def __init__(self,
sources,
num_seq_per_batch,
input_processing=None,
target_processing=None,
source_probabilities=None,
rng_seed=None,
dtype=np.float32):
self.source_ids = {}
self.source_names = []
self.sources = []
for source in sources:
self.source_ids[source] = len(self.sources)
self.sources.append(sources[source])
self.source_names.append(source)
#self.sources = np.array(self.sources)
self.num_seq_per_batch = num_seq_per_batch
self.input_processing = none_to_list(input_processing)
self.target_processing = none_to_list(target_processing)
num_sources = len(self.sources)
if source_probabilities is None:
self.source_probabilities = [1 / num_sources] * num_sources
else:
self.source_probabilities = source_probabilities
self.rng_seed = rng_seed
self.rng = np.random.RandomState(self.rng_seed)
self._source_iterators = [None] * num_sources
self.dtype = dtype
def __init__(self, sources, num_seq_per_batch,
input_processing=None,
target_processing=None,
source_probabilities=None,
rng_seed=None):
self.sources = sources
self.num_seq_per_batch = num_seq_per_batch
self.input_processing = none_to_list(input_processing)
self.target_processing = none_to_list(target_processing)
num_sources = len(self.sources)
if source_probabilities is None:
self.source_probabilities = [1 / num_sources] * num_sources
else:
self.source_probabilities = source_probabilities
self.rng_seed = rng_seed
self.rng = np.random.RandomState(self.rng_seed)
self._source_iterators = [None] * num_sources
def __init__(self, sources, num_seq_per_batch,
input_processing=None,
target_processing=None,
source_probabilities=None,
rng_seed=None):
self.sources = sources
self.num_seq_per_batch = num_seq_per_batch
self.input_processing = none_to_list(input_processing)
self.target_processing = none_to_list(target_processing)
num_sources = len(self.sources)
if source_probabilities is None:
self.source_probabilities = [1 / num_sources] * num_sources
else:
self.source_probabilities = source_probabilities
self.rng_seed = rng_seed
self.rng = np.random.RandomState(self.rng_seed)
self._source_iterators = [None] * num_sources
def __init__(self, output_layer, description="", tags=None,
predecessor_experiment=""):
self.layers = get_all_layers(output_layer)
self._deterministic_output_func = None
self.train_iterations = 0
self.description = description
self.tags = none_to_list(tags)
for tag in self.tags:
if tag not in VALID_TAGS:
raise ValueError("{} is not a valid tag!".format(tag))
self.predecessor_experiment = predecessor_experiment
def __init__(self,
output_layer,
description="",
tags=None,
predecessor_experiment=""):
self.layers = get_all_layers(output_layer)
self._deterministic_output_func = None
self.train_iterations = 0
self.description = description
self.tags = none_to_list(tags)
for tag in self.tags:
if tag not in VALID_TAGS:
raise ValueError("{} is not a valid tag!".format(tag))
self.predecessor_experiment = predecessor_experiment