def initialize_graph(recognizer, data, config, params):
# Separate attention_params to be handled differently
# when regularization is applied
attentions = recognizer.all_children().generator.transition.attention.get()
attention_params = [Selector(attention).get_parameters().values()
for attention in attentions]
logger.info(
"Initialization schemes for all bricks.\n"
"Works well only in my branch with __repr__ added to all them,\n"
"there is an issue #463 in Blocks to do that properly.")
def show_init_scheme(cur):
result = dict()
for attr in dir(cur):
if attr.endswith('_init'):
result[attr] = getattr(cur, attr)
for child in cur.children:
result[child.name] = show_init_scheme(child)
return result
logger.info(pprint.pformat(show_init_scheme(recognizer)))
observables = [] # monitored each batch
cg = recognizer.get_cost_graph(batch=True)
labels = []
labels_mask = []
for chld in recognizer.children:
lbls = VariableFilter(applications=[chld.cost], name='labels'+chld.names_postfix)(cg)
lbls_mask = VariableFilter(applications=[chld.cost], name='labels_mask'+chld.names_postfix)(cg)
if len(lbls) == 1:
labels += lbls
labels_mask += lbls_mask
batch_cost = cg.outputs[0].sum()
batch_size = rename(labels[0].shape[1], "batch_size")
# Assumes constant batch size. `aggregation.mean` is not used because
# of Blocks #514.
cost = batch_cost / batch_size
cost.name = "sequence_total_cost"
logger.info("Cost graph is built")
# Fetch variables useful for debugging.
# It is important not to use any aggregation schemes here,
# as it's currently impossible to spread the effect of
# regularization on their variables, see Blocks #514.
cost_cg = ComputationGraph(cost)
bottom_output = VariableFilter(
# We need name_regex instead of name because LookupTable calls itsoutput output_0
applications=recognizer.all_children().bottom.apply.get(), name_regex="output")(
cost_cg)
attended = VariableFilter(
applications=recognizer.all_children().generator.transition.apply.get(), name="attended")(
cost_cg)
attended_mask = VariableFilter(
applications=recognizer.all_children().generator.transition.apply.get(), name="attended_mask")(
cost_cg)
weights = VariableFilter(
applications=recognizer.all_children().generator.evaluate.get(), name="weights")(
cost_cg)
def get_renamed_list(rlist, elem_func, elem_name):
return [rename(elem_func(elem), elem_name+chld.names_postfix)
for elem,chld in zip(rlist, recognizer.children)]
max_sentence_lengths = get_renamed_list(bottom_output,
lambda e: e.shape[0],
"max_sentence_length")
max_attended_mask_lengths = get_renamed_list(attended_mask,
lambda e: e.shape[0],
"max_attended_mask_length")
max_attended_lengths = get_renamed_list(attended,
lambda e: e.shape[0],
"max_attended_length")
max_num_characters = get_renamed_list(labels,
lambda e: e.shape[0],
"max_num_characters")
mean_attended = get_renamed_list(attended,
lambda e: abs(e).mean(),
"mean_attended")
mean_bottom_output = get_renamed_list(bottom_output,
lambda e: abs(e).mean(),
"mean_bottom_output")
mask_density = get_renamed_list(labels_mask,
lambda e: e.mean(),
"mask_density")
weights_entropy = [rename(entropy(w, lm),
"weights_entropy"+chld.names_postfix)
for w, lm, chld in zip(weights, labels_mask, recognizer.children)]
observables += max_attended_lengths + max_attended_mask_lengths + max_sentence_lengths
#
# Monitoring of cost terms is tricky because of Blocks #514 - since the
# costs are annotations that are not part of the original output graph,
# they are unaffected by replacements such as dropout!!
#
cost_terms = []
for chld in recognizer.children:
chld_cost_terms = VariableFilter(applications=[chld.generator.evaluate],
name_regex='.*_nll')(cost_cg)
chld_cost_terms = [rename(var, var.name[:-4] + chld.names_postfix + '_nll')
for var in chld_cost_terms]
cost_terms += chld_cost_terms
cg = ComputationGraph([cost, batch_size] +
weights_entropy + mean_attended +
mean_bottom_output + max_num_characters +
mask_density + cost_terms)
# Regularization. It is applied explicitly to all variables
# of interest, it could not be applied to the cost only as it
# would not have effect on auxiliary variables, see Blocks #514.
reg_config = config['regularization']
regularized_cg = cg
if reg_config.get('dropout'):
drop_conf = reg_config['dropout']
bot_drop = drop_conf.get('bottom', 0.0)
if bot_drop:
logger.info('apply bottom dropout')
regularized_cg = apply_dropout(regularized_cg,
bottom_output, bot_drop)
enc_drop = drop_conf.get('encoder', 0.0)
if enc_drop:
logger.info('apply encoder dropout')
enc_bricks = reduce(lambda acc,x: acc+list(x), recognizer.all_children().encoder.children.get(), [])
enc_states = VariableFilter(bricks=enc_bricks,
name_regex='states')(regularized_cg)
regularized_cg = apply_dropout(regularized_cg,
enc_states,
enc_drop)
post_merge_drop = drop_conf.get('post_merge', 0.0)
if post_merge_drop:
logger.info('apply post_merge dropout')
pm_bricks = []
for chld in recognizer.children:
cpm_bricks = list(chld.generator.readout.post_merge.children)
cpm_bricks += cpm_bricks[-1].children
cpm_bricks = [b for b in cpm_bricks if
isinstance(b, type(chld.post_merge_activation))]
pm_bricks += cpm_bricks
regularized_cg = apply_dropout(
regularized_cg,
VariableFilter(bricks=pm_bricks, name='output')(regularized_cg),
post_merge_drop)
if reg_config.get('noise'):
logger.info('apply noise')
noise_subjects = [p for p in cg.parameters if p not in attention_params]
regularized_cg = apply_noise(cg, noise_subjects, reg_config['noise'])
train_cost = regularized_cg.outputs[0]
if reg_config.get("penalty_coof", .0) > 0:
# big warning!!!
# here we assume that:
# regularized_weights_penalty = regularized_cg.outputs[1]
train_cost = (train_cost +
reg_config.get("penalty_coof", .0) *
regularized_cg.outputs[1] / batch_size)
if reg_config.get("decay", .0) > 0:
train_cost = (train_cost + reg_config.get("decay", .0) *
l2_norm(VariableFilter(roles=[WEIGHT])(cg.parameters)) ** 2)
train_cost = train_cost.copy(name='train_cost')
gradients = None
if reg_config.get('adaptive_noise'):
logger.info('apply adaptive noise')
if ((reg_config.get("penalty_coof", .0) > 0) or
(reg_config.get("decay", .0) > 0)):
logger.error('using adaptive noise with alignment weight panalty '
'or weight decay is probably stupid')
train_cost, regularized_cg, gradients, noise_brick = apply_adaptive_noise(
cg, cg.outputs[0],
variables=cg.parameters,
num_examples=data.get_dataset('train').num_examples,
parameters=SpeechModel(regularized_cg.outputs[0]
).get_parameter_dict().values(),
**reg_config.get('adaptive_noise')
)
train_cost.name = 'train_cost'
adapt_noise_cg = ComputationGraph(train_cost)
model_prior_mean = rename(
VariableFilter(applications=[noise_brick.apply],
name='model_prior_mean')(adapt_noise_cg)[0],
'model_prior_mean')
model_cost = rename(
VariableFilter(applications=[noise_brick.apply],
name='model_cost')(adapt_noise_cg)[0],
'model_cost')
model_prior_variance = rename(
VariableFilter(applications=[noise_brick.apply],
name='model_prior_variance')(adapt_noise_cg)[0],
'model_prior_variance')
regularized_cg = ComputationGraph(
[train_cost, model_cost] +
regularized_cg.outputs +
[model_prior_mean, model_prior_variance])
observables += [
regularized_cg.outputs[1], # model cost
regularized_cg.outputs[2], # task cost
regularized_cg.outputs[-2], # model prior mean
regularized_cg.outputs[-1]] # model prior variance
if len(cost_terms):
# Please note - the aggragation (mean) is done in
# "attach_aggregation_schemes"
ct_names = [v.name for v in cost_terms]
for v in regularized_cg.outputs:
if v.name in ct_names:
observables.append(rename(v.sum()/batch_size,
v.name))
for chld in recognizer.children:
if chld.train_tags:
tags_cost = VariableFilter(applications=[chld.addTagCost],
name='output')(regularized_cg)[0]
observables += [rename(tags_cost.sum()/batch_size, 'tags_nll'+chld.names_postfix)]
# Model is weird class, we spend lots of time arguing with Bart
# what it should be. However it can already nice things, e.g.
# one extract all the parameters from the computation graphs
# and give them hierahical names. This help to notice when a
# because of some bug a parameter is not in the computation
# graph.
model = SpeechModel(train_cost)
if params:
logger.info("Load parameters from " + params)
# please note: we cannot use recognizer.load_params
# as it builds a new computation graph that dies not have
# shapred variables added by adaptive weight noise
param_values = load_parameter_values(params)
model.set_parameter_values(param_values)
parameters = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, parameters[key].get_value().shape) for key
in sorted(parameters.keys())],
width=120))
max_norm_rules = []
if reg_config.get('max_norm', False) > 0:
logger.info("Apply MaxNorm")
maxnorm_subjects = VariableFilter(roles=[WEIGHT])(cg.parameters)
if reg_config.get('max_norm_exclude_lookup', False):
maxnorm_subjects = [v for v in maxnorm_subjects
if not isinstance(get_brick(v), LookupTable)]
logger.info("Parameters covered by MaxNorm:\n"
+ pprint.pformat([name for name, p in parameters.items()
if p in maxnorm_subjects]))
logger.info("Parameters NOT covered by MaxNorm:\n"
+ pprint.pformat([name for name, p in parameters.items()
if not p in maxnorm_subjects]))
max_norm_rules = [
Restrict(VariableClipping(reg_config['max_norm'], axis=0),
maxnorm_subjects)]
return { 'observables': observables, 'max_norm_rules': max_norm_rules,
'cg': cg, 'regularized_cg' : regularized_cg, 'train_cost' : train_cost,
'cost' : cost, 'batch_size' : batch_size, 'batch_cost' : batch_cost,
'parameters' : parameters, 'gradients': gradients,
'model' : model, 'data' : data, 'recognizer' : recognizer,
'weights_entropy' : weights_entropy,
'labels_mask' : labels_mask, 'labels' : labels }
print lord
zaza = prob_function([lord], numpy.ones((1, len(lord)), dtype="int8"))[:, 0, :]
print zaza
print zaza.shape
for (ey, row) in enumerate(zaza):
print "PREDICTION PROBABILITIES FOR POSITION", ey, "LETTER", repr(lord_original[ey])
sorted_thing = [(prob, ind) for (ind, prob) in enumerate(row)]
sorted_thing.sort(reverse=True)
for (prob, ind) in sorted_thing:
print repr(map_ind_2_chr[ind]), ":", prob
print "\n"
"""
# define a function that gets the overall "sum of scores" at a given time step
readouts = VariableFilter(theano_name="readout_readout_output_0")(lstm_net.cost_model.variables)[0]
score_function = function([lstm_net.x, lstm_net.mask], readouts.sum(axis=2))
# this section of the playground has some fun rides that revolve around various correlation stuff. uncomment to access
# =)
sc = StateComputer(lstm_net.cost_model, map_chr_2_ind)
# storage for the correlations at the very end
correlation_dict = dict()
for name in sc.state_var_names:
correlation_dict[name] = numpy.zeros(lstm_net.hidden_dims[0], dtype=float)
# get validation data to run over
valid_data = H5PYDataset("bible.hdf5", which_sets=("valid",), load_in_memory=True)
data_stream = PadAndAddMasks(
DataStream.default_stream(dataset=valid_data, iteration_scheme=SequentialScheme(valid_data.num_examples,
batch_size=128)),
produces_examples=False)
