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Toy example of single-layer autoencoder in TensorFlow
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import tensorflow as tf | |
import numpy as np | |
import math | |
#import pandas as pd | |
#import sys | |
input = np.array([[2.0, 1.0, 1.0, 2.0], | |
[-2.0, 1.0, -1.0, 2.0], | |
[0.0, 1.0, 0.0, 2.0], | |
[0.0, -1.0, 0.0, -2.0], | |
[0.0, -1.0, 0.0, -2.0]]) | |
# Code here for importing data from file | |
noisy_input = input + .2 * np.random.random_sample((input.shape)) - .1 | |
output = input | |
# Scale to [0,1] | |
scaled_input_1 = np.divide((noisy_input-noisy_input.min()), (noisy_input.max()-noisy_input.min())) | |
scaled_output_1 = np.divide((output-output.min()), (output.max()-output.min())) | |
# Scale to [-1,1] | |
scaled_input_2 = (scaled_input_1*2)-1 | |
scaled_output_2 = (scaled_output_1*2)-1 | |
input_data = scaled_input_2 | |
output_data = scaled_output_2 | |
# Autoencoder with 1 hidden layer | |
n_samp, n_input = input_data.shape | |
n_hidden = 2 | |
x = tf.placeholder("float", [None, n_input]) | |
# Weights and biases to hidden layer | |
Wh = tf.Variable(tf.random_uniform((n_input, n_hidden), -1.0 / math.sqrt(n_input), 1.0 / math.sqrt(n_input))) | |
bh = tf.Variable(tf.zeros([n_hidden])) | |
h = tf.nn.tanh(tf.matmul(x,Wh) + bh) | |
# Weights and biases to hidden layer | |
Wo = tf.transpose(Wh) # tied weights | |
bo = tf.Variable(tf.zeros([n_input])) | |
y = tf.nn.tanh(tf.matmul(h,Wo) + bo) | |
# Objective functions | |
y_ = tf.placeholder("float", [None,n_input]) | |
cross_entropy = -tf.reduce_sum(y_*tf.log(y)) | |
meansq = tf.reduce_mean(tf.square(y_-y)) | |
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(meansq) | |
init = tf.initialize_all_variables() | |
sess = tf.Session() | |
sess.run(init) | |
n_rounds = 5000 | |
batch_size = min(50, n_samp) | |
for i in range(n_rounds): | |
sample = np.random.randint(n_samp, size=batch_size) | |
batch_xs = input_data[sample][:] | |
batch_ys = output_data[sample][:] | |
sess.run(train_step, feed_dict={x: batch_xs, y_:batch_ys}) | |
if i % 100 == 0: | |
print i, sess.run(cross_entropy, feed_dict={x: batch_xs, y_:batch_ys}), sess.run(meansq, feed_dict={x: batch_xs, y_:batch_ys}) | |
print "Target:" | |
print output_data | |
print "Final activations:" | |
print sess.run(y, feed_dict={x: input_data}) | |
print "Final weights (input => hidden layer)" | |
print sess.run(Wh) | |
print "Final biases (input => hidden layer)" | |
print sess.run(bh) | |
print "Final biases (hidden layer => output)" | |
print sess.run(bo) | |
print "Final activations of hidden layer" | |
print sess.run(h, feed_dict={x: input_data}) |
Isn't this actually a Denoising Autoencoders?
Is there anything specific about data distribution or nature of input array that is given above ? Because when I changed the array values of variable input
, I am not able to get the same output as input. Actually, I am trying to use this code in a generated multivariate normal data.
how to view the reconstructed input?
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wrt https://gist.github.com/hussius/1534135a419bb0b957b9#file-ae_toy_example-py-L42
pro tip: when it comes to autoencoders, it is common practice to reuse input (eg
x = tf.placeholder("float", [None, n_input])
) to highlight the fact that inputs and outputs are the same.