自编码器简介
深度学习在早期一度被认为是一种无监督的特征学(Unsupervised Feature Learning),模仿人脑对特征逐层抽象提取的过程
1.无监督学习：不需要对标注数据就可以对数据进行一定程度的学习，这种学习是对数据内容的组织形式的学习，提取的是频繁出现的特征
2.逐层抽象：特征是需要不断抽象的，就像人总是从简单基础概念开始学习，再到复杂的概念。深度学习也是一样，他从最简单的微观特征开始，不断抽象特征的层级，逐渐往复杂的宏观特征转变。
- 自编码器(AUtoEncoder)，顾名思义，既可以使用自身的高阶特征编码自己。
- 自编码器其实也是一种神经网络，它的输入和输出是一致的，它借助稀疏编码的思想，目标是使用稀疏的一些高阶特征重新组合来重构自己。特点如下：
1.期望输入和输出一致
2.希望使用高阶特征来重构自己，而不只是复制像素点
TensorFlow 实现自编码器
- 实现标准的均匀分布的Xavier初始化器
def xavier_init(fan_in,fan_out,constant = 1):
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,dtype = tf.float32)
- 定义去躁自编码器的class
class AddAutoencoder(object):
# - n_input:输入变量数
# - n_hidden:隐含层节点数
# - transfer_function:隐含层激活函数，默认为softplus
# - optimizer:优化器，默认为Adam
# - scale:高斯噪声系数，默认为0.1
def __init__(self,n_input,n_hidden,transfer_function = tf.nn.softplus,
optimizer = tf.train.AdamOptimizer(),scale = 0.1):
self.n_input = n_input
self.n_hidden = n_hidden
self.transfer = transfer_function
self.scale = tf.placeholder(tf.float32)
self.training_scale = scale
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+scale*tf.random_normal((n_input,)),
self.weights['w1']),self.weights['b1']))
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))
self.optimizer = optimizer.minimize(self.cost)
init = tf.global_variables_initializer()
self.sess = tf.Session()
self.sess.run(init)
# 参数初识化
def _initialize_weights(self):
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
# 计算损失cost及进一步训练的函数
# return：当前损失
def partial_fit(self,X):
cost,opt = self.sess.run((self.cost,self.optimizer),
feed_dict = {self.x:X,self.scale:self.training_scale})
return cost
# 对模型性能评测时的cost
def calc_total_cost(self,X):
return self.sess.run(self.cost,feed_dict={self.x:X,
self.scale:self.training_scale})
# 获取抽象后的特征
# return：自编码器隐含层的输出结果
def transform(self,X):
return self.sess.run(self.hidden,feed_dict = {self.x:X,self.scale:self.training_scale})
# 隐含层的输出作为输入，将高阶特征复原为原始数据
def generate(self,hidden = None):
if hidden is None:
hidden = np.random.normal(size = self.weights['w1'])
return self.sess.run(self.reconstruction,feed_dict = {self.hidden:hidden})
# 重构层，输入为原始数据，输出为复原后的数据
def reconstruct(self,X):
return self.sess.run(self.reconstruction,feed_dict = {self.x:X,self.scale:self.training_scale})
# 获取隐含层的权重w1
def generatetWeights(self):
return self.sess.run(self.weights['w1'])
# 获取隐含层的偏置系数b1
def getBiases(self):
return self.sess.run(self.weights['b1'])
- 载入数据集(使用TensorFlow提供的示例数据)
mnist = input_data.read_data_sets('MNIST_data',one_hot = True
- 载入数据集(使用TensorFlow提供的示例数据)
mnist = input_data.read_data_sets('MNIST_data',one_hot = True
- 测试 训练数据标准化处理
def standard_scale(X_train,X_test):
preprocessor = prep.StandardScaler().fit(X_train)
X_train = preprocessor.transform(X_train)
X_test = preprocessor.transform(X_test)
return X_train,X_test
- 获取随机block数据
def get_random_block_form_data(data,batch_size):
start_index = np.random.randint(0,len(data)-batch_size)
return data[start_index:(start_index+batch_size)]
- 对训练集、测试集进行标准化变换
X_train,X_test = standard_scale(mnist.train.images,mnist.test.images)
- 设置常用参数：总训练样本数、最大训练轮数、batch_size数、显示损失间隔
n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1
- 创建AGN编码器实例
autocode = AddAutoencoder(n_input = 784,
n_hidden = 200,
transfer_function = tf.nn.softplus,
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
scale = 0.01)
- 开始训练，并输出每次的损失cost、平均损失avg_cost
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(n_samples/batch_size)
for i in range(total_batch):
batch_xs = get_random_block_form_data(X_train,batch_size)
cost = autocode.partial_fit(batch_xs)
avg_cost += cost / n_samples * batch_size
if epoch%display_step == 0:
print ('Epoch:','%04d' %(epoch+1),"cost=",
"{:.9f}".format(avg_cost))
print ("Total cost:"+str(autocode.calc_total_cost(X_test)))
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