文件列表:

LICENSE (1079, 2017-11-15)
au.py (4959, 2017-11-15)
input_data.py (7559, 2017-11-15)
rbm.py (7519, 2017-11-15)
test-ae-rbm.py (4382, 2017-11-15)
test-img (0, 2017-11-15)
test-img\1rbm.jpg (233529, 2017-11-15)
test-img\2rbm.jpg (183599, 2017-11-15)
test-img\3rbm.jpg (53408, 2017-11-15)
test-img\deepauto.png (299032, 2017-11-15)
test-img\pcafig.png (210828, 2017-11-15)
util.py (5097, 2017-11-15)
utilsnn.py (1487, 2017-11-15)

# rbm-ae-tf Tensorflow implementation of Restricted Boltzman Machine and Autoencoder for layerwise pretraining of Deep Autoencoders with RBM. Idea is to first create RBMs for pretraining weights for autoencoder. Then weigts for autoencoder are loaded and autoencoder is trained again. In this implementation you can also use tied weights for autoencoder(that means that encoding and decoding layers have same transposed weights!). I was inspired with these implementations but I need to refactor them and improve them. I tried to use also similar api as it is in [tensorflow/models](https://github.com/tensorflow/models): > [myme5261314](https://gist.github.com/myme5261314/005ceac0483fc5a581cc) > [saliksyed](https://gist.github.com/saliksyed/593c950ba1a3b9dd08d5) > Thank you for your gists! More about pretraining of weights in this paper: ##### [Reducing the Dimensionality of Data with Neural Networks](https://www.cs.toronto.edu/~hinton/science.pdf) ```python from rbm import RBM from au import AutoEncoder import tensorflow as tf import input_data flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_string('data_dir', '/tmp/data/', 'Directory for storing data') # First RBM mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) rbmobject1 = RBM(784, 100, ['rbmw1', 'rbvb1', 'rbmhb1'], 0.001) # Second RBM rbmobject2 = RBM(100, 20, ['rbmw2', 'rbvb2', 'rbmhb2'], 0.001) # Autoencoder autoencoder = AutoEncoder(784, [100, 20], [['rbmw1', 'rbmhb1'], ['rbmw2', 'rbmhb2']], tied_weights=False) # Train First RBM for i in range(100): batch_xs, batch_ys = mnist.train.next_batch(10) cost = rbmobject1.partial_fit(batch_xs) rbmobject1.save_weights('./rbmw1.chp') # Train Second RBM for i in range(100): # Transform features with first rbm for second rbm batch_xs, batch_ys = mnist.train.next_batch(10) batch_xs = rbmobject1.transform(batch_xs) cost = rbmobject2.partial_fit(batch_xs) rbmobject2.save_weights('./rbmw2.chp') # Load RBM weights to Autoencoder autoencoder.load_rbm_weights('./rbmw1.chp', ['rbmw1', 'rbmhb1'], 0) autoencoder.load_rbm_weights('./rbmw2.chp', ['rbmw2', 'rbmhb2'], 1) # Train Autoencoder for i in range(500): batch_xs, batch_ys = mnist.train.next_batch(10) cost = autoencoder.partial_fit(batch_xs) autoencoder.save_weights('./au.chp') autoencoder.load_weights('./au.chp') ``` Feel free to make updates, repairs. You can enhance implementation with some tips from: > [Practical Guide to training RBM](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) ##### PCA vs DeepAutoencoder(RBM) on MNIST:  

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