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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())

• 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.

MIT. See License File.

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

pip install params-flow

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).

• 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.