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Description:

paramsflow 0.8.2

params-flow provides an alternative style for defining your Keras model
or layer configuration in order to reduce the boilerplate code related to
passing and (de)serializing your model/layer configuration arguments.
params-flow encourages this:
import params_flow as pf

class MyDenseLayer(pf.Layer): # using params_flow Layer/Model instead of Keras ones
class Params(pf.Layer.Params): # extend one or more base Params configurations
num_outputs = None # declare all configuration arguments
activation = "gelu" # provide or override super() defaults
# do not define an __init__()

def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.params.num_outputs]) # access config arguments
which would be sufficient to pass the right configuration arguments to the
super layer/model, as well as take care of (de)serialization, so you can concentrate
on the build() or call() implementations, instead of writing boilerplate
code like this:
from tf.keras.layers import Layer

class MyDenseLayer(Layer):
def __init__(self,
num_outputs, # put all of the layer configuration in the constructor
activation = "gelu", # provide defaults
**kwargs): # allow base layer configuration to be passed to super
self.num_outputs = num_outputs
self.activation = activation
super().__init__(**kwargs)

def build(self, in_shape):
self.kernel = self.add_variable("kernel",
[int(in_shape[-1]),
self.num_outputs]) # access config arguments

def get_config(self): # serialize layer configuration, __init__() is the deserializer
config = {
'num_outputs': self.num_outputs,
'activation': self.activation
}
base_config = super().get_config()
return dict(list(base_config.items())) + list(config.items())

NEWS

04.Apr.2020 - refactored to use WithParams mixin from kpe/py-params. Make
sure to use _construct() instead of __init__() in your Layer and Model subclasses.
Breaking Change - _construct() signature has changed, please update
your Layer and Model subclasses from:
def _construct(self, params: Params):
...
to:
def _construct(self, **kwargs):
super()._construct(**kwargs)
params = self.params
...

11.Sep.2019 - LookAhead optimizer wrapper implementation for efficient non eager graph mode execution (TPU) added.
05.Sep.2019 - LookAhead optimizer implementation as Keras callback added.
04.Sep.2019 - RAdam optimizer implementation added.

LICENSE
MIT. See License File.

Install
params-flow is on the Python Package Index (PyPI):
pip install params-flow

Usage
params-flow provides a Layer and Model base classes that help
reducing common boilerplate code in your custom Keras layers and models.
When subclassing a Keras Model or Layer, each configuration parameter
has to be provided as an argument in __init__(). Keras relies on both __init__()
and get_config() to make a model/layer serializable.
While python idiomatic this style of defining your Keras models/layers results
in a lot of boilerplate code. params-flow provides an alternative by
encapsulating all those __init__() configuration arguments in a dedicated
Params instance (Params is kind of a “type-safe” python dict -
see kpe/py-params).
The model/layer specific configuration needs to be declared as
a nested Model.Params/Layer.Params subclass, and your model/layer have to
subclass params_flow.Model/params_flow.Layer instead of the Keras ones:
class BertEmbeddingsLayer(Layer):
class Params(PositionEmbeddingLayer.Params):
vocab_size = None
token_type_vocab_size = 2
hidden_size = 768
use_position_embeddings = True

class TransformerEncoderLayer(Layer):
class Params(TransformerSelfAttentionLayer.Params,
ProjectionLayer.Params):
intermediate_size = 3072
intermediate_activation = "gelu"
this allows you to declare the model’s configuration by simply extending
the Params of the underlying layers:
class BertModel(Model):
class Params(BertEmbeddingsLayer.Params,
TransformerEncoderLayer.Params):
pass
N.B. The two code excerpts above are taken from kpe/bert-for-tf2, so check there
for the details of a non-trivial params-flow based implementation (of BERT).

Resources

kpe/py-params - A “type-safe” dict class for python.
kpe/bert-for-tf2 - BERT implementation using the TensorFlow 2 Keras API with the help of params-flow for reducing some of the common Keras boilerplate code needed when passing parameters to custom layers.