def ex(inputs):
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 100)
num_vis = inputs.shape[1]
num_hid = 400
epochs = 100
momentum = 0
initial_params = grbm.initial_params(num_hid, num_vis, 0.001, 1.0)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs,
grbm.sample_h_noisy_relu, grbm.sample_v,
neg_free_energy_grad)
learning_rate = 0.005
output_dir = utils.make_output_directory(OUTPUT_PATH)
save_params = parameters.save_hook(output_dir)
error_history = []
sparsity_history = []
def post_epoch(*args):
W_norm = utils.rescale(args[0].W)
utils.save_image(utils.tile(W_norm),
os.path.join(output_dir, ('w%i.png' % args[1])))
# Estimate sparsity from subset of data.
h_mean = grbm.sample_h_noisy_relu(args[0], inputs[0:5000], True)[1]
mean_activation = np.mean(h_mean > 0)
print 'approx mean activation: %f' % mean_activation
# The callback from optimize.sgd needs modifying so that it
# passes the reconstrcution error as an argument to make this
# work. (This was used when I did the original experiments.)
# error_history.append(args[2])
sparsity_history.append(mean_activation)
save_params(args[0], args[1])
params = optimize.sgd(f, initial_params, batches,
epochs, learning_rate,
momentum,
post_epoch=post_epoch)
with(open(os.path.join(output_dir, 'history.pickle'), 'wb')) as f:
pickle.dump(error_history, f, -1)
pickle.dump(sparsity_history, f, -1)
return params, error_history, sparsity_history
def ex1(inputs):
"""
Gaussian/Bernoulli RBM.
"""
# Can learns edge detector like filters although learning is quite
# slow and learning is very sensitive to meta-parameter selection.
# Momentum seems neccesary, without it it's difficult to learn
# anything.
# When learning on whitened data setting the fudge factor to
# something around 0.1 was important. Setting it much too much
# lower causes point filters to be learned.
# Learning does happen if you don't use whitening, but the
# features tend to be less localized when compared to the learned
# with whitening. Interestingly the reconstruction error is lower
# without whitening, but I suspect I'm comparing apples with
# oranges there.
# With only 25 hidden units I couldn't find a way to learn
# anything much. Contrast this to an autoencoder which does seem
# to learn filters in a similar situation.
# error (100 epochs) = 25.492607
# error (500 epochs) = 24.096789
# See ex1.png.
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 50)
num_vis = 64
num_hid = 100
epochs = 500
initial_params = grbm.initial_params(num_hid, num_vis, 0.05)
sample_v = functools.partial(grbm.sample_v, add_noise=False)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs,
grbm.sample_h, sample_v,
neg_free_energy_grad)
learning_rate = 0.01
momentum = meta.step(0.5, 0.9, 5)
return optimize.sgd(f, initial_params, batches,
epochs, learning_rate, momentum)
def ex1(inputs):
"""
Gaussian/Bernoulli RBM.
"""
# Can learns edge detector like filters although learning is quite
# slow and learning is very sensitive to meta-parameter selection.
# Momentum seems neccesary, without it it's difficult to learn
# anything.
# When learning on whitened data setting the fudge factor to
# something around 0.1 was important. Setting it much too much
# lower causes point filters to be learned.
# Learning does happen if you don't use whitening, but the
# features tend to be less localized when compared to the learned
# with whitening. Interestingly the reconstruction error is lower
# without whitening, but I suspect I'm comparing apples with
# oranges there.
# With only 25 hidden units I couldn't find a way to learn
# anything much. Contrast this to an autoencoder which does seem
# to learn filters in a similar situation.
# error (100 epochs) = 25.492607
# error (500 epochs) = 24.096789
# See ex1.png.
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 50)
num_vis = 64
num_hid = 100
epochs = 500
initial_params = grbm.initial_params(num_hid, num_vis, 0.05)
sample_v = functools.partial(grbm.sample_v, add_noise=False)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs, grbm.sample_h, sample_v,
neg_free_energy_grad)
learning_rate = 0.01
momentum = meta.step(0.5, 0.9, 5)
return optimize.sgd(f, initial_params, batches, epochs, learning_rate,
momentum)
def ex3(inputs):
"""
Gaussian/NReLU RBM with learned visible variances.
"""
# I found it essential to add noise/sample from the visible units
# during reconstruction. If I don't do this the variances increase
# at each epoch (I'd expect them to decrease during learning from
# their initial value of one) as does the error.
# This result was obtained by running SGD without using momentum.
# The default momentum schedule set-up a big oscillation which
# caused the error to increase over a few epochs after which we
# learning appeared to be stuck out on a plateau. More modest
# schedules (such as 0.1 for the first 10 epochs, 0.2 thereafter)
# allow learning but they don't result in any improvement in
# error.
# The variances learned are all very similar. Their mean is 0.39,
# their standard deviation is 0.04.
# A quick test suggests that smaller initial weights lead to a
# slightly lower reconstruction error.
# error (100 epochs) = 7.401834
# error (500 epochs) = 7.245722
# See ex3.png.
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 50)
num_vis = 64
num_hid = 100
epochs = 500
initial_params = grbm.initial_params(num_hid, num_vis, 0.05)
def f(params, inputs):
return rbm.cd(params, inputs,
grbm.sample_h_noisy_relu, grbm.sample_v,
grbm.neg_free_energy_grad)
learning_rate = 0.01
momentum = 0
return optimize.sgd(f, initial_params, batches,
epochs, learning_rate, momentum)
def ex3(inputs):
"""
Gaussian/NReLU RBM with learned visible variances.
"""
# I found it essential to add noise/sample from the visible units
# during reconstruction. If I don't do this the variances increase
# at each epoch (I'd expect them to decrease during learning from
# their initial value of one) as does the error.
# This result was obtained by running SGD without using momentum.
# The default momentum schedule set-up a big oscillation which
# caused the error to increase over a few epochs after which we
# learning appeared to be stuck out on a plateau. More modest
# schedules (such as 0.1 for the first 10 epochs, 0.2 thereafter)
# allow learning but they don't result in any improvement in
# error.
# The variances learned are all very similar. Their mean is 0.39,
# their standard deviation is 0.04.
# A quick test suggests that smaller initial weights lead to a
# slightly lower reconstruction error.
# error (100 epochs) = 7.401834
# error (500 epochs) = 7.245722
# See ex3.png.
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 50)
num_vis = 64
num_hid = 100
epochs = 500
initial_params = grbm.initial_params(num_hid, num_vis, 0.05)
def f(params, inputs):
return rbm.cd(params, inputs, grbm.sample_h_noisy_relu, grbm.sample_v,
grbm.neg_free_energy_grad)
learning_rate = 0.01
momentum = 0
return optimize.sgd(f, initial_params, batches, epochs, learning_rate,
momentum)
def ex2(inputs):
"""
Gaussian/NReLU RBM.
"""
# Using noisy rectified linear units for the visibles speeds up
# learning dramatically. The reconstruction error after a single
# epoch is lower (21.6986) than after 500 epochs in ex1.
# The filters learned have less noisy backgrounds than those
# learned in ex1.
# error (100 epochs) = 15.941531
# error (500 epochs) = 15.908922
# See ex2.png.
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 50)
num_vis = 64
num_hid = 100
epochs = 500
initial_params = grbm.initial_params(num_hid, num_vis, 0.05)
sample_v = functools.partial(grbm.sample_v, add_noise=False)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs,
grbm.sample_h_noisy_relu, sample_v,
neg_free_energy_grad)
learning_rate = 0.01
momentum = meta.step(0.5, 0.9, 5)
return optimize.sgd(f, initial_params, batches,
epochs, learning_rate, momentum)
def ex2(inputs):
"""
Gaussian/NReLU RBM.
"""
# Using noisy rectified linear units for the visibles speeds up
# learning dramatically. The reconstruction error after a single
# epoch is lower (21.6986) than after 500 epochs in ex1.
# The filters learned have less noisy backgrounds than those
# learned in ex1.
# error (100 epochs) = 15.941531
# error (500 epochs) = 15.908922
# See ex2.png.
inputs = utils.remove_dc(inputs)
inputs, zca = utils.zca_white(inputs, 0.1)
batches = data.BatchIterator(inputs, 50)
num_vis = 64
num_hid = 100
epochs = 500
initial_params = grbm.initial_params(num_hid, num_vis, 0.05)
sample_v = functools.partial(grbm.sample_v, add_noise=False)
neg_free_energy_grad = functools.partial(grbm.neg_free_energy_grad,
learn_sigma=False)
def f(params, inputs):
return rbm.cd(params, inputs, grbm.sample_h_noisy_relu, sample_v,
neg_free_energy_grad)
learning_rate = 0.01
momentum = meta.step(0.5, 0.9, 5)
return optimize.sgd(f, initial_params, batches, epochs, learning_rate,
momentum)