def example_mnist_rbm(paysage_path=None, show_plot = False):
num_hidden_units = 500
batch_size = 50
num_epochs = 10
learning_rate = 0.01
mc_steps = 1
(_, _, shuffled_filepath) = \
util.default_paths(paysage_path)
# set up the reader to get minibatches
data = batch.Batch(shuffled_filepath,
'train/images',
batch_size,
transform=batch.binarize_color,
train_fraction=0.99)
# set up the model and initialize the parameters
vis_layer = layers.BernoulliLayer(data.ncols)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = hidden.Model([vis_layer, hid_layer])
rbm.initialize(data)
# set up the optimizer and the fit method
opt = optimizers.ADAM(rbm,
stepsize=learning_rate,
scheduler=optimizers.PowerLawDecay(0.1))
sampler = fit.DrivenSequentialMC.from_batch(rbm, data,
method='stochastic')
cd = fit.PCD(rbm, data, opt, sampler,
num_epochs, mcsteps=mc_steps, skip=200,
metrics=[M.ReconstructionError(),
M.EnergyDistance(),
M.EnergyGap(),
M.EnergyZscore()])
# fit the model
print('training with contrastive divergence')
cd.train()
# evaluate the model
# this will be the same as the final epoch results
# it is repeated here to be consistent with the sklearn rbm example
metrics = [M.ReconstructionError(), M.EnergyDistance(),
M.EnergyGap(), M.EnergyZscore()]
performance = fit.ProgressMonitor(0, data, metrics=metrics)
util.show_metrics(rbm, performance)
util.show_reconstructions(rbm, data.get('validate'), fit, show_plot)
util.show_fantasy_particles(rbm, data.get('validate'), fit, show_plot)
util.show_weights(rbm, show_plot)
# close the HDF5 store
data.close()
print("Done")
def test_rbm(paysage_path=None):
"""TODO : this is just a placeholder, need to clean up & simplifiy setup. Also
need to figure how to deal with consistent random seeding throughout the
codebase to obtain deterministic checkable results.
"""
num_hidden_units = 50
batch_size = 50
num_epochs = 1
learning_rate = 0.01
mc_steps = 1
if not paysage_path:
paysage_path = os.path.dirname(
os.path.dirname(os.path.abspath(__file__)))
filepath = os.path.join(paysage_path, 'mnist', 'mnist.h5')
if not os.path.exists(filepath):
raise IOError(
"{} does not exist. run mnist/download_mnist.py to fetch from the web"
.format(filepath))
shuffled_filepath = os.path.join(paysage_path, 'mnist',
'shuffled_mnist.h5')
# shuffle the data
if not os.path.exists(shuffled_filepath):
shuffler = batch.DataShuffler(filepath, shuffled_filepath, complevel=0)
shuffler.shuffle()
# set a seed for the random number generator
be.set_seed()
# set up the reader to get minibatches
data = batch.Batch(shuffled_filepath,
'train/images',
batch_size,
transform=batch.binarize_color,
train_fraction=0.99)
# set up the model and initialize the parameters
vis_layer = layers.BernoulliLayer(data.ncols)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = hidden.Model([vis_layer, hid_layer])
rbm.initialize(data)
# obtain initial estimate of the reconstruction error
perf = fit.ProgressMonitor(0, data, metrics=[M.ReconstructionError()])
untrained_performance = perf.check_progress(rbm, 0)
# set up the optimizer and the fit method
opt = optimizers.RMSProp(rbm,
stepsize=learning_rate,
scheduler=optimizers.PowerLawDecay(0.1))
sampler = fit.DrivenSequentialMC.from_batch(rbm, data, method='stochastic')
cd = fit.PCD(rbm,
data,
opt,
sampler,
num_epochs,
mcsteps=mc_steps,
skip=200,
metrics=[M.ReconstructionError()])
# fit the model
print('training with contrastive divergence')
cd.train()
# obtain an estimate of the reconstruction error after 1 epoch
trained_performance = perf.check_progress(rbm, 0)
assert (trained_performance['ReconstructionError'] <
untrained_performance['ReconstructionError']), \
"Reconstruction error did not decrease"
# close the HDF5 store
data.close()