TensorFlow神经网络优化

学习率

#实现指数衰减学习率
global_step = tf.Variable(0)

#实现的功能类似于
#decay_learning_rate = learning_rate * decay_rate ^^ (global_step / decay_step)
#staircase的作用：是否对(global_step / decay_step)取整
#参数：learning_rate，global_step，decay_step，decay_rate
learning_rate = tf.trainexponential_decay(
    0.1,global_step,100,0.96,staircase=True)

#传入global_step使其自动更新
learning_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(
    loss,global_step=global_step)

正则化

损失函数变为：

J(θ)+λR(w)$J(\theta )+\lambda R(w)$

L1正则化：

R(w)=∑i|wi|$R(w)=\sum _i|w_i|$

L2正则化：

R(w)=∑i|w2i|$R(w)=\sum _i|w_i^2|$

也可以一起使用：

R(w)=∑iα|wi|+(1−α)w2i$R(w)=\sum _i\alpha |w_i|+(1-\alpha )w_i^2$

实例：

loss = tf.reduce_mean(tf.square(y_ - y)) + 
    tf.contrib.layers.l2_regularizer(0.5)(weights)

使用集合管理多个loss

#将各个loss添加到losses集合中
tf.add_to_collection('losses',loss1)
tf.add_to_collection('losses',loss2)
#获取集合中的所有loss，并将其相加，获得所有loss之和
total_loss = tf.add_n(tf.get_collection('losses'))

滑动平均

计算公式

shadow_variable=decay∗shadow_variable+(1−decay)∗variable$shadow\mathrm{\_}variable=decay\ast shadow\mathrm{\_}variable+(1-decay)\ast variable$

decay=min{decay,1+num_updates10+num_updates}$decay=min\{decay,\frac{1+num\mathrm{\_}updates}{10+num\mathrm{\_}updates}\}$

v = tf.Variable(0,dtype=tf.float32)
global_step = tf.Variable(0)
#参数：dacay,global_step
ema = tf.train.ExponentialMovingAverage(0.99,step)
ema_op = ema.apply([v])

sess.run(tf.assign(v,5))
#先计算衰减率，为min{0.99,(1+0)/(10+0)=0.1}=0.1
#故滑动平均为0.1 * 0 + 0.9 * 5 = 4.5
sess.run(ema_op)
print(sess.run(ema.average(v)))

sess.run(tf.assign(step,10000))
sess.run(tf.assign(v,10))
#同理，衰减率为min{0.99,(1+10000)/(10+10000)=0.999}=0.99
#滑动平均为0.99 * 4.5 + 0.01 * 10 = 4.555
sess.run(tf.ema_op)
print(sess.run(ema.average(v)))

实例

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

input_size = 784
output_size = 10
hidden_size = 500

batch_size = 100

learning_rate_base = 0.8
learning_rate_decay = 0.99

regularization_rate = 0.0001
training_steps = 30000
moving_average_decay = 0.99

def inference(input_tensor,ema,w1,b1,w2,b2):
    if ema is None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor,w1)+b1)
        layer2 = tf.matmul(layer1,w2)+b2
        return layer2
    else:
        layer1 = tf.nn.relu(tf.matmul(input_tensor,ema.average(w1))+ema.average(b1))
        layer2 = tf.matmul(layer1, ema.average(w2)) + ema.average(b2)
        return layer2

def train(mnist):
    x = tf.placeholder(tf.float32,[None,input_size])
    y_ = tf.placeholder(tf.float32,[None,output_size])

    w1 = tf.Variable(tf.truncated_normal([input_size,hidden_size],stddev=0.1))
    b1 = tf.Variable(tf.constant(0.1,shape=[hidden_size]))
    w2 = tf.Variable(tf.truncated_normal([hidden_size,output_size],stddev=0.1))
    b2 = tf.Variable(tf.constant(0.1, shape=[output_size]))

    y = inference(x,None,w1,b1,w2,b2)

    global_step = tf.Variable(0,trainable=False)

    #滑动平均
    ema = tf.train.ExponentialMovingAverage(moving_average_decay,global_step)
    ema_op = ema.apply(tf.trainable_variables())

    average_y = inference(x,ema,w1,b1,w2,b2)

    #交叉熵
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.arg_max(y_,1))
    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    #L2正则化
    regularizer = tf.contrib.layers.l2_regularizer(regularization_rate)
    regularization = regularizer(w1)+regularizer(w2)
    #损失函数
    loss = cross_entropy_mean + regularization

    #指数衰减的学习率
    learning_rate = tf.train.exponential_decay(
        learning_rate_base,
        global_step,
        mnist.train.num_examples/batch_size,
        learning_rate_decay
    )

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
    #将两个操作同时进行
    train_op = tf.group(train_step,ema_op)

    #使用滑动平均模型计算准确率
    correct_prediction = tf.equal(tf.arg_max(average_y,1),tf.arg_max(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        validate_feed = {x:mnist.validation.images,y_:mnist.validation.labels}
        test_feed = {x:mnist.test.images,y_:mnist.test.labels}

        for i in range(training_steps):
            if i % 1000 == 0:
                validate_acc = sess.run(accuracy,feed_dict=validate_feed)
                print('{} steps, validation acc: {}'.format(i,validate_acc))

            xs,ys = mnist.train.next_batch(batch_size)
            sess.run(train_op,feed_dict={x:xs,y_:ys})

        test_acc = sess.run(accuracy,feed_dict=test_feed)
        print('finally,test acc:{}'.format(test_acc))

def main(argv=None):
    mnist = input_data.read_data_sets('MNIST_data',one_hot=True)
    train(mnist)

if __name__ == '__main__':
    #运行main函数
    tf.app.run()