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, 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