Decoder¶

Inheritance Diagram

class ashpy.models.convolutional.decoders.Decoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, channels, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>)[source]¶

Bases: ashpy.models.convolutional.interfaces.Conv2DInterface

Primitive Model for all decoder (i.e., transpose convolution) based architecture.

Notes

Default to DCGAN Generator architecture.

Examples

Direct Usage:

dummy_generator = Decoder(
    layer_spec_input_res=(8, 8),
    layer_spec_target_res=(64, 64),
    kernel_size=(5, 5),
    initial_filters=1024,
    filters_cap=16,
    channels=3,
)

Subclassing

class DummyGenerator(Decoder):
    def call(self, input, training=True):
        print("Dummy Generator!")
        return input

dummy_generator = DummyGenerator(
    layer_spec_input_res=(8, 8),
    layer_spec_target_res=(32, 32),
    kernel_size=(5, 5),
    initial_filters=1024,
    filters_cap=16,
    channels=3,
)
dummy_generator(tf.random.normal((1, 100)))

Dummy Generator!

Methods

__init__(layer_spec_input_res, …[, …]) Instantiate the Decoder.

Attributes

`activity_regularizer`	Optional regularizer function for the output of this layer.
`dtype`
`dynamic`
`inbound_nodes`	Deprecated, do NOT use! Only for compatibility with external Keras.
`input`	Retrieves the input tensor(s) of a layer.
`input_mask`	Retrieves the input mask tensor(s) of a layer.
`input_shape`	Retrieves the input shape(s) of a layer.
`input_spec`	Gets the network’s input specs.
`layers`
`losses`	Losses which are associated with this Layer.
`metrics`	Returns the model’s metrics added using compile, add_metric APIs.
`metrics_names`	Returns the model’s display labels for all outputs.
`name`	Returns the name of this module as passed or determined in the ctor.
`name_scope`	Returns a tf.name_scope instance for this class.
`non_trainable_variables`
`non_trainable_weights`
`outbound_nodes`	Deprecated, do NOT use! Only for compatibility with external Keras.
`output`	Retrieves the output tensor(s) of a layer.
`output_mask`	Retrieves the output mask tensor(s) of a layer.
`output_shape`	Retrieves the output shape(s) of a layer.
`run_eagerly`	Settable attribute indicating whether the model should run eagerly.
`sample_weights`
`state_updates`	Returns the updates from all layers that are stateful.
`stateful`
`submodules`	Sequence of all sub-modules.
`trainable`
`trainable_variables`	Sequence of variables owned by this module and it’s submodules.
`trainable_weights`
`updates`
`variables`	Returns the list of all layer variables/weights.
`weights`	Returns the list of all layer variables/weights.

__init__(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, channels, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>)[source]¶

Instantiate the Decoder.

Model Assembly:

1. _add_initial_block(): Ingest the tf.keras.Model inputs and prepare them for _add_building_block().

2. _add_building_block(): Core of the model, the layers specified here get added to the tf.keras.Model multiple times consuming the hyperparameters generated in the _get_layer_spec().

3. _add_final_block(): Final block of our tf.keras.Model, take the model after _add_building_block() and prepare them for the for the final output.

Parameters:	layer_spec_input_res (`tuple` of (`int`, `int`)) – Shape of the `_get_layer_spec()` input tensors. layer_spec_target_res – (`tuple` of (`int`, `int`)): Shape of tensor desired as output of `_get_layer_spec()`. kernel_size (`tuple` of (`int`, `int`)) – Kernel used by the convolution layers. initial_filters (int) – Numbers of filters at the end of the first block. filters_cap (int) – Cap filters to a set amount, in the case of Decoder is a floor value AKA the minimum amount of filters. channels (int) – Channels of the output images (1 for Grayscale, 3 for RGB).
Returns:	`None`
Raises:	`ValueError` – If filters_cap > initial_filters.

_add_building_block(filters)[source]¶

Construct the core of the tf.keras.Model.

The layers specified here get added to the tf.keras.Model multiple times consuming the hyperparameters generated in the _get_layer_spec().

Parameters:	filters (int) – Number of filters to use for this iteration of the Building Block.

_add_final_block(channels)[source]¶

Prepare results of _add_building_block() for the for the final output.

Parameters:	channels (int) – Channels of the output images (1 for Grayscale, 3 for RGB).

_add_initial_block(initial_filters, input_res)[source]¶

Ingest the tf.keras.Model inputs and prepare them for _add_building_block().

Parameters:	initial_filters (int) – Numbers of filters to used as a base value. input_res (`tuple` of (`int`, `int`)) – Shape of the `_get_layer_spec()` input tensors.