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Initializer base class: all Keras initializers inherit from this class.
Initializers should implement a __call__() method with the following signature:
def __call__(self, shape, dtype=None, **kwargs): # returns a tensor of shape `shape` and dtype `dtype` # containing values drawn from a distribution of your choice. Optionally, you an also implement the method get_config() and the class method from_config in order to support serialization -- just like with any Keras object.
Here's a simple example: a random normal initializer.
class ExampleRandomNormal(Initializer): def __init__(self, mean, stddev): self.mean = mean self.stddev = stddev def __call__(self, shape, dtype=None, **kwargs): return keras.random.normal( shape, mean=self.mean, stddev=self.stddev, dtype=dtype ) def get_config(self): # To support serialization return {"mean": self.mean, "stddev": self.stddev} Note that we don't have to implement from_config() in the example above since the constructor arguments of the class the keys in the config returned by get_config() are the same. In this case, the default from_config() works fine.
Methods
clone
clone() from_config
@classmethodfrom_config( config )
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1) config = initializer.get_config() initializer = RandomUniform.from_config(config) | Args | |
|---|---|
config | A Python dictionary, the output of get_config(). |
| Returns | |
|---|---|
An Initializer instance. |
get_config
get_config() Returns the initializer's configuration as a JSON-serializable dict.
| Returns | |
|---|---|
| A JSON-serializable Python dict. |
__call__
__call__( shape, dtype=None ) Returns a tensor object initialized as specified by the initializer.
| Args | |
|---|---|
shape | Shape of the tensor. |
dtype | Optional dtype of the tensor. |