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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), 1) | |
| 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']) | |