FCNNBaseDecoder¶

Inheritance Diagram

class ashpy.models.convolutional.decoders.FCNNBaseDecoder(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.decoders.BaseDecoder

Fully Convolutional Decoder. Expected input is a feature map.

Examples

Direct Usage:

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

print(dummy_generator(tf.zeros((1, 1, 1, 100))).shape)

(1, 64, 64, 3)

Methods

__init__(layer_spec_input_res, …[, …])

Instantiate the BaseDecoder.

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.

_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.