def example_mnist_tap_machine(paysage_path=None,
num_epochs=10,
show_plot=False):
num_hidden_units = 256
batch_size = 100
learning_rate = schedules.power_law_decay(initial=0.1, coefficient=0.1)
(_, _, shuffled_filepath) = \
util.default_paths(paysage_path)
# set up the reader to get minibatches
data = batch.HDFBatch(shuffled_filepath,
'train/images',
batch_size,
transform=batch.binarize_color,
train_fraction=0.95)
# set up the model and initialize the parameters
vis_layer = layers.BernoulliLayer(data.ncols)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
rbm.initialize(data, 'glorot_normal')
perf = fit.ProgressMonitor(
data,
metrics=['ReconstructionError', 'EnergyDistance', 'HeatCapacity'])
opt = optimizers.Gradient(stepsize=learning_rate,
tolerance=1e-4,
ascent=True)
sampler = fit.DrivenSequentialMC.from_batch(rbm, data)
sgd = fit.SGD(rbm,
data,
opt,
num_epochs,
sampler=sampler,
method=fit.tap,
monitor=perf)
# fit the model
print('Training with stochastic gradient ascent using TAP expansion')
sgd.train()
util.show_metrics(rbm, perf)
valid = data.get('validate')
util.show_reconstructions(rbm,
valid,
fit,
show_plot,
n_recon=10,
vertical=False)
util.show_fantasy_particles(rbm, valid, fit, show_plot, n_fantasy=25)
util.show_weights(rbm, show_plot, n_weights=25)
# close the HDF5 store
data.close()
print("Done")
def test_grbm_save():
vis_layer = layers.BernoulliLayer(num_vis)
hid_layer = layers.GaussianLayer(num_hid)
grbm = model.Model([vis_layer, hid_layer])
with tempfile.NamedTemporaryFile() as file:
store = pandas.HDFStore(file.name, mode='w')
grbm.save(store)
store.close()
def example_mnist_deep_rbm(paysage_path=None, num_epochs=10, show_plot=False):
num_hidden_units = 500
batch_size = 100
learning_rate = schedules.power_law_decay(initial=0.01, coefficient=0.1)
mc_steps = 1
(_, _, shuffled_filepath) = \
util.default_paths(paysage_path)
# set up the reader to get minibatches
data = batch.HDFBatch(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_1_layer = layers.BernoulliLayer(num_hidden_units)
hid_2_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_1_layer, hid_2_layer])
rbm.initialize(data)
metrics = [
'ReconstructionError', 'EnergyDistance', 'EnergyGap', 'EnergyZscore',
'HeatCapacity'
]
perf = fit.ProgressMonitor(data, metrics=metrics)
# set up the optimizer and the fit method
opt = optimizers.ADAM(stepsize=learning_rate)
sampler = fit.SequentialMC.from_batch(rbm, data)
cd = fit.SGD(rbm,
data,
opt,
num_epochs,
method=fit.pcd,
sampler=sampler,
mcsteps=mc_steps,
monitor=perf)
# fit the model
print('training with contrastive divergence')
cd.train_layerwise()
# evaluate the model
util.show_metrics(rbm, perf)
valid = data.get('validate')
util.show_reconstructions(rbm, valid, fit, show_plot)
util.show_fantasy_particles(rbm, valid, fit, show_plot)
util.show_weights(rbm, show_plot)
# close the HDF5 store
data.close()
print("Done")
def test_grbm_from_config():
vis_layer = layers.BernoulliLayer(num_vis)
hid_layer = layers.GaussianLayer(num_hid)
grbm = model.Model([vis_layer, hid_layer])
config = grbm.get_config()
rbm_from_config = model.Model.from_config(config)
config_from_config = rbm_from_config.get_config()
assert config == config_from_config
def example_mnist_grbm(paysage_path=None, num_epochs=10, show_plot=False):
num_hidden_units = 500
batch_size = 50
learning_rate = 0.001 # gaussian rbm usually requires smaller learnign rate
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=transform,
train_fraction=0.99)
# set up the model and initialize the parameters
vis_layer = layers.GaussianLayer(data.ncols)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
rbm.initialize(data)
metrics = [
'ReconstructionError', 'EnergyDistance', 'EnergyGap', 'EnergyZscore'
]
perf = fit.ProgressMonitor(data, metrics=metrics)
opt = optimizers.ADAM(stepsize=learning_rate,
scheduler=optimizers.PowerLawDecay(0.1))
sampler = fit.DrivenSequentialMC.from_batch(rbm, data, method='stochastic')
cd = fit.SGD(rbm,
data,
opt,
num_epochs,
method=fit.pcd,
sampler=sampler,
mcsteps=mc_steps,
monitor=perf)
# fit the model
print('training with contrastive divergence')
cd.train()
# evaluate the model
util.show_metrics(rbm, perf)
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_state_for_grad_DrivenSequentialMC():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.BernoulliLayer(num_visible_units)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
data_state = State.from_visible(vdata, rbm)
dropout_scale = State.dropout_rescale(rbm)
# since we set no dropout, dropout_scale should be None
assert dropout_scale is None
for u in [
'markov_chain', 'mean_field_iteration', 'deterministic_iteration'
]:
# set up the sampler
sampler = fit.DrivenSequentialMC(rbm, updater=u, clamped=[0])
sampler.set_state(data_state)
# update the state of the hidden layer
grad_state = sampler.state_for_grad(1, dropout_scale)
assert be.allclose(data_state.units[0], grad_state.units[0]), \
"visible layer is clamped, and shouldn't get updated: {}".format(u)
assert not be.allclose(data_state.units[1], grad_state.units[1]), \
"hidden layer is not clamped, and should get updated: {}".format(u)
# compute the conditional mean with the layer function
ave = rbm.layers[1].conditional_mean(
rbm._connected_rescaled_units(1, data_state, dropout_scale),
rbm._connected_weights(1))
assert be.allclose(ave, grad_state.units[1]), \
"hidden layer of grad_state should be conditional mean: {}".format(u)
def test_exponential_derivatives():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.ExponentialLayer(num_visible_units)
hid_layer = layers.ExponentialLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
# for the exponential layers, we need a > 0, b > 0, and W < 0
a = be.rand((num_visible_units, ))
b = be.rand((num_hidden_units, ))
W = -be.rand((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
vdata_scaled = rbm.layers[0].rescale(vdata)
# compute the mean of the hidden layer
hid_mean = rbm.layers[1].conditional_mean([vdata], [rbm.weights[0].W()])
hid_mean_scaled = rbm.layers[1].rescale(hid_mean)
# compute the derivatives
d_visible_loc = be.mean(vdata, axis=0)
d_hidden_loc = be.mean(hid_mean_scaled, axis=0)
d_W = -be.batch_outer(vdata, hid_mean_scaled) / len(vdata)
# compute the derivatives using the layer functions
vis_derivs = rbm.layers[0].derivatives(vdata, [hid_mean_scaled],
[rbm.weights[0].W_T()])
hid_derivs = rbm.layers[1].derivatives(hid_mean, [vdata_scaled],
[rbm.weights[0].W()])
weight_derivs = rbm.weights[0].derivatives(vdata, hid_mean_scaled)
assert be.allclose(d_visible_loc, vis_derivs.loc), \
"derivative of visible loc wrong in exponential-exponential rbm"
assert be.allclose(d_hidden_loc, hid_derivs.loc), \
"derivative of hidden loc wrong in exponential-exponential rbm"
assert be.allclose(d_W, weight_derivs.matrix), \
"derivative of weights wrong in exponential-exponential rbm"
def run(paysage_path=None, num_epochs=10, show_plot=False):
num_hidden_units = 500
batch_size = 100
learning_rate = schedules.PowerLawDecay(initial=0.001, coefficient=0.1)
mc_steps = 1
(_, _, shuffled_filepath) = \
util.default_paths(paysage_path)
# set up the reader to get minibatches
data = batch.HDFBatch(shuffled_filepath,
'train/images',
batch_size,
transform=pre.binarize_color,
train_fraction=0.99)
# set up the model and initialize the parameters
vis_layer = layers.BernoulliLayer(data.ncols)
hid_layer = layers.GaussianLayer(num_hidden_units)
hid_layer.set_fixed_params(["loc", "log_var"])
rbm = model.Model([vis_layer, hid_layer])
rbm.initialize(data, method="glorot_normal")
metrics = ['ReconstructionError', 'EnergyDistance', 'EnergyGap',
'EnergyZscore', 'HeatCapacity', 'WeightSparsity', 'WeightSquare']
perf = fit.ProgressMonitor(data, metrics=metrics)
# set up the optimizer and the fit method
opt = optimizers.ADAM(stepsize=learning_rate)
sampler = fit.DrivenSequentialMC.from_batch(rbm, data)
cd = fit.SGD(rbm, data, opt, num_epochs, sampler, method=fit.pcd,
mcsteps=mc_steps, monitor=perf)
# fit the model
print('training with contrastive divergence')
cd.train()
# evaluate the model
util.show_metrics(rbm, perf)
valid = data.get('validate')
util.show_reconstructions(rbm, valid, fit, show_plot,
n_recon=10, vertical=False, num_to_avg=10)
util.show_fantasy_particles(rbm, valid, fit, show_plot, n_fantasy=25)
util.show_weights(rbm, show_plot, n_weights=25)
# close the HDF5 store
data.close()
print("Done")
def test_random_grad():
num_visible_units = 100
num_hidden_units = 50
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.BernoulliLayer(num_visible_units)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# create a gradient object filled with random numbers
gu.random_grad(rbm)
def test_clamped_SequentialMC():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
steps = 1
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.BernoulliLayer(num_visible_units)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
data_state = State.from_visible(vdata, rbm)
dropout_scale = State.dropout_rescale(rbm)
# since we set no dropout, dropout_scale should be None
assert dropout_scale is None
for u in [
'markov_chain', 'mean_field_iteration', 'deterministic_iteration'
]:
# set up the sampler with the visible layer clamped
sampler = fit.SequentialMC(rbm, updater=u, clamped=[0])
sampler.set_state(data_state)
# update the sampler state and check the output
sampler.update_state(steps, dropout_scale)
assert be.allclose(data_state.units[0], sampler.state.units[0]), \
"visible layer is clamped, and shouldn't get updated: {}".format(u)
assert not be.allclose(data_state.units[1], sampler.state.units[1]), \
"hidden layer is not clamped, and should get updated: {}".format(u)
def test_exponential_conditional_params():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.ExponentialLayer(num_visible_units)
hid_layer = layers.ExponentialLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
# for the exponential layers, we need a > 0, b > 0, and W < 0
a = be.rand((num_visible_units, ))
b = be.rand((num_hidden_units, ))
W = -be.rand((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
hdata = rbm.layers[1].random((batch_size, num_hidden_units))
# compute conditional parameters
hidden_rate = -be.dot(vdata, W) # (batch_size, num_hidden_units)
hidden_rate += be.broadcast(b, hidden_rate)
visible_rate = -be.dot(hdata,
be.transpose(W)) # (batch_size, num_visible_units)
visible_rate += be.broadcast(a, visible_rate)
# compute the conditional parameters using the layer functions
hidden_rate_func = rbm.layers[1]._conditional_params([vdata],
[rbm.weights[0].W()])
visible_rate_func = rbm.layers[0]._conditional_params(
[hdata], [rbm.weights[0].W_T()])
assert be.allclose(hidden_rate, hidden_rate_func), \
"hidden rate wrong in exponential-exponential rbm"
assert be.allclose(visible_rate, visible_rate_func), \
"visible rate wrong in exponential-exponential rbm"
def test_bernoulli_conditional_params():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.BernoulliLayer(num_visible_units)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
hdata = rbm.layers[1].random((batch_size, num_hidden_units))
# compute conditional parameters
hidden_field = be.dot(vdata, W) # (batch_size, num_hidden_units)
hidden_field += b
visible_field = be.dot(hdata,
be.transpose(W)) # (batch_size, num_visible_units)
visible_field += a
# compute conditional parameters with layer funcitons
hidden_field_layer = rbm.layers[1]._conditional_params(
[vdata], [rbm.weights[0].W()])
visible_field_layer = rbm.layers[0]._conditional_params(
[hdata], [rbm.weights[0].W_T()])
assert be.allclose(hidden_field, hidden_field_layer), \
"hidden field wrong in bernoulli-bernoulli rbm"
assert be.allclose(visible_field, visible_field_layer), \
"visible field wrong in bernoulli-bernoulli rbm"
def test_bernoulli_GFE_derivatives():
num_units = 500
layer_1 = layers.BernoulliLayer(num_units)
layer_2 = layers.BernoulliLayer(num_units)
layer_3 = layers.BernoulliLayer(num_units)
rbm = model.Model([layer_1, layer_2, layer_3])
for i in range(len(rbm.weights)):
rbm.weights[i].params.matrix[:] = \
0.01 * be.randn(rbm.weights[i].shape)
for lay in rbm.layers:
lay.params.loc[:] = be.rand_like(lay.params.loc)
state = rbm.compute_StateTAP(init_lr=0.1, tol=1e-7, max_iters=50)
GFE = rbm.gibbs_free_energy(state)
lr = 0.1
gogogo = True
grad = rbm.grad_TAP_free_energy(0.1, 1e-7, 50)
while gogogo:
cop = deepcopy(rbm)
lr_mul = partial(be.tmul, -lr)
delta = gu.grad_apply(lr_mul, grad)
cop.parameter_update(delta)
cop_state = cop.compute_StateTAP(init_lr=0.1, tol=1e-7, max_iters=50)
cop_GFE = cop.gibbs_free_energy(cop_state)
regress = cop_GFE - GFE < 0.0
print(lr, cop_GFE, GFE, cop_GFE - GFE, regress)
if regress:
if lr < 1e-6:
assert False, \
"TAP FE gradient is not working properly for Bernoulli models"
break
else:
lr *= 0.5
else:
break
def test_grbm_reload():
vis_layer = layers.BernoulliLayer(num_vis)
hid_layer = layers.GaussianLayer(num_hid)
# create some extrinsics
grbm = model.Model([vis_layer, hid_layer])
with tempfile.NamedTemporaryFile() as file:
# save the model
store = pandas.HDFStore(file.name, mode='w')
grbm.save(store)
store.close()
# reload
store = pandas.HDFStore(file.name, mode='r')
grbm_reload = model.Model.from_saved(store)
store.close()
# check the two models are consistent
vis_data = vis_layer.random((num_samples, num_vis))
data_state = model.State.from_visible(vis_data, grbm)
vis_orig = grbm.deterministic_iteration(1, data_state).units[0]
vis_reload = grbm_reload.deterministic_iteration(1, data_state).units[0]
assert be.allclose(vis_orig, vis_reload)
def test_bernoulli_GFE_magnetization_gradient():
num_units = 500
layer_1 = layers.BernoulliLayer(num_units)
layer_2 = layers.BernoulliLayer(num_units)
layer_3 = layers.BernoulliLayer(num_units)
layer_4 = layers.BernoulliLayer(num_units)
rbm = model.Model([layer_1, layer_2, layer_3, layer_4])
for i in range(len(rbm.weights)):
rbm.weights[i].params.matrix[:] = \
0.01 * be.randn(rbm.weights[i].shape)
for lay in rbm.layers:
lay.params.loc[:] = be.rand_like(lay.params.loc)
state = mu.StateTAP.from_model_rand(rbm)
GFE = rbm.gibbs_free_energy(state)
lr = 0.001
gogogo = True
grad = rbm._TAP_magnetization_grad(state)
while gogogo:
cop = deepcopy(state)
for i in range(rbm.num_layers):
cop.cumulants[
i].mean[:] = state.cumulants[i].mean + lr * grad[i].mean
GFE_next = rbm.gibbs_free_energy(cop)
regress = GFE_next - GFE < 0.0
if regress:
if lr < 1e-6:
assert False,\
"Bernoulli GFE magnetization gradient is wrong"
break
else:
lr *= 0.5
else:
break
def test_independent():
"""
Test sampling from an rbm with two layers connected by a weight matrix that
contains all zeros, so that the layers are independent.
Note:
This test compares values estimated by *sampling* to values computed
analytically. It can fail for small batch_size, or strict tolerances,
even if everything is working propery.
"""
num_visible_units = 20
num_hidden_units = 10
batch_size = 1000
steps = 100
mean_tol = 0.1
corr_tol = 0.2
# set a seed for the random number generator
be.set_seed()
layer_types = [layers.BernoulliLayer, layers.GaussianLayer]
for layer_type in layer_types:
# set up some layer and model objects
vis_layer = layer_type(num_visible_units)
hid_layer = layer_type(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.rand((num_visible_units, ))
b = be.rand((num_hidden_units, ))
W = be.zeros((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
if layer_type == layers.GaussianLayer:
log_var_a = be.randn((num_visible_units, ))
log_var_b = be.randn((num_hidden_units, ))
rbm.layers[0].params.log_var[:] = log_var_a
rbm.layers[1].params.log_var[:] = log_var_b
# initialize a state
state = State.from_model(batch_size, rbm)
dropout = State.dropout_rescale(rbm)
# run a markov chain to update the state
state = rbm.markov_chain(steps, state, dropout)
# compute the mean
state_for_moments = State.from_model(1, rbm)
sample_mean = [
be.mean(state.units[i], axis=0) for i in range(len(state.units))
]
model_mean = [
rbm.layers[i].conditional_mean(
rbm._connected_rescaled_units(i, state_for_moments, dropout),
rbm._connected_weights(i)) for i in range(rbm.num_layers)
]
# check that the means are roughly equal
for i in range(rbm.num_layers):
ave = sample_mean[i]
close = be.allclose(ave,
model_mean[i][0],
rtol=mean_tol,
atol=mean_tol)
assert close, "{0} {1}: sample mean does not match model mean".format(
layer_type, i)
# check the cross correlation between the layers
crosscov = be.cov(state.units[0], state.units[1])
norm = be.outer(be.std(state.units[0], axis=0),
be.std(state.units[1], axis=0))
crosscorr = be.divide(norm, crosscov)
assert be.tmax(
be.tabs(crosscorr)
) < corr_tol, "{} cross correlation too large".format(layer_type)
def test_conditional_sampling():
"""
Test sampling from one layer conditioned on the state of another layer.
Note:
This test compares values estimated by *sampling* to values computed
analytically. It can fail for small batch_size, or strict tolerances,
even if everything is working propery.
"""
num_visible_units = 20
num_hidden_units = 10
steps = 1000
mean_tol = 0.1
# set a seed for the random number generator
be.set_seed()
layer_types = [layers.BernoulliLayer, layers.GaussianLayer]
for layer_type in layer_types:
# set up some layer and model objects
vis_layer = layer_type(num_visible_units)
hid_layer = layer_type(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.rand((num_visible_units, ))
b = be.rand((num_hidden_units, ))
W = 10 * be.rand((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
if layer_type == layers.GaussianLayer:
log_var_a = be.randn((num_visible_units, ))
log_var_b = be.randn((num_hidden_units, ))
rbm.layers[0].params.log_var[:] = log_var_a
rbm.layers[1].params.log_var[:] = log_var_b
# initialize a state
state = State.from_model(1, rbm)
dropout = State.dropout_rescale(rbm)
# set up a calculator for the moments
moments = mu.MeanVarianceArrayCalculator()
for _ in range(steps):
moments.update(rbm.layers[0].conditional_sample(
rbm._connected_rescaled_units(0, state, dropout),
rbm._connected_weights(0)))
model_mean = rbm.layers[0].conditional_mean(
rbm._connected_rescaled_units(0, state, dropout),
rbm._connected_weights(0))
ave = moments.mean
close = be.allclose(ave, model_mean[0], rtol=mean_tol, atol=mean_tol)
assert close, "{} conditional mean".format(layer_type)
if layer_type == layers.GaussianLayer:
model_mean, model_var = rbm.layers[0]._conditional_params(
rbm._connected_rescaled_units(0, state, dropout),
rbm._connected_weights(0))
close = be.allclose(be.sqrt(moments.var),
be.sqrt(model_var[0]),
rtol=mean_tol,
atol=mean_tol)
assert close, "{} conditional standard deviation".format(
layer_type)
def test_grbm_config():
vis_layer = layers.BernoulliLayer(num_vis)
hid_layer = layers.GaussianLayer(num_hid)
grbm = model.Model([vis_layer, hid_layer])
grbm.get_config()
def test_hopfield_construction():
vis_layer = layers.BernoulliLayer(num_vis)
hid_layer = layers.GaussianLayer(num_hid)
rbm = model.Model([vis_layer, hid_layer])
def test_rmb_construction():
vis_layer = layers.BernoulliLayer(num_vis)
hid_layer = layers.BernoulliLayer(num_hid)
rbm = model.Model([vis_layer, hid_layer])
def test_rbm(paysage_path=None):
num_hidden_units = 50
batch_size = 50
num_epochs = 1
learning_rate = schedules.PowerLawDecay(initial=0.01, coefficient=0.1)
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.HDFBatch(shuffled_filepath,
'train/images',
batch_size,
transform=pre.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 = model.Model([vis_layer, hid_layer])
rbm.initialize(data)
# obtain initial estimate of the reconstruction error
perf = fit.ProgressMonitor(data, metrics=['ReconstructionError'])
untrained_performance = perf.check_progress(rbm)
# set up the optimizer and the fit method
opt = optimizers.RMSProp(stepsize=learning_rate)
sampler = fit.DrivenSequentialMC.from_batch(rbm, data)
cd = fit.SGD(rbm,
data,
opt,
num_epochs,
sampler,
method=fit.pcd,
mcsteps=mc_steps,
monitor=perf)
# 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)
assert (trained_performance['ReconstructionError'] <
untrained_performance['ReconstructionError']), \
"Reconstruction error did not decrease"
# close the HDF5 store
data.close()
def run(paysage_path=None, num_epochs=10, show_plot=False):
num_hidden_units = 100
batch_size = 100
learning_rate = schedules.PowerLawDecay(initial=0.012, coefficient=0.1)
mc_steps = 1
(_, _, shuffled_filepath) = \
util.default_paths(paysage_path)
# set up the reader to get minibatches
data = batch.HDFBatch(shuffled_filepath,
'train/images',
batch_size,
transform=pre.binarize_color,
train_fraction=0.95)
# set up the model and initialize the parameters
vis_layer = layers.BernoulliLayer(data.ncols)
hid_1_layer = layers.BernoulliLayer(num_hidden_units)
hid_2_layer = layers.BernoulliLayer(num_hidden_units)
hid_3_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_1_layer, hid_2_layer, hid_3_layer])
rbm.initialize(data, method='glorot_normal')
print("Norms of the weights before training")
util.weight_norm_histogram(rbm, show_plot=show_plot)
# small penalties prevent the weights from consolidating
rbm.weights[1].add_penalty({'matrix': pen.logdet_penalty(0.001)})
rbm.weights[2].add_penalty({'matrix': pen.logdet_penalty(0.001)})
metrics = [
'ReconstructionError', 'EnergyDistance', 'EnergyGap', 'EnergyZscore',
'HeatCapacity', 'WeightSparsity', 'WeightSquare'
]
perf = fit.ProgressMonitor(data, metrics=metrics)
# set up the optimizer and the fit method
opt = optimizers.ADAM(stepsize=learning_rate)
cd = fit.LayerwisePretrain(rbm,
data,
opt,
num_epochs,
method=fit.pcd,
mcsteps=mc_steps,
metrics=metrics)
# fit the model
print('training with persistent contrastive divergence')
cd.train()
# evaluate the model
util.show_metrics(rbm, perf)
valid = data.get('validate')
util.show_reconstructions(rbm, valid, fit, show_plot, num_to_avg=10)
util.show_fantasy_particles(rbm, valid, fit, show_plot)
from math import sqrt
dim = tuple([28] +
[int(sqrt(num_hidden_units)) for _ in range(rbm.num_weights)])
util.show_weights(rbm, show_plot, dim=dim, n_weights=16)
util.show_one_hot_reconstructions(rbm,
fit,
dim=28,
n_recon=16,
num_to_avg=1)
print("Norms of the weights after training")
util.weight_norm_histogram(rbm, show_plot=show_plot)
# close the HDF5 store
data.close()
print("Done")
def run(paysage_path=None, num_epochs=10, show_plot=False):
num_hidden_units = 256
batch_size = 100
learning_rate = schedules.PowerLawDecay(initial=0.01, coefficient=0.1)
mc_steps = 1
(_, _, shuffled_filepath) = \
util.default_paths(paysage_path)
# set up the reader to get minibatches
import pandas
data = batch.InMemoryBatch(pre.binarize_color(
be.float_tensor(
pandas.read_hdf(shuffled_filepath,
key='train/images').as_matrix())),
batch_size,
train_fraction=0.95)
# set up the model and initialize the parameters
vis_layer = layers.BernoulliLayer(data.ncols)
hid_layer = layers.BernoulliLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
rbm.weights[0].add_penalty({'matrix': pen.l2_penalty(0.001)})
rbm.initialize(data, method='glorot_normal')
metrics = [
'ReconstructionError', 'EnergyDistance', 'EnergyGap', 'EnergyZscore',
'HeatCapacity', 'WeightSparsity', 'WeightSquare'
]
perf = fit.ProgressMonitor(data, metrics=metrics)
# set up the optimizer and the fit method
opt = optimizers.ADAM(stepsize=learning_rate)
sampler = fit.DrivenSequentialMC.from_batch(rbm, data)
cd = fit.SGD(rbm,
data,
opt,
num_epochs,
sampler,
method=fit.pcd,
mcsteps=mc_steps,
monitor=perf)
# fit the model
print('training with contrastive divergence')
cd.train()
# evaluate the model
util.show_metrics(rbm, perf)
valid = data.get('validate')
util.show_reconstructions(rbm,
valid,
fit,
show_plot,
n_recon=10,
vertical=False,
num_to_avg=10)
util.show_fantasy_particles(rbm, valid, fit, show_plot, n_fantasy=25)
util.show_weights(rbm, show_plot, n_weights=25)
# close the HDF5 store
data.close()
print("Done")
def test_gaussian_derivatives():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.GaussianLayer(num_visible_units)
hid_layer = layers.GaussianLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
log_var_a = 0.1 * be.randn((num_visible_units, ))
log_var_b = 0.1 * be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.layers[0].params.log_var[:] = log_var_a
rbm.layers[1].params.log_var[:] = log_var_b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
visible_var = be.exp(log_var_a)
vdata_scaled = vdata / visible_var
# compute the mean of the hidden layer
hid_mean = rbm.layers[1].conditional_mean([vdata_scaled],
[rbm.weights[0].W()])
hidden_var = be.exp(log_var_b)
hid_mean_scaled = rbm.layers[1].rescale(hid_mean)
# compute the derivatives
d_vis_loc = -be.mean(vdata_scaled, axis=0)
d_vis_logvar = -0.5 * be.mean(be.square(be.subtract(a, vdata)), axis=0)
d_vis_logvar += be.batch_dot(
hid_mean_scaled, be.transpose(W), vdata, axis=0) / len(vdata)
d_vis_logvar /= visible_var
d_hid_loc = -be.mean(hid_mean_scaled, axis=0)
d_hid_logvar = -0.5 * be.mean(be.square(hid_mean - b), axis=0)
d_hid_logvar += be.batch_dot(vdata_scaled, W, hid_mean,
axis=0) / len(hid_mean)
d_hid_logvar /= hidden_var
d_W = -be.batch_outer(vdata_scaled, hid_mean_scaled) / len(vdata_scaled)
# compute the derivatives using the layer functions
vis_derivs = rbm.layers[0].derivatives(vdata, [hid_mean_scaled],
[rbm.weights[0].W_T()])
hid_derivs = rbm.layers[1].derivatives(hid_mean, [vdata_scaled],
[rbm.weights[0].W()])
weight_derivs = rbm.weights[0].derivatives(vdata_scaled, hid_mean_scaled)
assert be.allclose(d_vis_loc, vis_derivs[0].loc), \
"derivative of visible loc wrong in gaussian-gaussian rbm"
assert be.allclose(d_hid_loc, hid_derivs[0].loc), \
"derivative of hidden loc wrong in gaussian-gaussian rbm"
assert be.allclose(d_vis_logvar, vis_derivs[0].log_var, rtol=1e-05, atol=1e-01), \
"derivative of visible log_var wrong in gaussian-gaussian rbm"
assert be.allclose(d_hid_logvar, hid_derivs[0].log_var, rtol=1e-05, atol=1e-01), \
"derivative of hidden log_var wrong in gaussian-gaussian rbm"
assert be.allclose(d_W, weight_derivs[0].matrix), \
"derivative of weights wrong in gaussian-gaussian rbm"
def test_gaussian_conditional_params():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.GaussianLayer(num_visible_units)
hid_layer = layers.GaussianLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
log_var_a = 0.1 * be.randn((num_visible_units, ))
log_var_b = 0.1 * be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.layers[0].params.log_var[:] = log_var_a
rbm.layers[1].params.log_var[:] = log_var_b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
hdata = rbm.layers[1].random((batch_size, num_hidden_units))
# compute the variance
visible_var = be.exp(log_var_a)
hidden_var = be.exp(log_var_b)
# rescale the data
vdata_scaled = vdata / visible_var
hdata_scaled = hdata / hidden_var
# test rescale
assert be.allclose(vdata_scaled, rbm.layers[0].rescale(vdata)),\
"visible rescale wrong in gaussian-gaussian rbm"
assert be.allclose(hdata_scaled, rbm.layers[1].rescale(hdata)),\
"hidden rescale wrong in gaussian-gaussian rbm"
# compute the mean
hidden_mean = be.dot(vdata_scaled, W) # (batch_size, num_hidden_units)
hidden_mean += b
visible_mean = be.dot(hdata_scaled,
be.transpose(W)) # (batch_size, num_hidden_units)
visible_mean += a
# update the conditional parameters using the layer functions
vis_mean_func, vis_var_func = rbm.layers[0]._conditional_params(
[hdata_scaled], [rbm.weights[0].W_T()])
hid_mean_func, hid_var_func = rbm.layers[1]._conditional_params(
[vdata_scaled], [rbm.weights[0].W()])
assert be.allclose(visible_var, vis_var_func),\
"visible variance wrong in gaussian-gaussian rbm"
assert be.allclose(hidden_var, hid_var_func),\
"hidden variance wrong in gaussian-gaussian rbm"
assert be.allclose(visible_mean, vis_mean_func),\
"visible mean wrong in gaussian-gaussian rbm"
assert be.allclose(hidden_mean, hid_mean_func),\
"hidden mean wrong in gaussian-gaussian rbm"
