05_convolutional_net4.py

#-*- coding:utf-8 -*-
# 两层卷积网络 识别 手写字体
import tensorflow as tf
import numpy as np
 
# 下载mnist数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/tmp/', one_hot=True)
 
 
n_output_layer = 10
 
# 定义待训练的神经网络
def convolutional_neural_network(data):
	weights = {'w_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
              'w_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
              'w_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
              'out':tf.Variable(tf.random_normal([1024,n_output_layer]))}
 
	biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
              'b_conv2':tf.Variable(tf.random_normal([64])),
              'b_fc':tf.Variable(tf.random_normal([1024])),
              'out':tf.Variable(tf.random_normal([n_output_layer]))}
 
	data = tf.reshape(data, [-1,28,28,1])
 
	conv1 = tf.nn.relu(tf.add(tf.nn.conv2d(data, weights['w_conv1'], strides=[1,1,1,1], padding='SAME'), biases['b_conv1']))
	conv1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
 
	conv2 = tf.nn.relu(tf.add(tf.nn.conv2d(conv1, weights['w_conv2'], strides=[1,1,1,1], padding='SAME'), biases['b_conv2']))
	conv2 = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
 
	fc = tf.reshape(conv2, [-1,7*7*64])
	fc = tf.nn.relu(tf.add(tf.matmul(fc, weights['w_fc']), biases['b_fc']))
 
	# dropout剔除一些"神经元"
	#fc = tf.nn.dropout(fc, 0.8)
 
	output = tf.add(tf.matmul(fc, weights['out']), biases['out'])
	return output
 
 
# 每次使用100条数据进行训练
batch_size = 100
 
X = tf.placeholder('float', [None, 28*28]) 
Y = tf.placeholder('float')
# 使用数据训练神经网络
def train_neural_network(X, Y):
	predict = convolutional_neural_network(X)
	cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(predict, Y))
	optimizer = tf.train.AdamOptimizer().minimize(cost_func)  # learning rate 默认 0.001 
 
	epochs = 1
	with tf.Session() as session:
		session.run(tf.initialize_all_variables())
		epoch_loss = 0
		for epoch in range(epochs):
			for i in range( int(mnist.train.num_examples/batch_size) ):
				x, y = mnist.train.next_batch(batch_size)
				_, c = session.run([optimizer, cost_func], feed_dict={X:x,Y:y})
				epoch_loss += c
			print(epoch, ' : ', epoch_loss)
 
		correct = tf.equal(tf.argmax(predict,1), tf.argmax(Y,1))
		accuracy = tf.reduce_mean(tf.cast(correct,'float'))
		print('准确率: ', accuracy.eval({X:mnist.test.images, Y:mnist.test.labels}))
 
train_neural_network(X,Y)