def forward(self, inputs):
# shape notation
# i_s: input_size
# s: size
# u: units
# b_s: batch_size
# inputs shape [b_s, i_s]
hidden = self.fc_layer(inputs) # [b_s, u]
hidden = self.batch_norm(hidden) # [b_s, u]
if self.apply_glu:
hidden = glu(hidden) # [bs, s]
return hidden # [b_s, 2*s] if apply_glu else [b_s, s]
def call(self, inputs, training: bool = None, **kwargs):
hidden = self.fc_layer(inputs)
hidden = self.batch_norm(hidden, training=training)
if self.apply_glu:
hidden = glu(hidden, self.size)
return hidden
def call(self, inputs, training: bool = None, alpha: float = 0.0):
hidden = self.fc_layer(inputs)
hidden = self.batch_norm(hidden, training=training, alpha=alpha)
if self.apply_glu:
hidden = glu(hidden, self.size)
return hidden