Bug: get_config method #926
Description
What happened?
What I do not understand now is why AnyCustomLayer.get_config() still returns the correct config including my constructor parameters without me explicitly overriding the get_config method.
I have checked keras and tensorflow documentation and I haven't come across any source that says that keras automatically infer configuration from my constructor argument.
For more details: I am using kaggle notebook and this behaviour is occuring, struggling to understand why.
Relevant code
import keras
import tensorflow as tf
keras.saving.get_custom_objects().clear()
def positional_encoding(length, depth):
half_depth = depth / 2
positions = np.arange(length)[:, np.newaxis] # (length, 1)
depths = np.arange(half_depth)[np.newaxis, :] / half_depth # (1, depth/2)
angle_rates = 1 / (10000**depths) # (1, depth/2)
angle_rads = positions * angle_rates # (length, depth/2)
pos_encoding = np.concatenate(
[np.sin(angle_rads), np.cos(angle_rads)],
axis=-1) # (length, depth)
return tf.cast(pos_encoding, dtype=tf.float32)
# @keras.saving.register_keras_serializable()
class PositionalEmbedding(tf.keras.layers.Layer):
def __init__(self, vocab_size, d_model, dtype=tf.float32, **kwargs):
super().__init__(dtype=dtype, **kwargs)
assert d_model % 2 == 0, "Embedding size must be even"
self.d_model = d_model
self.embedding = tf.keras.layers.Embedding(input_dim=vocab_size,
output_dim=d_model,
mask_zero=True
)
self.pos_encoding = positional_encoding(2048, depth=d_model)
def call(self, x):
length = tf.shape(x)[1]
x = self.embedding(x)
# This factor sets the relative scale of the embedding and positonal_encoding.
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x = x + self.pos_encoding[tf.newaxis, :length, :]
return x
def compute_mask(self, *args, **kwargs):
return self.embedding.compute_mask(*args, **kwargs)
def compute_output_shape(self, *args, **kwargs):
return self.embedding.compute_output_shape(*args, **kwargs)
pos_embed = PositionalEmbedding(vocab_size=32000, d_model=128)
pos_embed.get_config()Relevant log output
{'name': 'positional_embedding_4',
'vocab_size': 32000,
'd_model': 128,
'trainable': True,
'dtype': {'module': 'keras',
'class_name': 'DTypePolicy',
'config': {'name': 'float32'},
'registered_name': None}}tensorflow_hub Version
0.13.0.dev (unstable development build)
TensorFlow Version
2.8 (latest stable release)
Other libraries
No response
Python Version
3.x
OS
Linux