import tensorflow as tf
class AdditiveGaussianNoiseAutoencoder(object):
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
# model
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'])
# cost
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)
def _initialize_weights(self):
all_weights = dict()
all_weights['w1'] = tf.get_variable("w1", shape=[self.n_input, self.n_hidden],
initializer=tf.contrib.layers.xavier_initializer())
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):
cost, opt = self.sess.run((self.cost, self.optimizer), feed_dict = {self.x: X,
self.scale: self.training_scale
})
return cost
def calc_total_cost(self, X):
return self.sess.run(self.cost, feed_dict = {self.x: X,
self.scale: self.training_scale
})
def transform(self, X):
return self.sess.run(self.hidden, feed_dict = {self.x: X,
self.scale: self.training_scale
})
def generate(self, hidden=None):
if hidden is None:
hidden = self.sess.run(tf.random_normal([1, self.n_hidden]))
return self.sess.run(self.reconstruction, feed_dict = {self.hidden: hidden})
def reconstruct(self, X):
return self.sess.run(self.reconstruction, feed_dict = {self.x: X,
self.scale: self.training_scale
})
def getWeights(self):
return self.sess.run(self.weights['w1'])
def getBiases(self):
return self.sess.run(self.weights['b1'])
class MaskingNoiseAutoencoder(object):
def __init__(self, n_input, n_hidden, transfer_function = tf.nn.softplus, optimizer = tf.train.AdamOptimizer(),
dropout_probability = 0.95):
self.n_input = n_input
self.n_hidden = n_hidden
self.transfer = transfer_function
self.dropout_probability = dropout_probability
self.keep_prob = tf.placeholder(tf.float32)
network_weights = self._initialize_weights()
self.weights = network_weights
# model
self.x = tf.placeholder(tf.float32, [None, self.n_input])
self.hidden = self.transfer(tf.add(tf.matmul(tf.nn.dropout(self.x, self.keep_prob), self.weights['w1']),
self.weights['b1']))
self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])
# cost
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)
def _initialize_weights(self):
all_weights = dict()
all_weights['w1'] = tf.get_variable("w1", shape=[self.n_input, self.n_hidden],
initializer=tf.contrib.layers.xavier_initializer())
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):
cost, opt = self.sess.run((self.cost, self.optimizer),
feed_dict = {self.x: X, self.keep_prob: self.dropout_probability})
return cost
def calc_total_cost(self, X):
return self.sess.run(self.cost, feed_dict = {self.x: X, self.keep_prob: 1.0})
def transform(self, X):
return self.sess.run(self.hidden, feed_dict = {self.x: X, self.keep_prob: 1.0})
def generate(self, hidden=None):
if hidden is None:
hidden = self.sess.run(tf.random_normal([1, self.n_hidden]))
return self.sess.run(self.reconstruction, feed_dict = {self.hidden: hidden})
def reconstruct(self, X):
return self.sess.run(self.reconstruction, feed_dict = {self.x: X, self.keep_prob: 1.0})
def getWeights(self):
return self.sess.run(self.weights['w1'])
def getBiases(self):
return self.sess.run(self.weights['b1'])
