`ramjet.models.components.gaussian_dropout`¶

Module Contents¶

Classes¶

GaussianDropout Apply multiplicative 1-centered Gaussian noise.

class GaussianDropout(rate, noise_shape: Optional[Tuple[int, ...]] = None, **kwargs)[source]¶

Bases: tensorflow.python.keras.engine.base_layer.Layer

Apply multiplicative 1-centered Gaussian noise.

As it is a regularization layer, it is only active at training time.

Parameters:	rate – Float, drop probability (as with Dropout). The multiplicative noise will have standard deviation sqrt(rate / (1 - rate)).

Call arguments:: inputs: Input tensor (of any rank). training: Python boolean indicating whether the layer should behave in

training mode (adding dropout) or in inference mode (doing nothing).
Input shape:: Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.
Output shape:: Same shape as input.

__init__(self, rate, noise_shape: Optional[Tuple[int, ...]] = None, **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

call(self, inputs, training=None)[source]¶

This is where the layer’s logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Parameters:

Parameters:	inputs – Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument. NumPy array or Python scalar values in inputs get cast as tensors. Keras mask metadata is only collected from inputs. Layers are built (build(input_shape) method) using shape info from inputs only. input_spec compatibility is only checked against inputs. Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in args or kwargs, their casting behavior in mixed precision should be handled manually. The SavedModel input specification is generated using inputs only. Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments. args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above. **kwargs – Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference. mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).
Returns:	A tensor or list/tuple of tensors.

inputs –
Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.
- NumPy array or Python scalar values in inputs get cast as tensors.
- Keras mask metadata is only collected from inputs.
- Layers are built (build(input_shape) method) using shape info from inputs only.
- input_spec compatibility is only checked against inputs.
- Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
- The SavedModel input specification is generated using inputs only.
- Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.
*args – Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
**kwargs –
Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.
- mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

get_config(self)[source]¶

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Returns:	Python dictionary.

compute_output_shape(self, input_shape)[source]¶

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters:	input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns:	An input shape tuple.

ramjet.models.components.gaussian_dropout¶

Module Contents¶

Classes¶

`ramjet.models.components.gaussian_dropout`¶