import tensorflow as tf
import numpy as np
import math
from libs.utils import corrupt
# %%
def autoencoder(dimensions=[784, 512, 256, 64]):
"""Build a deep denoising autoencoder w/ tied weights.
Parameters
----------
dimensions : list, optional
The number of neurons for each layer of the autoencoder.
Returns
-------
x : Tensor
Input placeholder to the network
z : Tensor
Inner-most latent representation
y : Tensor
Output reconstruction of the input
cost : Tensor
Overall cost to use for training
"""
# input to the network
x = tf.placeholder(tf.float32, [None, dimensions[0]], name='x')
# Probability that we will corrupt input.
# This is the essence of the denoising autoencoder, and is pretty
# basic. We'll feed forward a noisy input, allowing our network
# to generalize better, possibly, to occlusions of what we're
# really interested in. But to measure accuracy, we'll still
# enforce a training signal which measures the original image's
# reconstruction cost.
#
# We'll change this to 1 during training
# but when we're ready for testing/production ready environments,
# we'll put it back to 0.
corrupt_prob = tf.placeholder(tf.float32, [1])
current_input = corrupt(x) * corrupt_prob + x * (1 - corrupt_prob)
# Build the encoder
encoder = []
for layer_i, n_output in enumerate(dimensions[1:]):
n_input = int(current_input.get_shape()[1])
W = tf.Variable(
tf.random_uniform([n_input, n_output],
-1.0 / math.sqrt(n_input),
1.0 / math.sqrt(n_input)))
b = tf.Variable(tf.zeros([n_output]))
encoder.append(W)
output = tf.nn.tanh(tf.matmul(current_input, W) + b)
current_input = output
# latent representation
z = current_input
encoder.reverse()
# Build the decoder using the same weights
for layer_i, n_output in enumerate(dimensions[:-1][::-1]):
W = tf.transpose(encoder[layer_i])
b = tf.Variable(tf.zeros([n_output]))
output = tf.nn.tanh(tf.matmul(current_input, W) + b)
current_input = output
# now have the reconstruction through the network
y = current_input
# cost function measures pixel-wise difference
cost = tf.sqrt(tf.reduce_mean(tf.square(y - x)))
return {'x': x, 'z': z, 'y': y,
'corrupt_prob': corrupt_prob,
'cost': cost}
# %%
def test_mnist():
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import matplotlib.pyplot as plt
# %%
# load MNIST as before
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
mean_img = np.mean(mnist.train.images, axis=0)
ae = autoencoder(dimensions=[784, 256, 64])
# %%
learning_rate = 0.001
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(ae['cost'])
# %%
# We create a session to use the graph
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# %%
# Fit all training data
batch_size = 50
n_epochs = 10
for epoch_i in range(n_epochs):
for batch_i in range(mnist.train.num_examples // batch_size):
batch_xs, _ = mnist.train.next_batch(batch_size)
train = np.array([img - mean_img for img in batch_xs])
sess.run(optimizer, feed_dict={
ae['x']: train, ae['corrupt_prob']: [1.0]})
print(epoch_i, sess.run(ae['cost'], feed_dict={
ae['x']: train, ae['corrupt_prob']: [1.0]}))
# %%
# Plot example reconstructions
n_examples = 15
test_xs, _ = mnist.test.next_batch(n_examples)
test_xs_norm = np.array([img - mean_img for img in test_xs])
recon = sess.run(ae['y'], feed_dict={
ae['x']: test_xs_norm, ae['corrupt_prob']: [0.0]})
fig, axs = plt.subplots(2, n_examples, figsize=(10, 2))
for example_i in range(n_examples):
axs[0][example_i].imshow(
np.reshape(test_xs[example_i, :], (28, 28)))
axs[1][example_i].imshow(
np.reshape([recon[example_i, :] + mean_img], (28, 28)))
fig.show()
plt.draw()
plt.waitforbuttonpress()
if __name__ == '__main__':
test_mnist()
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