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- import tensorflow as tf
- class VariationalAutoencoder(object):
- def __init__(self, n_input, n_hidden, optimizer = tf.train.AdamOptimizer()):
- self.n_input = n_input
- self.n_hidden = n_hidden
- network_weights = self._initialize_weights()
- self.weights = network_weights
- # model
- self.x = tf.placeholder(tf.float32, [None, self.n_input])
- self.z_mean = tf.add(tf.matmul(self.x, self.weights['w1']), self.weights['b1'])
- self.z_log_sigma_sq = tf.add(tf.matmul(self.x, self.weights['log_sigma_w1']), self.weights['log_sigma_b1'])
- # sample from gaussian distribution
- eps = tf.random_normal(tf.stack([tf.shape(self.x)[0], self.n_hidden]), 0, 1, dtype = tf.float32)
- self.z = tf.add(self.z_mean, tf.multiply(tf.sqrt(tf.exp(self.z_log_sigma_sq)), eps))
- self.reconstruction = tf.add(tf.matmul(self.z, self.weights['w2']), self.weights['b2'])
- # cost
- reconstr_loss = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
- latent_loss = -0.5 * tf.reduce_sum(1 + self.z_log_sigma_sq
- - tf.square(self.z_mean)
- - tf.exp(self.z_log_sigma_sq), 1)
- self.cost = tf.reduce_mean(reconstr_loss + latent_loss)
- 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['log_sigma_w1'] = tf.get_variable("log_sigma_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['log_sigma_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})
- return cost
- def calc_total_cost(self, X):
- return self.sess.run(self.cost, feed_dict = {self.x: X})
- def transform(self, X):
- return self.sess.run(self.z_mean, feed_dict={self.x: X})
- 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.z: hidden})
- def reconstruct(self, X):
- return self.sess.run(self.reconstruction, feed_dict={self.x: X})
- def getWeights(self):
- return self.sess.run(self.weights['w1'])
- def getBiases(self):
- return self.sess.run(self.weights['b1'])
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