Small binary RBM example added

examples/small_binary_RBM_MNIST.py

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+''' Example using a small BB-RBMs on the MNIST handwritten digit database.
+
+    :Version:
+        1.1.0
+
+    :Date:
+        20.04.2017
+
+    :Author:
+        Jan Melchior
+
+    :Contact:
+        JanMelchior@gmx.de
+
+    :License:
+
+        Copyright (C) 2017 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 as numx
+import pydeep.rbm.model as model
+import pydeep.rbm.trainer as trainer
+import pydeep.rbm.estimator as estimator
+
+import pydeep.misc.io as io
+import pydeep.misc.visualization as vis
+import pydeep.misc.measuring as mea
+
+# Set random seed (optional)
+numx.random.seed(42)
+
+# Input and hidden dimensionality
+v1 = v2 = 28
+h1 = h2 = 4
+
+# Load data , get it from 'deeplearning.net/data/mnist/mnist.pkl.gz'
+train_data = io.load_mnist("../../data/mnist.pkl.gz", True)[0]
+
+# Training paramters
+batch_size = 100
+epochs = 39
+
+# Create trainer and model
+rbm = model.BinaryBinaryRBM(number_visibles=v1 * v2,
+                            number_hiddens=h1 * h2,
+                            data=train_data)
+trainer = trainer.PCD(rbm, batch_size)
+
+# Measuring time
+measurer = mea.Stopwatch()
+
+# Train model
+print('Training')
+print('Epoch\t\tRecon. Error\tLog likelihood \tExpected End-Time')
+for epoch in range(1, epochs + 1):
+
+    # Shuffle training samples (optional)
+    train_data = numx.random.permutation(train_data)
+
+    # Loop over all batches
+    for b in range(0, train_data.shape[0], batch_size):
+        batch = train_data[b:b + batch_size, :]
+        trainer.train(data=batch, epsilon=0.05)
+
+    # Calculate Log-Likelihood, reconstruction error and expected end time every 10th epoch
+    if epoch % 10 == 0:
+        logZ = estimator.partition_function_factorize_h(rbm)
+        ll = numx.mean(estimator.log_likelihood_v(rbm, logZ, train_data))
+        re = numx.mean(estimator.reconstruction_error(rbm, train_data))
+        print('{}\t\t{:.4f}\t\t\t{:.4f}\t\t\t{}'.format(epoch, re, ll, measurer.get_expected_end_time(epoch, epochs)))
+    else:
+        print(epoch)
+
+measurer.end()
+
+# Print end/training time
+print("End-time: \t{}".format(measurer.get_end_time()))
+print("Training time:\t{}".format(measurer.get_interval()))
+
+# Calculate true partition function
+logZ = estimator.partition_function_factorize_h(rbm, batchsize_exponent=h1, status=False)
+print("True Partition: {} (LL: {})".format(logZ, numx.mean(estimator.log_likelihood_v(rbm, logZ, train_data))))
+
+# Approximate partition function by AIS (tends to overestimate)
+logZ_approx_ = estimator.annealed_importance_sampling(rbm)[0]
+print(
+"AIS Partition: {} (LL: {})".format(logZ_approx_, numx.mean(estimator.log_likelihood_v(rbm, logZ_approx_, train_data))))
+
+# Approximate partition function by reverse AIS (tends to underestimate)
+logZ_approx_up = estimator.reverse_annealed_importance_sampling(rbm, data=train_data)[0]
+print("reverse AIS Partition: {} (LL: {})".format(logZ_approx_up, numx.mean(
+    estimator.log_likelihood_v(rbm, logZ_approx_up, train_data))))
+
+# Reorder RBM features by average activity decreasingly
+reordered_rbm = vis.reorder_filter_by_hidden_activation(rbm, train_data)
+
+# Display RBM parameters
+vis.imshow_standard_rbm_parameters(reordered_rbm, v1, v2, h1, h2)
+
+# Sample some steps and show results
+samples = vis.generate_samples(rbm, train_data[0:30], 30, 1, v1, v2, False, None)
+vis.imshow_matrix(samples, 'Samples')
+
+# Display results
+vis.show()