Autoencoder

Inheritance Diagram

Inheritance diagram of ashpy.models.convolutional.autoencoders.Autoencoder

class ashpy.models.convolutional.autoencoders.Autoencoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension, channels)[source]

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

Primitive Model for all convolutional autoencoders.

Examples

  • Direct Usage:

    autoencoder = Autoencoder(
        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 BaseAutoEncoder.
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)[source]

Instantiate the BaseAutoEncoder.

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)[source]

Execute the model on input data.

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

(encoding, reconstruction) – Pair of tensors.