BaseEncoder¶

Inheritance Diagram

class ashpy.models.convolutional.encoders.BaseEncoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, output_shape, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>)[source]¶

Bases: ashpy.models.convolutional.interfaces.Conv2DInterface

Primitive Model for all encoder (i.e., convolution) based architecture.

Notes

Default to DCGAN Discriminator architecture.

Examples

Direct Usage:

dummy_generator = BaseEncoder(
    layer_spec_input_res=(64, 64),
    layer_spec_target_res=(8, 8),
    kernel_size=5,
    initial_filters=4,
    filters_cap=128,
    output_shape=1,
)

Subclassing

class DummyDiscriminator(BaseEncoder):
    def call(self, inputs, training=True):
        print("Dummy Discriminator!")
        # build the model using
        # self._layers and inputs
        return inputs

dummy_discriminator = DummyDiscriminator(
    layer_spec_input_res=(64, 64),
    layer_spec_target_res=(8, 8),
    kernel_size=5,
    initial_filters=16,
    filters_cap=128,
    output_shape=1,
)
dummy_discriminator(tf.zeros((1,28,28,3)))

Dummy Discriminator!

Methods

__init__(layer_spec_input_res, …[, …])

Instantiate the BaseDecoder.

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, output_shape, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>)[source]¶

Instantiate the BaseDecoder.

Parameters

layer_spec_input_res (tuple of (int, int)) – Shape of the input tensors.
layer_spec_target_res (Union[int, Tuple[int, int]]) – (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.
output_shape (int) – Amount of units of the last tf.keras.layers.Dense.

Returns

None

Raises

ValueError – If filters_cap < initial_filters

_add_building_block(filters)[source]¶

Construct the core of the tf.keras.Model.

The layers specified here get added to the tf.keras.Model multiple times consuming the hyper-parameters generated in the _get_layer_spec().

Parameters: filters (int) – Number of filters to use for this iteration of the Building Block.

_add_final_block(output_shape)[source]¶

Take the results of _add_building_block() and prepare them for the for the final output.

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