书籍集合MNIST，手写数字分类数据集。