tf-1.py

import tensorflow as tf


from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 超参数
learning_rate = 0.5 #
epochs = 10
batch_size = 200

# placeholder
# 输入图片为28 x 28 像素 = 784
x = tf.placeholder(tf.float32, [None, 784]) # a 行不定，列是784
# 输出为0-9的one-hot编码
y = tf.placeholder(tf.float32, [None, 10]) # 行不定 列是10

# hidden layer => w, b
W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')
b1 = tf.Variable(tf.random_normal([300]), name='b1')
# output layer => w, b
W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')
b2 = tf.Variable(tf.random_normal([10]), name='b2')

# hidden layer 构造隐层网络
hidden_out = tf.add(tf.matmul(x, W1), b1)
hidden_out = tf.nn.relu(hidden_out)

# 计算输出 构造输出 十分类
y_ = tf.nn.softmax(tf.add(tf.matmul(hidden_out, W2), b2))

y_clipped = tf.clip_by_value(y_, 1e-10, 0.9999999)
cross_entropy = -tf.reduce_mean(tf.reduce_sum(y * tf.log(y_clipped) + (1 - y) * tf.log(1 - y_clipped), axis=1))

# 创建优化器，确定优化目标
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)

# init operator
init_op = tf.global_variables_initializer()

# 创建准确率节点
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver=tf.train.Saver(max_to_keep=1)

# 创建session
with tf.Session() as sess:
	# 变量初始化
	sess.run(init_op)
	total_batch = int(len(mnist.train.labels) / batch_size)
	for epoch in range(epochs):
		avg_cost = 0
		for i in range(total_batch):
			batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)
			_, c = sess.run([optimizer, cross_entropy], feed_dict={x: batch_x, y: batch_y})
			avg_cost += c / total_batch
		print("Epoch:", (epoch + 1), "cost = ", "{:.3f}".format(avg_cost))
	print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))
	saver.save(sess, 'ckpt/mnist.ckpt')