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| 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']) | |