FCNNAutoencoder

Inheritance Diagram

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class ashpy.models.convolutional.autoencoders.FCNNAutoencoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension, channels)

Bases: tensorflow.python.keras.engine.training.Model

Primitive Model for all fully convolutional autoencoders.

Examples

• Direct Usage:

  autoencoder = FCNNAutoencoder(
      layer_spec_input_res=(64, 64),
      layer_spec_target_res=(8, 8),
      kernel_size=5,
      initial_filters=32,
      filters_cap=128,
      encoding_dimension=100,
      channels=3,
  )
  
  encoding, reconstruction = autoencoder(tf.zeros((1, 64, 64, 3)))
  print(encoding.shape)
  print(reconstruction.shape)
  

Methods

__init__(layer_spec_input_res, …)	Instantiate the FCNNBaseAutoEncoder.
call(inputs[, training])	Execute the model on input data.

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, channels)

Instantiate the FCNNBaseAutoEncoder.

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.
• channels (int) – Number of channels for the reconstructed image.

Returns:

None

call(inputs, training=True)

Execute the model on input data.

Parameters:

• inputs (tf.Tensor) – Input tensors.
• training (bool) – Training flag.

Returns:

(encoding, reconstruction) – Pair of tensors.