def __init__(self,
input_dim,
output_dim,
embedding_initializer='zeros',
**kwargs):
super(RelativePositionEmbedding, self).__init__(**kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.embedding_initializer = initializers.get(embedding_initializer)
def __init__(self,
units,
activation='relu',
use_bias=True,
kernel_initializer='glorot_uniform',
**kwargs):
super(FeedForward, self).__init__(**kwargs)
self.units = units
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
def __init__(self,
input_dim,
output_dim,
merge_mode='add',
embeddings_initializer='zero',
**kwargs):
super(PositionEmbedding, self).__init__(**kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.merge_mode = merge_mode
self.embeddings_initializer = initializers.get(embeddings_initializer)
def __init__(self, head_nums, head_size, key_size=None, use_bias=True, attention_scale=True,
kernel_initializer='glorot_uniform', with_residual_attention=False, **kwargs):
super(MultiHeadAttention, self).__init__(**kwargs)
self.head_nums = head_nums
self.head_size = head_size
self.key_size = key_size or head_size
self.output_dim = head_nums * head_size
self.use_bias = use_bias
self.attention_scale = attention_scale
self.kernel_initializer = initializers.get(kernel_initializer)
self.with_residual_attention = with_residual_attention # realformer
def __init__(self, center=True, scale=True, epsilon=None,
conditional=False, condition_hidden_units=None, condition_hidden_activation='linear',
condition_hidden_initializer='glorot_uniform', **kwargs):
super(LayerNormalization, self).__init__(**kwargs)
self.center = center
self.scale = scale
self.epsilon = epsilon or 1e-12
self.conditional = conditional
self.condition_hidden_units = condition_hidden_units
self.condition_hidden_activation = activations.get(condition_hidden_activation)
self.condition_hidden_initializer = initializers.get(condition_hidden_initializer)
def __init__(self,
input_dim,
output_dim,
num_attention_heads,
attn_scale_factor=2,
relative_position_bias=True,
embeddings_initializer='zeros',
**kwargs
):
super(AbsolutePositionEmbeddingTUPE, self).__init__(**kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.num_attention_heads = num_attention_heads
self.attn_scale_factor = attn_scale_factor
self.relative_position_bias = relative_position_bias
self.embedding_initializer = initializers.get(embeddings_initializer)
self.pos_scaling = float(self.output_dim / num_attention_heads * self.attn_scale_factor) ** -0.5
