之前学习过的代码，又敲了一遍，新的收获也还是有的，因为这次注释写的比较详尽，所以再次记录一下，具体的相关知识查阅之前写的文章即可（见上面链接）。
# Author : Hellcat
# Time : 2017/12/6 import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data def xavier_init(fan_in,fan_out, constant = 1):
'''
xavier 权重初始化方式
:param fan_in: 行数
:param fan_out: 列数
:param constant: 常数权重，调节初始化范围的倍数
:return: 初始化后的权重tensor
'''
low = -constant * np.sqrt(6.0 / (fan_in + fan_out))
high = constant * np.sqrt(6.0 / (fan_in + fan_out))
return tf.random_uniform((fan_in, fan_out),
minval=low, maxval=high) class AdditiveGaussianNoiseAutoencoder(): def __init__(self, n_input, n_hidden,
transfer_function=tf.nn.softplus,
optimizer=tf.train.AdamOptimizer(),scale=0.1):
'''
初始化自编码器
:param n_input: 输入层结点数
:param n_hidden: 隐藏层节点数
:param transfer_function: 隐藏层激活函数
:param optimizer: 优化器，是实例化的对象
:param scale: 高斯噪声系数
'''
self.n_input = n_input
self.n_hidden = n_hidden
self.transfer = transfer_function
self.scale = tf.placeholder(tf.float32) # 实际网络中调用的
self.training_scale = scale # 训练用噪声系数
network_weights = self._initialize_weights()
self.weights = network_weights self.x = tf.placeholder(tf.float32, [None, self.n_input])
self.hidden = \
self.transfer(
tf.add(
tf.matmul(
self.x + self.scale * tf.random_normal((n_input,)),
self.weights['w1']),
self.weights['b1'])) # 重建部分没有使用激活函数
self.reconstruction = \
tf.add(
tf.matmul(
self.hidden, self.weights['w2']),
self.weights['b2']) self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction,self.x),2.0))
# 可以将类的实例过程作为实参传入函数
self.optimizer = optimizer.minimize(self.cost) init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init) def _initialize_weights(self):
'''
初始化全部变量
:return: 装有变量的字典
'''
all_weights = dict()
all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))
all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32))
all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype=tf.float32))
all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype=tf.float32))
return all_weights def partial_fit(self, X):
'''
进行单次训练并返回loss
:param X: 训练数据
:return: 本次损失函数值
'''
cost, opt = self.sess.run((self.cost, self.optimizer),
feed_dict={self.x:X, self.scale:self.training_scale})
return cost def calc_totul_cost(self, X):
'''
计算损失函数，不触发训练
:param X: 训练数据
:return: 损失函数
'''
return self.sess.run(self.cost, feed_dict={self.x:X, self.scale:self.training_scale}) def transform(self, X):
'''
返回隐藏层输出结果，目的是获取抽象后的特征
:param X: 训练数据
:return: 隐藏层输出
'''
return self.sess.run(self.hidden, feed_dict={self.x:X, self.scale:self.training_scale}) def generate(self, hidden=None):
'''
通过隐藏层特征重建
:param hidden: 隐藏层特征
:return: 重建数据
'''
if hidden is None:
hidden = np.random.normal(size=[self.n_input])
return self.sess.run(self.reconstruction, feed_dict={self.hidden:hidden}) def reconstruct(self,X):
'''
从原始数据重建
:param X: 训练数据
:return: 重建数据
'''
return self.sess.run(self.reconstruction,
feed_dict={self.x:X, self.scale:self.training_scale}) def getWeights(self):
'''
获取参数值
:return: 隐藏层权重
'''
return self.sess.run(self.weights['w1']) def getBaises(self):
'''
获取参数值
:return: 隐藏层偏置
'''
return self.sess.run(self.weights['b1']) def standard_scale(X_train, X_test):
'''
标准化数据
:param X_train: 训练数据
:param X_test: 测试数据
:return: 标准化之后的训练、测试数据
'''
preprocessor = prep.StandardScaler().fit(X_train)
X_train = preprocessor.transform(X_train)
X_test = preprocessor.transform(X_test)
return X_train, X_test def get_random_block_from_data(data, batch_size):
start_index = np.random.randint(0, len(data) - batch_size)
return data[start_index:(start_index + batch_size)] if __name__ == '__main__':
mnist = input_data.read_data_sets('../../../Mnist_data/',one_hot=True)
X_train, X_test = standard_scale(mnist.train.images, mnist.test.images) n_samples = int(mnist.train.num_examples)
train_epochs = 20
batch_size = 20
display_step = 1 autoencoder = AdditiveGaussianNoiseAutoencoder(
n_input=784,
n_hidden=200,
transfer_function=tf.nn.softplus,
optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
scale=0.01) for epoch in range(train_epochs):
avg_cost = 0.
totu_batch = int(n_samples / batch_size)
for i in range(totu_batch):
batch_xs = get_random_block_from_data(X_train, batch_size) # 单数据块训练并计算损失函数
cost = autoencoder.partial_fit(batch_xs)
avg_cost += cost / n_samples * batch_size if epoch % display_step == 0:
print('epoch : %04d, cost = %.9f' % (epoch + 1,avg_cost)) # 计算测试集上的cost
print('Total coat:',str(autoencoder.calc_totul_cost(X_test)))
部分输出如下：
1.xavier初始化权重方法
2.函数实参可以是class()，即实例化的类