tfm.nlp.models.T5TransformerParams

Transformer parameters.

tfm.nlp.models.T5TransformerParams(
    num_layers: int,
    d_model: int,
    d_kv: int,
    num_heads: int,
    d_ff: int,
    vocab_size: int,
    target_vocab_size: Optional[int] = None,
    dropout_rate: float = 0.0,
    layer_norm_epsilon: float = 1e-06,
    shared_embedding: bool = False,
    vocab_embeddings_initializer: Optional[Initializer] = None,
    relative_attention_num_buckets: int = 32,
    relative_attention_max_distance: int = 128,
    relative_embeddings_initializer: Optional[Initializer] = None,
    weight_initializer: Optional[Initializer] = tfm.nlp.models.T5TransformerParams.weight_initializer,
    bias_initializer: Optional[Initializer] = None,
    rescale_query: bool = False,
    bidirectional: bool = True,
    ffn_activations: Sequence[str] = tfm.nlp.models.T5TransformerParams.ffn_activations,
    logits_via_embedding: bool = True,
    num_decoder_layers: Optional[int] = None,
    one_hot_embedding: bool = True,
    layer_sharing: bool = False,
    use_shared_relative_position_bias: bool = True,
    return_attention_scores: bool = False
)

num_layers Dataclass field
d_model Dataclass field
d_kv Dataclass field
num_heads Dataclass field
d_ff Dataclass field
vocab_size Dataclass field
target_vocab_size Dataclass field
dropout_rate Dataclass field
layer_norm_epsilon Dataclass field
shared_embedding Dataclass field
vocab_embeddings_initializer Dataclass field
relative_attention_num_buckets Dataclass field
relative_attention_max_distance Dataclass field
relative_embeddings_initializer Dataclass field
weight_initializer Dataclass field
bias_initializer Dataclass field
rescale_query Dataclass field
bidirectional Dataclass field
ffn_activations Dataclass field
logits_via_embedding Dataclass field
num_decoder_layers Dataclass field
one_hot_embedding Dataclass field
layer_sharing Dataclass field
use_shared_relative_position_bias Dataclass field
return_attention_scores Dataclass field

Methods

weight_initializer

weight_initializer(
    dtype=None, **kwargs
)

He normal initializer.

Also available via the shortcut function tf.keras.initializers.he_normal.

It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.

Examples:

# Standalone usage:
initializer = tf.keras.initializers.HeNormal()
values = initializer(shape=(2, 2))

# Usage in a Keras layer:
initializer = tf.keras.initializers.HeNormal()
layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)

Args
seed A Python integer. Used to make the behavior of the initializer deterministic. Note that a seeded initializer will not produce the same random values across multiple calls, but multiple initializers will produce the same sequence when constructed with the same seed value.

References

__eq__

__eq__(
    other
)

bias_initializer None
bidirectional True
dropout_rate 0.0
ffn_activations ('relu',)
layer_norm_epsilon 1e-06
layer_sharing False
logits_via_embedding True
num_decoder_layers None
one_hot_embedding True
relative_attention_max_distance 128
relative_attention_num_buckets 32
relative_embeddings_initializer None
rescale_query False
return_attention_scores False
shared_embedding False
target_vocab_size None
use_shared_relative_position_bias True
vocab_embeddings_initializer None