def cd_stats(rbm, v0_vmap, visible_units, hidden_units, context_units=[], k=1, mean_field_for_stats=[], mean_field_for_gibbs=[], persistent_vmap=None):
# mean_field_for_gibbs is a list of units for which 'mean_field' should be used during gibbs sampling, rather than 'sample'.
# mean_field_for_stats is a list of units for which 'mean_field' should be used to compute statistics, rather than 'sample'.
# complete units lists
visible_units = rbm.complete_units_list(visible_units)
hidden_units = rbm.complete_units_list(hidden_units)
context_units = rbm.complete_units_list(context_units)
# complete the supplied vmap
v0_vmap = rbm.complete_vmap(v0_vmap)
# extract the context vmap, because we will need to merge it into all other vmaps
context_vmap = dict((u, v0_vmap[u]) for u in context_units)
h0_activation_vmap = dict((h, h.activation(v0_vmap)) for h in hidden_units)
h0_stats_vmap, h0_gibbs_vmap = gibbs_step(rbm, v0_vmap, hidden_units, mean_field_for_stats, mean_field_for_gibbs)
# add context
h0_activation_vmap.update(context_vmap)
h0_gibbs_vmap.update(context_vmap)
h0_stats_vmap.update(context_vmap)
exp_input = [v0_vmap[u] for u in visible_units]
exp_context = [v0_vmap[u] for u in context_units]
exp_latent = [h0_gibbs_vmap[u] for u in hidden_units]
# scan requires a function that returns theano expressions, so we cannot pass vmaps in or out. annoying.
def gibbs_hvh(*args):
h0_gibbs_vmap = dict(zip(hidden_units, args))
v1_in_vmap = h0_gibbs_vmap.copy()
v1_in_vmap.update(context_vmap) # add context
v1_activation_vmap = dict((v, v.activation(v1_in_vmap)) for v in visible_units)
v1_stats_vmap, v1_gibbs_vmap = gibbs_step(rbm, v1_in_vmap, visible_units, mean_field_for_stats, mean_field_for_gibbs)
h1_in_vmap = v1_gibbs_vmap.copy()
h1_in_vmap.update(context_vmap) # add context
h1_activation_vmap = dict((h, h.activation(h1_in_vmap)) for h in hidden_units)
h1_stats_vmap, h1_gibbs_vmap = gibbs_step(rbm, h1_in_vmap, hidden_units, mean_field_for_stats, mean_field_for_gibbs)
# get the v1 values in a fixed order
v1_activation_values = [v1_activation_vmap[u] for u in visible_units]
v1_gibbs_values = [v1_gibbs_vmap[u] for u in visible_units]
v1_stats_values = [v1_stats_vmap[u] for u in visible_units]
# same for the h1 values
h1_activation_values = [h1_activation_vmap[u] for u in hidden_units]
h1_gibbs_values = [h1_gibbs_vmap[u] for u in hidden_units]
h1_stats_values = [h1_stats_vmap[u] for u in hidden_units]
return v1_activation_values + v1_stats_values + v1_gibbs_values + \
h1_activation_values + h1_stats_values + h1_gibbs_values
# support for persistent CD
if persistent_vmap is None:
chain_start = exp_latent
else:
chain_start = [persistent_vmap[u] for u in hidden_units]
# The 'outputs_info' keyword argument of scan configures how the function outputs are mapped to the inputs.
# in this case, we want the h1_gibbs_vmap values to map onto the function arguments, so they become
# h0_gibbs_vmap values in the next iteration. To this end, we construct outputs_info as follows:
outputs_info = [None] * (len(exp_input)*3) + [None] * (len(exp_latent)*2) + list(chain_start)
# 'None' indicates that this output is not used in the next iteration.
exp_output_all_list, theano_updates = theano.scan(gibbs_hvh, outputs_info = outputs_info, n_steps = k)
# we only need the final outcomes, not intermediary values
exp_output_list = [out[-1] for out in exp_output_all_list]
# reconstruct vmaps from the exp_output_list.
n_input, n_latent = len(visible_units), len(hidden_units)
vk_activation_vmap = dict(zip(visible_units, exp_output_list[0:1*n_input]))
vk_stats_vmap = dict(zip(visible_units, exp_output_list[1*n_input:2*n_input]))
vk_gibbs_vmap = dict(zip(visible_units, exp_output_list[2*n_input:3*n_input]))
hk_activation_vmap = dict(zip(hidden_units, exp_output_list[3*n_input:3*n_input+1*n_latent]))
hk_stats_vmap = dict(zip(hidden_units, exp_output_list[3*n_input+1*n_latent:3*n_input+2*n_latent]))
hk_gibbs_vmap = dict(zip(hidden_units, exp_output_list[3*n_input+2*n_latent:3*n_input+3*n_latent]))
# add the Theano updates for the persistent CD states:
if persistent_vmap is not None:
for u, v in persistent_vmap.items():
theano_updates[v] = hk_gibbs_vmap[u] # this should be the gibbs vmap, and not the stats vmap!
activation_data_vmap = v0_vmap.copy() # TODO: this doesn't really make sense to have in an activation vmap!
activation_data_vmap.update(h0_activation_vmap)
activation_model_vmap = vk_activation_vmap.copy()
activation_model_vmap.update(context_vmap)
activation_model_vmap.update(hk_activation_vmap)
stats = Stats(theano_updates) # create a new stats object
# store the computed stats in a dictionary of vmaps.
stats_data_vmap = v0_vmap.copy()
stats_data_vmap.update(h0_stats_vmap)
stats_model_vmap = vk_stats_vmap.copy()
stats_model_vmap.update(context_vmap)
stats_model_vmap.update(hk_stats_vmap)
stats.update({
'data': stats_data_vmap,
'model': stats_model_vmap,
})
stats['data_activation'] = activation_data_vmap
stats['model_activation'] = activation_model_vmap
return stats
def pt_stats(rbm, v0_vmap, visible_units, hidden_units, persistent_vmap, beta, k=1, m=1):
"""Returns stats for parallel tempering given a list of inverse temperatures beta.
"""
# TODO: use an additional shared variable in scan's output info to count
# the switch acceptances.
# v0_vmap is the batch of train data
# k is the number of sampling steps
# persistent_vmap and v0_vmap should determine the data batch size and the number of chains
# m is the number of chains that is used for model statistics
N_chains = persistent_vmap[rbm.h].shape[0]
# complete units lists
visible_units = rbm.complete_units_list(visible_units)
hidden_units = rbm.complete_units_list(hidden_units)
# complete the supplied vmap
v0_vmap = rbm.complete_vmap(v0_vmap)
mb_size = theano.tensor.cast(v0_vmap[rbm.v].shape[0], dtype=theano.config.floatX)
n_chains = theano.tensor.cast(m, dtype=theano.config.floatX)
h0_activation_vmap = dict((h, h.activation(v0_vmap)) for h in hidden_units)
# compute data dependent gradient component
h0_stats_vmap = rbm.mean_field_from_activation(h0_activation_vmap)
# scan requires a function that returns theano expressions, so we cannot pass vmaps in or out. annoying.
# for this reason, the exp lists are used and the identity of the variables is coded in the order.
exp_input = [v0_vmap[u] for u in visible_units]
exp_latent = [h0_stats_vmap[u] for u in hidden_units]
def gibbs_hvh(*args):
# generates a fixed order list to be processed by scan
h0_gibbs_vmap = dict(zip(hidden_units, args))
# what goes 'in' the rbm to compute the visibles
v1_in_vmap = h0_gibbs_vmap.copy()
v1_activation_vmap = dict((v, v.activation(v1_in_vmap)) for v in visible_units)
v1_scaled_activation_vmap = rescale_activations(v1_activation_vmap, beta)
v1_gibbs_vmap_preswap = rbm.sample_from_activation(v1_scaled_activation_vmap)
h1_in_vmap = v1_gibbs_vmap_preswap.copy()
h1_activation_vmap = dict((h, h.activation(h1_in_vmap)) for h in hidden_units)
h1_scaled_activation_vmap = rescale_activations(h1_activation_vmap, beta)
h1_gibbs_vmap_preswap = rbm.sample_from_activation(h1_scaled_activation_vmap)
# merge activations
samples = h1_gibbs_vmap_preswap
samples[rbm.v] = v1_gibbs_vmap_preswap[rbm.v]
# Select a candidate (from N_chains-1 possible pairs) pair for replica exchange
rv_i = rng.random_integers(low=0, high=N_chains-2) #the range is inclusive
rv_u = rng.uniform()
chain_pair = manipulate_vmap(samples, lambda x: x[rv_i:rv_i+2, :])
# compute relevant energy scores
e_pair = rbm.energy(chain_pair) # vector with two elements
b1, b2 = beta[rv_i][0], beta[rv_i+1][0]
r = T.exp((b1 - b2) * (e_pair[0] - e_pair[1]))
comparison = T.le(rv_u, r)
accepted = T.cast(T.switch(comparison, rv_i, -1), dtype=theano.config.floatX)
def swap(x):
return T.concatenate((x[:rv_i, :], x[rv_i+1:rv_i+2, :],
x[rv_i:rv_i+1, :], x[rv_i+2:, :]))
v1_gibbs_vmap_swap = manipulate_vmap(v1_gibbs_vmap_preswap, swap)
h1_gibbs_vmap_swap = manipulate_vmap(h1_gibbs_vmap_preswap, swap)
v1_gibbs_vmap = switch_vmap(comparison, v1_gibbs_vmap_swap, v1_gibbs_vmap_preswap)
h1_gibbs_vmap = switch_vmap(comparison, h1_gibbs_vmap_swap, h1_gibbs_vmap_preswap)
# only use the first m chains for stats
h1_stats_vmap = manipulate_vmap(h1_gibbs_vmap, lambda x: x[:m, :])
v1_stats_vmap = manipulate_vmap(v1_gibbs_vmap, lambda x: x[:m, :])
# get the v1 values in a fixed order
v1_activation_values = [v1_activation_vmap[u] for u in visible_units]
v1_gibbs_values = [v1_gibbs_vmap[u] for u in visible_units]
v1_stats_values = [v1_stats_vmap[u] for u in visible_units]
# same for the h1 values
h1_activation_values = [h1_activation_vmap[u] for u in hidden_units]
h1_gibbs_values = [h1_gibbs_vmap[u] for u in hidden_units]
h1_stats_values = [h1_stats_vmap[u] for u in hidden_units]
return v1_activation_values + v1_stats_values + v1_gibbs_values + \
h1_activation_values + h1_stats_values + h1_gibbs_values + [accepted]
chain_start = [persistent_vmap[u] for u in hidden_units]
# The 'outputs_info' keyword argument of scan configures how the function outputs are mapped to the inputs.
# in this case, we want the h1_gibbs_vmap values to map onto the function arguments, so they become
# h0_gibbs_vmap values in the next iteration. To this end, we construct outputs_info as follows:
outputs_info = [None] * (len(exp_input)*3) + [None] * (len(exp_latent)*2) + list(chain_start) + [None]
# 'None' indicates that this output is not used in the next iteration.
exp_output_all_list, theano_updates = theano.scan(gibbs_hvh, outputs_info = outputs_info, n_steps = k)
# we only need the final outcomes, not intermediary values
exp_output_list = [out[-1] for out in exp_output_all_list]
accepted = exp_output_all_list[-1]
#accepted = exp_output_list[-1]
# TODO: this messes things up when I use larger batches of data
# reconstruct vmaps from the exp_output_list.
n_input, n_latent = len(visible_units), len(hidden_units)
vk_activation_vmap = dict(zip(visible_units, exp_output_list[0:1*n_input]))
vk_stats_vmap = dict(zip(visible_units, exp_output_list[1*n_input:2*n_input]))
vk_gibbs_vmap = dict(zip(visible_units, exp_output_list[2*n_input:3*n_input]))
hk_activation_vmap = dict(zip(hidden_units, exp_output_list[3*n_input:3*n_input+1*n_latent]))
hk_stats_vmap = dict(zip(hidden_units, exp_output_list[3*n_input+1*n_latent:3*n_input+2*n_latent]))
hk_gibbs_vmap = dict(zip(hidden_units, exp_output_list[3*n_input+2*n_latent:3*n_input+3*n_latent]))
# add the Theano updates for the persistent CD states:
if persistent_vmap is not None:
for u, v in persistent_vmap.items():
theano_updates[v] = hk_gibbs_vmap[u] # this should be the gibbs vmap, and not the stats vmap!
activation_data_vmap = v0_vmap.copy() # TODO: this doesn't really make sense to have in an activation vmap!
activation_data_vmap.update(h0_activation_vmap)
activation_model_vmap = vk_activation_vmap.copy()
activation_model_vmap.update(hk_activation_vmap)
stats = Stats(theano_updates) # create a new stats object
# store the computed stats in a dictionary of vmaps.
stats_data_vmap = v0_vmap.copy()
stats_data_vmap.update(h0_stats_vmap)
stats_model_vmap = vk_stats_vmap.copy()
stats_model_vmap.update(hk_stats_vmap)
stats.update({
'data': stats_data_vmap,
'model': stats_model_vmap,
})
stats['data_activation'] = activation_data_vmap
stats['model_activation'] = activation_model_vmap
stats['accepted'] = accepted
return stats