cnn_mnist

#导入TensorFlow
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#加载数据集
mnist = input_data.read_data_sets('./MNIST', one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
#定义weight和bias
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)
#定义卷积层和池化层
def conv2d(x, W):
	return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
	return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#reshape image数据
x_image = tf.reshape(x, [-1, 28, 28, 1])

#搭建一个卷积层
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

#第二层卷积
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

#构建全连接层
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
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)

#添加Dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_dropout = tf.nn.dropout(h_fc1, keep_prob)

#输入层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_dropout, W_fc2) + b_fc2

#训练和评估
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y_, 1), tf.argmax(y_conv, 1)), tf.float32))
init = tf.global_variables_initializer()
tf.summary.scalar(name='loss',tensor=cross_entropy)
loss_opts = tf.summary.merge_all()
with tf.Session() as sess:
	sess.run(init)
	summary_writer = tf.summary.FileWriter(logdir='./logs', graph=sess.graph)
	for i in range(30000):
		batch = mnist.train.next_batch(50)
		if i % 100 == 0:
			train_accuracy = accuracy.eval(feed_dict = {x:batch[0], y_:batch[1], keep_prob:1.})
			loss_val = sess.run(loss_opts, feed_dict = {x:batch[0], y_:batch[1], keep_prob:1.})
			summary_writer.add_summary(loss_val, global_step=i)
			print("step {}, the train accuracy:{}".format(i, train_accuracy))
	test_accuracy = accuracy.eval(feed_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.})
	print("the test accuracy: {}".format(test_accuracy))
	saver = tf.train.Saver()
	path = saver.save(sess, './my_net/mnist_deep.ckpt')
	print("save path:{}".format(path))