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Tutorial AE on natural images added (corrections 1)
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| """ Example for sparse Autoencoder (SAE) on natural image patches. | ||
| :Version: | ||
| 1.0.0 | ||
| :Date: | ||
| 25.01.2018 | ||
| :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 | ||
|
|
||
| # 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 | ||
|
|
||
| # Set random seed | ||
| numx.random.seed(42) | ||
|
|
||
| # Load data (download is not existing) | ||
| data = io.load_natural_image_patches('../../../data/NaturalImage.mat') | ||
|
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||
| # Remove mean individually | ||
| data = pre.remove_rows_means(data) | ||
|
|
||
| # Shuffle data | ||
| data = numx.random.permutation(data) | ||
|
|
||
| # Specify input and hidden dimensions | ||
| h1 = 20 | ||
| h2 = 20 | ||
| v1 = 14 | ||
| v2 = 14 | ||
|
|
||
| # Whiten data using ZCA or change it to STANDARIZER for unwhitened results | ||
| zca = pre.ZCA(v1 * v2) | ||
| zca.train(data) | ||
| data = zca.project(data) | ||
|
|
||
| # Split in tarining and test data | ||
| train_data = data[0:50000] | ||
| test_data = data[50000:70000] | ||
|
|
||
| # Set hyperparameters batchsize and number of epochs | ||
| batch_size = 10 | ||
| max_epochs = 20 | ||
|
|
||
| # 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.Identity(), | ||
| hidden_activation_function = act.Sigmoid(), | ||
| cost_function = cost.SquaredError(), | ||
| initial_weights = 0.01, | ||
| initial_visible_bias = 0.0, | ||
| initial_hidden_bias = -2.0, # Set initially the units to be inactive, speeds up learning a little bit | ||
| initial_visible_offsets = 0.0, | ||
| initial_hidden_offsets = 0.02, | ||
| dtype = numx.float64) | ||
|
|
||
| # Initialized gradient descent trainer | ||
| trainer = aeTrainer.GDTrainer(ae) | ||
|
|
||
| # 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) : | ||
|
|
||
| # Shuffle data | ||
| train_data = numx.random.permutation(train_data) | ||
|
|
||
| # 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): | ||
|
|
||
| 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 = 2.0, # Rather strong sparsity regularization | ||
| desired_sparseness=0.001, | ||
| reg_contractive=0.0, | ||
| reg_slowness=0.0, | ||
| data_next=None, | ||
| restrict_gradient=0.1, # The gradient restriction is important for fast learning, see also GRBMs | ||
| restriction_norm='Cols') | ||
|
|
||
| # Show filters/features | ||
| filters = vis.tile_matrix_rows(ae.w, v1,v2,h1,h2, border_size = 1,normalized = True) | ||
| vis.imshow_matrix(filters, 'Filter') | ||
|
|
||
| # Show samples | ||
| samples = vis.tile_matrix_rows(train_data[0:100].T, v1,v2,10,10, border_size = 1,normalized = True) | ||
| vis.imshow_matrix(samples, 'Data samples') | ||
|
|
||
| # Show reconstruction | ||
| samples = vis.tile_matrix_rows(ae.decode(ae.encode(train_data[0:100])).T, v1,v2,10,10, border_size = 1,normalized = True) | ||
| vis.imshow_matrix(samples, 'Reconstructed samples') | ||
|
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||
| # Get the optimal gabor wavelet frequency and angle for the filters | ||
| opt_frq, opt_ang = vis.filter_frequency_and_angle(ae.w, num_of_angles=40) | ||
|
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| # Show some tuning curves | ||
| num_filters =20 | ||
| vis.imshow_filter_tuning_curve(ae.w[:,0:num_filters], num_of_ang=40) | ||
|
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||
| # Show some optima grating | ||
| vis.imshow_filter_optimal_gratings(ae.w[:,0:num_filters], | ||
| opt_frq[0:num_filters], | ||
| opt_ang[0:num_filters]) | ||
|
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||
| # Show histograms of frequencies and angles. | ||
| vis.imshow_filter_frequency_angle_histogram(opt_frq=opt_frq, | ||
| opt_ang=opt_ang, | ||
| max_wavelength=14) | ||
|
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||
| # Show all windows. | ||
| vis.show() |