TFGENZOO.flows.utils.actnorm_activation module

class TFGENZOO.flows.utils.actnorm_activation.ActnormActivation(scale: float = 1.0, logscale_factor=3.0, **kwargs)[source]

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

Actnorm Layer without inverse function

This layer cannot sync mean / variance via Multi GPU

Sources:

scale

scaling

Type

float

logscale_factor

logscale_factor

Type

float

Note

  • initialize
    mean = mean(first_batch)
    var = variance(first-batch)
    logs = log(scale / sqrt(var)) / log-scale-factor
    bias = -mean
  • forward formula (forward only)
    logs = logs * log_scale_factor
    scale = exp(logs)
    z = (x + bias) * scale
build(input_shape: tensorflow.python.framework.tensor_shape.TensorShape)[source]

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters

input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call(x: tensorflow.python.framework.ops.Tensor)[source]

This is where the layer’s logic lives.

Parameters

  • inputs – Input tensor, or list/tuple of input tensors.

  • **kwargs – Additional keyword arguments.

Returns

A tensor or list/tuple of tensors.

data_dep_initialize(x: tensorflow.python.framework.ops.Tensor)[source]
get_config()[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).

Returns

Python dictionary.

TFGENZOO.flows.utils.actnorm_activation.main()[source]