def __init__(self, name, config):
"""
Initializes the ``SentenceEmbeddings`` layer.
:param name: Name of the model (taken from the configuration file).
:param config: Parameters read from configuration file.
:type config: ``ptp.configuration.ConfigInterface``
"""
# Call base class constructors.
Model.__init__(self, name, SentenceEmbeddings, config)
WordMappings.__init__(self)
# Set key mappings.
self.key_inputs = self.stream_keys["inputs"]
self.key_outputs = self.stream_keys["outputs"]
# Force padding to a fixed length
self.fixed_padding = self.config['fixed_padding']
# Retrieve embeddings size from configuration and export it to globals.
self.embeddings_size = self.config['embeddings_size']
self.globals["embeddings_size"] = self.embeddings_size
# Create the embeddings layer.
self.logger.info("Initializing embeddings layer with vocabulary size = {} and embeddings size = {}".format(len(self.word_to_ix), self.embeddings_size))
self.embeddings = torch.nn.Embedding(len(self.word_to_ix), self.embeddings_size, padding_idx=0) # Index of self.word_to_ix['<PAD>']
# Load the embeddings first.
if self.config["pretrained_embeddings_file"] != '':
emb_vectors = emb.load_pretrained_embeddings(self.logger, self.data_folder, self.config["pretrained_embeddings_file"], self.word_to_ix, self.embeddings_size)
self.embeddings.weight = torch.nn.Parameter(emb_vectors)
# Get index of <PAD> from vocabulary.
self.pad_index = self.word_to_ix['<PAD>']
def __init__(self, name, config):
"""
Initializes the component.
:param name: Component name (read from configuration file).
:type name: str
:param config: Dictionary of parameters (read from the configuration ``.yaml`` file).
:type config: :py:class:`ptp.configuration.ConfigInterface`
"""
# Call constructor(s) of parent class(es) - in the right order!
Component.__init__(self, name, SentenceIndexer, config)
WordMappings.__init__(self)
# Set key mappings.
self.key_inputs = self.stream_keys["inputs"]
self.key_outputs = self.stream_keys["outputs"]
# Read mode from the configuration.
self.mode_reverse = self.config['reverse']
# Force padding to a fixed length
self.fixed_padding = self.config['fixed_padding']
# Wether to add <EOS> at the end of sequence
self.enable_eos_token = self.config['eos_token']
if self.mode_reverse:
# We will need reverse (index:word) mapping.
self.ix_to_word = dict((v, k) for k, v in self.word_to_ix.items())
# Get inputs distributions/indices flag.
self.use_input_distributions = self.config["use_input_distributions"]
def __init__(self, name, config):
"""
Initializes the component.
:param name: Component name (read from configuration file).
:type name: str
:param config: Dictionary of parameters (read from the configuration ``.yaml`` file).
:type config: :py:class:`ptp.configuration.ConfigInterface`
"""
# Call constructor(s) of parent class(es).
WordMappings.__init__(self, name, SentenceOneHotEncoder, config)
# Set key mappings.
self.key_inputs = self.stream_keys["inputs"]
self.key_outputs = self.stream_keys["outputs"]
def __init__(self, name, config):
"""
Initializes the component.
:param name: Component name (read from configuration file).
:type name: str
:param config: Dictionary of parameters (read from the configuration ``.yaml`` file).
:type config: :py:class:`ptp.configuration.ConfigInterface`
"""
# Call constructor(s) of parent class(es).
WordMappings.__init__(self, name, WordDecoder, config)
# Construct reverse mapping for faster processing.
self.ix_to_word = dict((v, k) for k, v in self.word_to_ix.items())
# Set key mappings.
self.key_inputs = self.stream_keys["inputs"]
self.key_outputs = self.stream_keys["outputs"]
def __init__(self, name, config):
"""
Initializes the component.
:param name: Component name (read from configuration file).
:type name: str
:param config: Dictionary of parameters (read from the configuration ``.yaml`` file).
:type config: :py:class:`ptp.configuration.ConfigInterface`
"""
# Call constructor(s) of parent class(es) - in the right order!
Component.__init__(self, name, LabelIndexer, config)
WordMappings.__init__(self)
# Set key mappings.
self.key_inputs = self.stream_keys["inputs"]
self.key_outputs = self.stream_keys["outputs"]
# Get value from configuration.
self.out_of_vocabulary_value = self.config["out_of_vocabulary_value"]