FCNNEncoder

Inheritance Diagram

---

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

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)

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)

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