def __init__(self, metrics_history=None):
example_inputter = inputters.ExampleInputter(TestInputter(), TestInputter())
super(TestModel, self).__init__(example_inputter)
if metrics_history is None:
metrics_history = {}
self.metrics_history = metrics_history
self.next_loss = tf.Variable(0)
def __init__(self, loss_history=None, metrics_history=None):
example_inputter = inputters.ExampleInputter(TestInputter(),
TestInputter())
super(TestModel, self).__init__(example_inputter)
if loss_history is None:
loss_history = []
if metrics_history is None:
metrics_history = {}
self.loss_history = loss_history
self.metrics_history = metrics_history
def __init__(self, inputter, encoder):
"""Initializes a sequence classifier.
Args:
inputter: A :class:`opennmt.inputters.Inputter` to process the
input data.
encoder: A :class:`opennmt.encoders.Encoder` to encode the input.
Raises:
ValueError: if :obj:`encoding` is invalid.
"""
example_inputter = inputters.ExampleInputter(inputter, ClassInputter())
super().__init__(example_inputter)
self.encoder = encoder
def __init__(self,
name,
features_inputter=None,
labels_inputter=None,
daisy_chain_variables=False,
dtype=None,
examples_inputter=None):
if examples_inputter is None:
examples_inputter = inputters.ExampleInputter(features_inputter, labels_inputter)
self.examples_inputter = examples_inputter
if dtype is None:
dtype = self.features_inputter.dtype
super(Model, self).__init__(name=name, dtype=dtype)
self.daisy_chain_variables = daisy_chain_variables
def __init__(self, inputter, encoder, crf_decoding=False):
"""Initializes a sequence tagger.
Args:
inputter: A :class:`opennmt.inputters.Inputter` to process the
input data.
encoder: A :class:`opennmt.encoders.Encoder` to encode the input.
crf_decoding: If ``True``, add a CRF layer after the encoder.
"""
example_inputter = inputters.ExampleInputter(inputter, TagsInputter())
super(SequenceTagger, self).__init__(example_inputter)
self.encoder = encoder
self.crf_decoding = crf_decoding
self.tagging_scheme = None
self.transition_params = None
def __init__(self,
name,
features_inputter=None,
labels_inputter=None,
daisy_chain_variables=False,
dtype=None,
examples_inputter=None):
self.name = name
self.examples_inputter = examples_inputter
if self.examples_inputter is None:
self.examples_inputter = inputters.ExampleInputter(
features_inputter, labels_inputter)
self.features_inputter = self.examples_inputter.features_inputter
self.labels_inputter = self.examples_inputter.labels_inputter
self.daisy_chain_variables = daisy_chain_variables
if dtype is None and self.features_inputter is not None:
self.dtype = self.features_inputter.dtype
else:
self.dtype = dtype or tf.float32
