cnn for mnist

	#代码可运行，准确率高达97% #tensorflow实现的用cnn处理mnist数据集，2个卷积层，2个全连接层，注意tf.reshape(...)函数的用法
	"""
	Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
	"""
	from __future__ import print_function
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data
	# number 1 to 10 data
	mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
	def compute_accuracy(v_xs, v_ys):
	global prediction
	y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
	correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
	accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
	result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
	return result
	def weight_variable(shape):
	initial = tf.truncated_normal(shape, stddev=0.1)
	return tf.Variable(initial)
	def bias_variable(shape):
	initial = tf.constant(0.1, shape=shape)
	return tf.Variable(initial)
	def conv2d(x, W):
	# stride [1, x_movement, y_movement, 1]
	# Must have strides[0] = strides[3] = 1
	return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
	def max_pool_2x2(x):
	# stride [1, x_movement, y_movement, 1]
	return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
	# define placeholder for inputs to network
	xs = tf.placeholder(tf.float32, [None, 784])/255. # 28x28
	ys = tf.placeholder(tf.float32, [None, 10])
	keep_prob = tf.placeholder(tf.float32)
	x_image = tf.reshape(xs, [-1, 28, 28, 1])
	# print(x_image.shape) # [n_samples, 28,28,1]
	## conv1 layer ##
	W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32
	b_conv1 = bias_variable([32])
	h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32
	h_pool1 = max_pool_2x2(h_conv1) # output size 14x14x32
	## conv2 layer ##
	W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64
	b_conv2 = bias_variable([64])
	h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64
	h_pool2 = max_pool_2x2(h_conv2) # output size 7x7x64
	## fc1 layer ##
	W_fc1 = weight_variable([7*7*64, 1024])
	b_fc1 = bias_variable([1024])
	# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64]
	h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
	h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
	h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
	## fc2 layer ##
	W_fc2 = weight_variable([1024, 10])
	b_fc2 = bias_variable([10])
	prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
	# the error between prediction and real data
	cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
	reduction_indices=[1])) # loss
	train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
	sess = tf.Session()
	# important step
	# tf.initialize_all_variables() no long valid from
	# 2017-03-02 if using tensorflow >= 0.12
	if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
	init = tf.initialize_all_variables()
	else:
	init = tf.global_variables_initializer()
	sess.run(init)
	for i in range(1000):
	batch_xs, batch_ys = mnist.train.next_batch(100)
	sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5})
	if i % 50 == 0:
	print(compute_accuracy(
	mnist.test.images[:1000], mnist.test.labels[:1000]))