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):
super().__init__(hparams=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 default_hparams() -> Dict[str, Any]:
r"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
# (1) Same hyperparameters as in Conv1DEncoder
...
# (2) Additional hyperparameters
"num_classes": 2,
"logit_layer_kwargs": {
"use_bias": False
},
"name": "conv1d_classifier"
}
Here:
1. Same hyperparameters as in :class:`~texar.modules.Conv1DEncoder`.
See the :meth:`~texar.modules.Conv1DEncoder.default_hparams`.
An instance of :class:`~texar.modules.Conv1DEncoder` is created for
feature extraction.
2. Additional hyperparameters:
`"num_classes"`: int
Number of classes:
- If `> 0`, an additional :torch_nn:`Linear`
layer is appended to the encoder to compute the logits over
classes.
- If `<= 0`, no dense layer is appended. The number of
classes is assumed to be equal to ``out_features`` of the
final dense layer size of the encoder.
`"logit_layer_kwargs"`: dict
Keyword arguments for the logit :torch_nn:`Linear` layer
constructor, except for argument ``out_features`` which is set
to ``"num_classes"``. Ignored if no extra logit layer is
appended.
`"name"`: str
Name of the classifier.
"""
hparams = Conv1DEncoder.default_hparams()
hparams.update({
"name": "conv1d_classifier",
"num_classes": 2, # set to <=0 to avoid appending output layer
"logit_layer_kwargs": {
"in_features": hparams["out_features"],
"bias": True
}
})
return hparams
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
"""
hparams = Conv1DEncoder.default_hparams()
hparams.update({
"name": "conv1d_classifier",
"num_classes": 2, #set to <=0 to avoid appending output layer
"logit_layer_kwargs": {"use_bias": False}
})
return hparams
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
# (1) Same hyperparameters as in Conv1DEncoder
...
# (2) Additional hyperparameters
"num_classes": 2,
"logit_layer_kwargs": {
"use_bias": False
},
"name": "conv1d_classifier"
}
Here:
1. Same hyperparameters as in :class:`~texar.modules.Conv1DEncoder`.
See the :meth:`~texar.modules.Conv1DEncoder.default_hparams`.
An instance of Conv1DEncoder is created for feature extraction.
2. Additional hyperparameters:
"num_classes" : int
Number of classes:
- If **`> 0`**, an additional :tf_main:`Dense <layers/Dense>` \
layer is appended to the encoder to compute the logits over \
classes.
- If **`<= 0`**, no dense layer is appended. The number of \
classes is assumed to be the final dense layer size of the \
encoder.
"logit_layer_kwargs" : dict
Keyword arguments for the logit Dense layer constructor,
except for argument "units" which is set to "num_classes".
Ignored if no extra logit layer is appended.
"name" : str
Name of the classifier.
"""
hparams = Conv1DEncoder.default_hparams()
hparams.update({
"name": "conv1d_classifier",
"num_classes": 2, #set to <=0 to avoid appending output layer
"logit_layer_kwargs": {
"use_bias": False
}
})
return hparams