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Tutorial AE MNIST added added (corrections 1)
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""" Example for sparse Autoencoder (SAE) on natural image patches. | ||
:Version: | ||
1.0.0 | ||
:Date: | ||
28.01.2018 | ||
:Author: | ||
Jan Melchior | ||
:Contact: | ||
JanMelchior@gmx.de | ||
:License: | ||
Copyright (C) 2018 Jan Melchior | ||
This file is part of the Python library PyDeep. | ||
PyDeep is free software: you can redistribute it and/or modify | ||
it under the terms of the GNU General Public License as published by | ||
the Free Software Foundation, either version 3 of the License, or | ||
(at your option) any later version. | ||
This program is distributed in the hope that it will be useful, | ||
but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
GNU General Public License for more details. | ||
You should have received a copy of the GNU General Public License | ||
along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
""" | ||
# Import numpy, i/o functions, preprocessing, and visualization. | ||
import numpy as numx | ||
import pydeep.misc.io as io | ||
import pydeep.misc.visualization as vis | ||
import pydeep.preprocessing as pre | ||
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# Import cost functions, activation function, Autencoder and trainer module | ||
import pydeep.base.activationfunction as act | ||
import pydeep.base.costfunction as cost | ||
import pydeep.ae.model as aeModel | ||
import pydeep.ae.trainer as aeTrainer | ||
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# Set random seed (optional) | ||
numx.random.seed(42) | ||
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# Input and hidden dimensionality | ||
v1 = v2 = 28 | ||
h1 = 10 | ||
h2 = 10 | ||
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# Load data , get it from 'deeplearning.net/data/mnist/mnist.pkl.gz' | ||
train_data, _, _, _, test_data, _ = io.load_mnist("../../data/mnist.pkl.gz", False) | ||
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# Set hyperparameters batchsize and number of epochs | ||
batch_size = 10 | ||
max_epochs = 10 | ||
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# Create model with sigmoid hidden units, linear output units, and squared error loss. | ||
ae = aeModel.AutoEncoder(v1*v2, | ||
h1*h2, | ||
data = train_data, | ||
visible_activation_function = act.Sigmoid(), | ||
hidden_activation_function = act.Sigmoid(), | ||
cost_function = cost.CrossEntropyError(), | ||
initial_weights = 'AUTO', | ||
initial_visible_bias = 'AUTO', | ||
initial_hidden_bias = 'AUTO', | ||
initial_visible_offsets = 'AUTO', | ||
initial_hidden_offsets = 'AUTO', | ||
dtype = numx.float64) | ||
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# Initialized gradient descent trainer | ||
trainer = aeTrainer.GDTrainer(ae) | ||
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# Train model | ||
print 'Training' | ||
print 'Epoch\tRE train\t\tRE test\t\t\tSparsness train\t\tSparsness test ' | ||
for epoch in range(0,max_epochs+1,1) : | ||
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# Shuffle data | ||
train_data = numx.random.permutation(train_data) | ||
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# Print reconstruction errors and sparseness for Training and test data | ||
print epoch, ' \t\t', numx.mean(ae.reconstruction_error(train_data)), ' \t',\ | ||
numx.mean(ae.reconstruction_error(test_data)), ' \t', numx.mean(ae.encode(train_data)), ' \t',\ | ||
numx.mean(ae.encode(test_data)) | ||
for b in range(0,train_data.shape[0],batch_size): | ||
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trainer.train(data = train_data[b:(b+batch_size),:], | ||
num_epochs=1, | ||
epsilon=0.1, | ||
momentum=0.0, | ||
update_visible_offsets=0.0, | ||
update_hidden_offsets=0.01, | ||
reg_L1Norm=0.0, | ||
reg_L2Norm=0.0, | ||
corruptor=None, | ||
reg_sparseness = 0.0, | ||
desired_sparseness=0.0, | ||
# Set to 0.0 to disable contractive penalty | ||
reg_contractive=0.3, | ||
reg_slowness=0.0, | ||
data_next=None, | ||
restrict_gradient=0.0, | ||
restriction_norm='Cols') | ||
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# Show filters/features | ||
filters = vis.tile_matrix_rows(ae.w, v1,v2,h1,h2, border_size = 1, | ||
normalized = True) | ||
vis.imshow_matrix(filters, 'Filter') | ||
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# Show samples | ||
samples = vis.tile_matrix_rows(test_data[0:100].T, v1,v2,10,10, | ||
border_size = 1, | ||
normalized = True) | ||
vis.imshow_matrix(samples, 'Data samples') | ||
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# Show reconstruction | ||
samples = vis.tile_matrix_rows(ae.decode(ae.encode(test_data[0:100])).T, | ||
v1,v2,10,10, | ||
border_size = 1, | ||
normalized = True) | ||
vis.imshow_matrix(samples, 'Reconstructed samples') | ||
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# Show all windows. | ||
vis.show() |