06_02_02_tflearn_autoencoder.py

# -*- coding: utf-8 -*-

""" Auto Encoder Example.
Using an auto encoder on MNIST handwritten digits.
References:
    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
    learning applied to document recognition." Proceedings of the IEEE,
    86(11):2278-2324, November 1998.
Links:
    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/
"""
from __future__ import division, print_function, absolute_import

import numpy as np
import matplotlib.pyplot as plt
import tflearn

# Data loading and preprocessing
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot=True)

# Building the encoder
encoder = tflearn.input_data(shape=[None, 784])
encoder = tflearn.fully_connected(encoder, 256)
encoder = tflearn.fully_connected(encoder, 64)

# Building the decoder
decoder = tflearn.fully_connected(encoder, 256)
decoder = tflearn.fully_connected(decoder, 784, activation='sigmoid')

# Regression, with mean square error
net = tflearn.regression(decoder, optimizer='adam', learning_rate=0.001,
                         loss='mean_square', metric=None)

import os
import datetime

log_dir = os.path.join('logs', 'fit', datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
print(log_dir)

os.makedirs(log_dir, exist_ok=True)
# Train using classifier
# tensorboard --logdir='./logs/autoencoder'
# Training the auto encoder
model = tflearn.DNN(net,  tensorboard_verbose=1, tensorboard_dir=log_dir)
model.fit(X, X, n_epoch=20, validation_set=(testX, testX),
          run_id="auto_encoder", batch_size=100)

# Encoding X[0] for test
print("\nTest encoding of X[0]:")
# New model, re-using the same session, for weights sharing
encoding_model = tflearn.DNN(encoder, session=model.session)
print(encoding_model.predict([X[0]]))

# Testing the image reconstruction on new data (test set)
print("\nVisualizing results after being encoded and decoded:")
testX = tflearn.data_utils.shuffle(testX)[0]
# Applying encode and decode over test set
encode_decode = model.predict(testX)
# Compare original images with their reconstructions
f, a = plt.subplots(2, 10, figsize=(10, 2))
for i in range(10):
    temp = [[ii, ii, ii] for ii in list(testX[i])]
    a[0][i].imshow(np.reshape(temp, (28, 28, 3)))
    temp = [[ii, ii, ii] for ii in list(encode_decode[i])]
    a[1][i].imshow(np.reshape(temp, (28, 28, 3)))
f.show()
plt.draw()
#plt.waitforbuttonpress()