FCNNEncoder

Inheritance Diagram

Inheritance diagram of ashpy.models.convolutional.encoders.FCNNEncoder

class ashpy.models.convolutional.encoders.FCNNEncoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension)[source]

Bases: ashpy.models.convolutional.encoders.Encoder

Fully Convolutional Encoder.

Output a 1x1xencoding_size vector. The output neurons are linear.

Examples

  • Direct Usage:

    dummy_generator = FCNNEncoder(
        layer_spec_input_res=(64, 64),
        layer_spec_target_res=(8, 8),
        kernel_size=5,
        initial_filters=4,
        filters_cap=128,
        encoding_dimension=100,
    )
    print(dummy_generator(tf.zeros((1, 64, 64, 3))).shape)
    
    (1, 1, 1, 100)
    

Methods

__init__(layer_spec_input_res, …)

Instantiate the FCNNDecoder.

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, encoding_dimension)[source]

Instantiate the FCNNDecoder.

Parameters
  • layer_spec_input_res (tuple of (int, int)) – Shape of the input tensors.

  • layer_spec_target_res – (tuple of (int, int)): Shape of tensor desired as output of _get_layer_spec().

  • kernel_size (int) – Kernel used by the convolution layers.

  • initial_filters (int) – Numbers of filters to used as a base value.

  • filters_cap (int) – Cap filters to a set amount, in the case of an Encoder is a ceil value AKA the max amount of filters.

  • encoding_dimension (int) – encoding dimension.

Returns

None

Raises

ValueError – If filters_cap < initial_filters

_add_final_block(output_shape)[source]

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

Parameters

output_shape (int) – Amount of units of the last tf.keras.layers.Dense