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initial commit, simple, separated models #1
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autoencoder/MNIST_data/* | ||
*.pyc |
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Very simple implementations of some autoencoder variations |
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import numpy as np | ||
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import sklearn.preprocessing as prep | ||
import tensorflow as tf | ||
from tensorflow.examples.tutorials.mnist import input_data | ||
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from autoencoder.autoencoder_models.DenoisingAutoencoder import AdditiveGaussianNoiseAutoencoder | ||
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mnist = input_data.read_data_sets('MNIST_data', one_hot = True) | ||
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def standard_scale(X_train, X_test): | ||
preprocessor = prep.StandardScaler().fit(X_train) | ||
X_train = preprocessor.transform(X_train) | ||
X_test = preprocessor.transform(X_test) | ||
return X_train, X_test | ||
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def get_random_block_from_data(data, batch_size): | ||
start_index = np.random.randint(0, len(data) - batch_size) | ||
return data[start_index:(start_index + batch_size)] | ||
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X_train, X_test = standard_scale(mnist.train.images, mnist.test.images) | ||
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n_samples = int(mnist.train.num_examples) | ||
training_epochs = 20 | ||
batch_size = 128 | ||
display_step = 1 | ||
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autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784, | ||
n_hidden = 200, | ||
transfer_function = tf.nn.softplus, | ||
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001), | ||
scale = 0.01) | ||
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for epoch in range(training_epochs): | ||
avg_cost = 0. | ||
total_batch = int(n_samples / batch_size) | ||
# Loop over all batches | ||
for i in range(total_batch): | ||
batch_xs = get_random_block_from_data(X_train, batch_size) | ||
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# Fit training using batch data | ||
cost = autoencoder.partial_fit(batch_xs) | ||
# Compute average loss | ||
avg_cost += cost / n_samples * batch_size | ||
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# Display logs per epoch step | ||
if epoch % display_step == 0: | ||
print "Epoch:", '%04d' % (epoch + 1), \ | ||
"cost=", "{:.9f}".format(avg_cost) | ||
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print "Total cost: " + str(autoencoder.calc_total_cost(X_test)) |
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import numpy as np | ||
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import sklearn.preprocessing as prep | ||
import tensorflow as tf | ||
from tensorflow.examples.tutorials.mnist import input_data | ||
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from autoencoder.autoencoder_models.Autoencoder import Autoencoder | ||
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mnist = input_data.read_data_sets('MNIST_data', one_hot = True) | ||
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def standard_scale(X_train, X_test): | ||
preprocessor = prep.StandardScaler().fit(X_train) | ||
X_train = preprocessor.transform(X_train) | ||
X_test = preprocessor.transform(X_test) | ||
return X_train, X_test | ||
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def get_random_block_from_data(data, batch_size): | ||
start_index = np.random.randint(0, len(data) - batch_size) | ||
return data[start_index:(start_index + batch_size)] | ||
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X_train, X_test = standard_scale(mnist.train.images, mnist.test.images) | ||
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n_samples = int(mnist.train.num_examples) | ||
training_epochs = 20 | ||
batch_size = 128 | ||
display_step = 1 | ||
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autoencoder = Autoencoder(n_input = 784, | ||
n_hidden = 200, | ||
transfer_function = tf.nn.softplus, | ||
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001)) | ||
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for epoch in range(training_epochs): | ||
avg_cost = 0. | ||
total_batch = int(n_samples / batch_size) | ||
# Loop over all batches | ||
for i in range(total_batch): | ||
batch_xs = get_random_block_from_data(X_train, batch_size) | ||
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# Fit training using batch data | ||
cost = autoencoder.partial_fit(batch_xs) | ||
# Compute average loss | ||
avg_cost += cost / n_samples * batch_size | ||
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# Display logs per epoch step | ||
if epoch % display_step == 0: | ||
print "Epoch:", '%04d' % (epoch + 1), \ | ||
"cost=", "{:.9f}".format(avg_cost) | ||
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print "Total cost: " + str(autoencoder.calc_total_cost(X_test)) |
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import numpy as np | ||
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import sklearn.preprocessing as prep | ||
import tensorflow as tf | ||
from tensorflow.examples.tutorials.mnist import input_data | ||
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from autoencoder.autoencoder_models.DenoisingAutoencoder import MaskingNoiseAutoencoder | ||
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mnist = input_data.read_data_sets('MNIST_data', one_hot = True) | ||
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def standard_scale(X_train, X_test): | ||
preprocessor = prep.StandardScaler().fit(X_train) | ||
X_train = preprocessor.transform(X_train) | ||
X_test = preprocessor.transform(X_test) | ||
return X_train, X_test | ||
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def get_random_block_from_data(data, batch_size): | ||
start_index = np.random.randint(0, len(data) - batch_size) | ||
return data[start_index:(start_index + batch_size)] | ||
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X_train, X_test = standard_scale(mnist.train.images, mnist.test.images) | ||
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n_samples = int(mnist.train.num_examples) | ||
training_epochs = 100 | ||
batch_size = 128 | ||
display_step = 1 | ||
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autoencoder = MaskingNoiseAutoencoder(n_input = 784, | ||
n_hidden = 200, | ||
transfer_function = tf.nn.softplus, | ||
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001), | ||
dropout_probability = 0.95) | ||
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for epoch in range(training_epochs): | ||
avg_cost = 0. | ||
total_batch = int(n_samples / batch_size) | ||
for i in range(total_batch): | ||
batch_xs = get_random_block_from_data(X_train, batch_size) | ||
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cost = autoencoder.partial_fit(batch_xs) | ||
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avg_cost += cost / n_samples * batch_size | ||
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if epoch % display_step == 0: | ||
print "Epoch:", '%04d' % (epoch + 1), \ | ||
"cost=", "{:.9f}".format(avg_cost) | ||
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print "Total cost: " + str(autoencoder.calc_total_cost(X_test)) |
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import numpy as np | ||
import tensorflow as tf | ||
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def xavier_init(fan_in, fan_out, constant = 1): | ||
low = -constant * np.sqrt(6.0 / (fan_in + fan_out)) | ||
high = constant * np.sqrt(6.0 / (fan_in + fan_out)) | ||
return tf.random_uniform((fan_in, fan_out), | ||
minval = low, maxval = high, | ||
dtype = tf.float32) |
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import numpy as np | ||
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import sklearn.preprocessing as prep | ||
import tensorflow as tf | ||
from tensorflow.examples.tutorials.mnist import input_data | ||
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from autoencoder.autoencoder_models.VariationalAutoencoder import VariationalAutoencoder | ||
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mnist = input_data.read_data_sets('MNIST_data', one_hot = True) | ||
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def standard_scale(X_train, X_test): | ||
preprocessor = prep.StandardScaler().fit(X_train) | ||
X_train = preprocessor.transform(X_train) | ||
X_test = preprocessor.transform(X_test) | ||
return X_train, X_test | ||
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def get_random_block_from_data(data, batch_size): | ||
start_index = np.random.randint(0, len(data) - batch_size) | ||
return data[start_index:(start_index + batch_size)] | ||
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X_train, X_test = standard_scale(mnist.train.images, mnist.test.images) | ||
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n_samples = int(mnist.train.num_examples) | ||
training_epochs = 20 | ||
batch_size = 128 | ||
display_step = 1 | ||
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autoencoder = VariationalAutoencoder(n_input = 784, | ||
n_hidden = 200, | ||
optimizer = tf.train.AdamOptimizer(learning_rate = 0.001), | ||
gaussian_sample_size = 128) | ||
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for epoch in range(training_epochs): | ||
avg_cost = 0. | ||
total_batch = int(n_samples / batch_size) | ||
# Loop over all batches | ||
for i in range(total_batch): | ||
batch_xs = get_random_block_from_data(X_train, batch_size) | ||
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# Fit training using batch data | ||
cost = autoencoder.partial_fit(batch_xs) | ||
# Compute average loss | ||
avg_cost += cost / n_samples * batch_size | ||
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# Display logs per epoch step | ||
if epoch % display_step == 0: | ||
print "Epoch:", '%04d' % (epoch + 1), \ | ||
"cost=", "{:.9f}".format(avg_cost) | ||
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print "Total cost: " + str(autoencoder.calc_total_cost(X_test)) |
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import tensorflow as tf | ||
import numpy as np | ||
import autoencoder.Utils | ||
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class Autoencoder(object): | ||
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def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus, optimizer = tf.train.AdamOptimizer()): | ||
self.n_input = n_input | ||
self.n_hidden = n_hidden | ||
self.transfer = transfer_function | ||
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network_weights = self._initialize_weights() | ||
self.weights = network_weights | ||
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# model | ||
self.x = tf.placeholder(tf.float32, [None, self.n_input]) | ||
self.hidden = self.transfer(tf.add(tf.matmul(self.x, self.weights['w1']), self.weights['b1'])) | ||
self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2']) | ||
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# cost | ||
self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.sub(self.reconstruction, self.x), 2.0)) | ||
self.optimizer = optimizer.minimize(self.cost) | ||
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init = tf.initialize_all_variables() | ||
self.sess = tf.Session() | ||
self.sess.run(init) | ||
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def _initialize_weights(self): | ||
all_weights = dict() | ||
all_weights['w1'] = tf.Variable(autoencoder.Utils.xavier_init(self.n_input, self.n_hidden)) | ||
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 | ||
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def partial_fit(self, X): | ||
cost, opt = self.sess.run((self.cost, self.optimizer), feed_dict={self.x: X}) | ||
return cost | ||
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def calc_total_cost(self, X): | ||
return self.sess.run(self.cost, feed_dict = {self.x: X}) | ||
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def transform(self, X): | ||
return self.sess.run(self.hidden, feed_dict={self.x: X}) | ||
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def generate(self, hidden = None): | ||
if hidden is None: | ||
hidden = np.random.normal(size=self.weights["b1"]) | ||
return self.sess.run(self.reconstruction, feed_dict={self.hidden: hidden}) | ||
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def reconstruct(self, X): | ||
return self.sess.run(self.reconstruction, feed_dict={self.x: X}) | ||
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def getWeights(self): | ||
return self.sess.run(self.weights['w1']) | ||
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def getBiases(self): | ||
return self.sess.run(self.weights['b1']) | ||
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If you are adding .gitignore cosider adding *.pyc there and remove the pyc files from this PR (and squash it, so the never make it to the repo).