Autoencoder¶
Inheritance Diagram
-
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: - layer_spec_input_res (