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import numpy as np
import tensorflow as tf
class Autoencoder(object):
def __init__(self, n_layers, transfer_function=tf.nn.softplus, optimizer=tf.train.AdamOptimizer()):
self.n_layers = n_layers
self.transfer = transfer_function
network_weights = self._initialize_weights()
self.weights = network_weights
# model
self.x = tf.placeholder(tf.float32, [None, self.n_layers[0]])
self.hidden_encode = []
h = self.x
for layer in range(len(self.n_layers)-1):
h = self.transfer(
tf.add(tf.matmul(h, self.weights['encode'][layer]['w']),
self.weights['encode'][layer]['b']))
self.hidden_encode.append(h)
self.hidden_recon = []
for layer in range(len(self.n_layers)-1):
h = self.transfer(
tf.add(tf.matmul(h, self.weights['recon'][layer]['w']),
self.weights['recon'][layer]['b']))
self.hidden_recon.append(h)
self.reconstruction = self.hidden_recon[-1]
# 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()
initializer = tf.contrib.layers.xavier_initializer()
# Encoding network weights
encoder_weights = []
for layer in range(len(self.n_layers)-1):
w = tf.Variable(
initializer((self.n_layers[layer], self.n_layers[layer + 1]),
dtype=tf.float32))
b = tf.Variable(
tf.zeros([self.n_layers[layer + 1]], dtype=tf.float32))
encoder_weights.append({'w': w, 'b': b})
# Recon network weights
recon_weights = []
for layer in range(len(self.n_layers)-1, 0, -1):
w = tf.Variable(
initializer((self.n_layers[layer], self.n_layers[layer - 1]),
dtype=tf.float32))
b = tf.Variable(
tf.zeros([self.n_layers[layer - 1]], dtype=tf.float32))
recon_weights.append({'w': w, 'b': b})
all_weights['encode'] = encoder_weights
all_weights['recon'] = recon_weights
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.hidden_encode[-1], feed_dict={self.x: X})
def generate(self, hidden=None):
if hidden is None:
hidden = np.random.normal(size=self.weights['encode'][-1]['b'])
return self.sess.run(self.reconstruction, feed_dict={self.hidden_encode[-1]: hidden})
def reconstruct(self, X):
return self.sess.run(self.reconstruction, feed_dict={self.x: X})
def getWeights(self):
raise NotImplementedError
return self.sess.run(self.weights)
def getBiases(self):
raise NotImplementedError
return self.sess.run(self.weights)
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