def call(self, inputs):
segment, memory = inputs
full = K.concatenate([K.zeros_like(memory[:, :, 0]), segment], axis=1)
relative = K.not_equal(K.expand_dims(segment, axis=-1),
K.expand_dims(full, axis=1))
relative = K.one_hot(K.cast(relative, 'uint8'), 2)
return [relative, K.identity(self.embeddings)]
def call(self, inputs, **kwargs):
if K.dtype(inputs) != 'int32':
inputs = K.cast(inputs, 'int32')
if self.div_val == 1:
out = K.gather(self.embeddings, inputs)
if self.embed_dim != self.output_dim or self.force_projection:
out = K.dot(out, self.projections)
else:
out = K.tile(
K.expand_dims(K.zeros_like(inputs, dtype=K.floatx()), axis=-1),
(1, ) * K.ndim(inputs) + (self.output_dim, ),
)
for i in range(len(self.cutoffs) - 1):
embed_dim = self.embed_dim // (self.div_val**i)
low, high = self.cutoffs[i], self.cutoffs[i + 1]
mask = K.cast(low <= inputs, K.floatx()) * K.cast(
inputs < high, K.floatx())
selected = K.gather(self.embeddings[i],
(inputs - low) * K.cast(mask, 'int32'))
if embed_dim != self.output_dim or self.force_projection:
projected = K.dot(selected, self.projections[i])
else:
projected = selected
out += projected * K.expand_dims(mask, axis=-1)
if self.return_embeddings or self.return_projections:
out = [out]
if self.return_embeddings:
if self.div_val == 1:
out += [self.embeddings]
else:
out += [K.identity(embed) for embed in self.embeddings]
if self.return_projections:
if self.div_val == 1:
if self.projections is not None:
out += [self.projections]
else:
out += [K.identity(proj) for proj in self.projections]
return out
def call(self, inputs):
return [
super(EmbeddingRet, self).call(inputs),
K.identity(self.embeddings),
]
def call(self, inputs, **kwargs):
return K.identity(self.bias_context)
def call(self, inputs, **kwargs):
return K.identity(inputs[0])
def call(self, inputs):
return [
super(TokenEmbedding, self).call(inputs),
K.identity(self.embeddings)
]
def call(self, inputs, **kwargs):
return [
K.identity(self.bias_context),
K.identity(self.bias_relative),
]