本文主要是介绍【深度学习笔记3.1 正则化】权重衰减(weight decay)，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
权重衰减是什么？参考有关文献
这里参考文献[1]整理成如下代码：(详见文献[5]regularization/WeightDecay.py)
import numpy as np
import tensorflow as tffrom matplotlib import pyplot as pltn_train = 20
n_test = 100
num_inputs = 200true_w = np.ones((num_inputs, 1)) * 0.01
true_b = 0.05
features = np.random.normal(size=(n_train+n_test, num_inputs))
np.random.shuffle(features)
labels = np.dot(features, true_w) + true_b
labels += np.random.normal(scale=0.01, size=labels.shape)train_features, test_features = features[:n_train], features[n_train:]
train_labels, test_labels = labels[:n_train], labels[n_train:]batch_size = 1
epochs = 100
learning_rate = 0.003
lambd = 5x = tf.placeholder(tf.float32, shape=(None, num_inputs))
y = tf.placeholder(tf.float32, shape=(None, 1))w = tf.Variable(tf.random_normal((num_inputs, 1)))
b = tf.Variable(tf.zeros(1))
y_hat = tf.add(tf.matmul(x, w), b)loss = tf.reduce_mean(tf.square(y-y_hat)) + lambd * (tf.reduce_sum(tf.pow(w, 2)) / 2)
train = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)dataset = tf.data.Dataset.from_tensor_slices((train_features, train_labels))
dataset = dataset.repeat().batch(batch_size)
iterator = dataset.make_initializable_iterator()
next_data = iterator.get_next()train_loss = []
test_loss = []
init = [tf.global_variables_initializer(), iterator.initializer]
with tf.Session() as sess:sess.run(init)for i in range(epochs):for _ in range(20):batch_x, batch_y = sess.run(next_data)sess.run(train, feed_dict={x: batch_x,y: batch_y})train_loss.append(sess.run(loss, feed_dict={x: train_features,y: train_labels}))test_loss.append(sess.run(loss, feed_dict={x: test_features,y: test_labels}))plt.semilogy(range(1, epochs+1), train_loss)
plt.semilogy(range(1, epochs+1), test_loss)
plt.xlabel('epochs')
plt.ylabel('loss')
plt.show()
参考文献
[1] 使用 TensorFlow 了解權重衰減
[2] L2正则=Weight Decay？并不是这样
[3] 【TensorFlow-windows】(五) CNN（卷积神经网络）对cifar10的识别
[4] 动手学深度学习->权重衰减
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