tensorflow 动态学习率

实现一：来自：点击打开链接

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# 每个批次的大小
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

# 定义两个placeholder
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
lr = tf.Variable(0.001, dtype=tf.float32)    #这里加上了一个学习率的变化

# 创建一个简单的神经网络
W1 = tf.Variable(tf.truncated_normal([784, 500], stddev=0.1))
b1 = tf.Variable(tf.zeros([500]) + 0.1)
L1 = tf.nn.tanh(tf.matmul(x, W1) + b1)
L1_drop = tf.nn.dropout(L1, keep_prob)

W2 = tf.Variable(tf.truncated_normal([500, 300], stddev=0.1))
b2 = tf.Variable(tf.zeros([300]) + 0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2)
L2_drop = tf.nn.dropout(L2, keep_prob)

W3 = tf.Variable(tf.truncated_normal([300, 10], stddev=0.1))
b3 = tf.Variable(tf.zeros([10]) + 0.1)
prediction = tf.nn.softmax(tf.matmul(L2_drop, W3) + b3)

# 交叉熵代价函数
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
# 训练
train_step = tf.train.AdamOptimizer(lr).minimize(loss)

# 初始化变量
init = tf.global_variables_initializer()

# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一维张量中最大的值所在的位置
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(51):
        sess.run(tf.assign(lr, 0.001 * (0.95 ** epoch)))
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0})

        learning_rate = sess.run(lr)
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})
        print("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc) + ", Learning Rate= " + str(learning_rate))

运行的结果

实现二： 来自点击打开链接

tensorflow提供了一个灵活的学习率设置方法，指数衰减函数tf.train.exponential_decay()

decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) 

DEMO：

current_epoch = tf.Variable(0)
loss = ...  
learning_rate = tf.train.exponential_decay(0.001,  
                                           current_epoch,  
                                           decay_steps=num_epochs,  
                                           decay_rate=0.03)
train_step = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss,global_step=current_epoch)  
...  
with tf.Session() as sess:  
    init = tf.global_variables_initializer()  
    sess.run(init)  
    for i in range(num_epochs):  
        current_epoch = i  
        ...