def __init__(self, hparams=None): ClassifierBase.__init__(self, hparams) with tf.variable_scope(self.variable_scope): encoder_hparams = utils.dict_fetch( hparams, Conv1DEncoder.default_hparams()) self._encoder = Conv1DEncoder(hparams=encoder_hparams) # Add an additional dense layer if needed self._num_classes = self._hparams.num_classes if self._num_classes > 0: if self._hparams.num_dense_layers <= 0: self._encoder.append_layer({"type": "Flatten"}) logit_kwargs = self._hparams.logit_layer_kwargs if logit_kwargs is None: logit_kwargs = {} elif not isinstance(logit_kwargs, HParams): raise ValueError( "hparams['logit_layer_kwargs'] must be a dict.") else: logit_kwargs = logit_kwargs.todict() logit_kwargs.update({"units": self._num_classes}) if 'name' not in logit_kwargs: logit_kwargs['name'] = "logit_layer" self._encoder.append_layer( {"type": "Dense", "kwargs": logit_kwargs})
def __init__(self, in_channels: int, in_features: Optional[int] = None, hparams: Optional[Union[HParams, Dict[str, Any]]] = None): ClassifierBase.__init__(self, hparams) encoder_hparams = utils.dict_fetch(hparams, Conv1DEncoder.default_hparams()) self._encoder = Conv1DEncoder(in_channels=in_channels, in_features=in_features, hparams=encoder_hparams) # Add an additional dense layer if needed self._num_classes = self._hparams.num_classes if self._num_classes > 0: if self._hparams.num_dense_layers <= 0: self._encoder.append_layer({"type": "Flatten"}) logit_kwargs = self._hparams.logit_layer_kwargs if logit_kwargs is None: logit_kwargs = {} elif not isinstance(logit_kwargs, HParams): raise ValueError( "hparams['logit_layer_kwargs'] must be a dict.") else: logit_kwargs = logit_kwargs.todict() logit_kwargs.update({"out_features": self._num_classes}) self._encoder.append_layer({ "type": "Linear", "kwargs": logit_kwargs })
def __init__(self, pretrained_model_name=None, cache_dir=None, hparams=None): ClassifierBase.__init__(self, hparams) with tf.variable_scope(self.variable_scope): # Creates the underlying encoder encoder_hparams = utils.dict_fetch( hparams, BertEncoder.default_hparams()) if encoder_hparams is not None: encoder_hparams['name'] = None self._encoder = BertEncoder( pretrained_model_name=pretrained_model_name, cache_dir=cache_dir, hparams=encoder_hparams) # Creates an dropout layer drop_kwargs = {"rate": self._hparams.dropout} layer_hparams = {"type": "Dropout", "kwargs": drop_kwargs} self._dropout_layer = layers.get_layer(hparams=layer_hparams) # Creates an additional classification layer if needed self._num_classes = self._hparams.num_classes if self._num_classes <= 0: self._logit_layer = None else: logit_kwargs = self._hparams.logit_layer_kwargs if logit_kwargs is None: logit_kwargs = {} elif not isinstance(logit_kwargs, HParams): raise ValueError( "hparams['logit_layer_kwargs'] must be a dict.") else: logit_kwargs = logit_kwargs.todict() logit_kwargs.update({"units": self._num_classes}) if 'name' not in logit_kwargs: logit_kwargs['name'] = "logit_layer" layer_hparams = {"type": "Dense", "kwargs": logit_kwargs} self._logit_layer = layers.get_layer(hparams=layer_hparams)
def __init__(self, cell=None, cell_dropout_mode=None, output_layer=None, hparams=None): ClassifierBase.__init__(self, hparams) with tf.variable_scope(self.variable_scope): # Creates the underlying encoder encoder_hparams = utils.dict_fetch( hparams, UnidirectionalRNNEncoder.default_hparams()) if encoder_hparams is not None: encoder_hparams['name'] = None self._encoder = UnidirectionalRNNEncoder( cell=cell, cell_dropout_mode=cell_dropout_mode, output_layer=output_layer, hparams=encoder_hparams) # Creates an additional classification layer if needed self._num_classes = self._hparams.num_classes if self._num_classes <= 0: self._logit_layer = None else: logit_kwargs = self._hparams.logit_layer_kwargs if logit_kwargs is None: logit_kwargs = {} elif not isinstance(logit_kwargs, HParams): raise ValueError( "hparams['logit_layer_kwargs'] must be a dict.") else: logit_kwargs = logit_kwargs.todict() logit_kwargs.update({"units": self._num_classes}) if 'name' not in logit_kwargs: logit_kwargs['name'] = "logit_layer" layer_hparams = {"type": "Dense", "kwargs": logit_kwargs} self._logit_layer = layers.get_layer(hparams=layer_hparams)