PatchDiscriminator

Inheritance Diagram

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class ashpy.models.convolutional.discriminators.PatchDiscriminator(input_res, min_res, kernel_size, initial_filters, filters_cap, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, use_attention=False)

Bases: ashpy.models.convolutional.encoders.BaseEncoder

Pix2Pix discriminator: The last layer is an image in which each pixels is the probability of being fake or real.

Examples

x = tf.ones((1, 64, 64, 3))

# instantiate the PathDiscriminator
patchDiscriminator = PatchDiscriminator(input_res=64,
                                        min_res=16,
                                        kernel_size=5,
                                        initial_filters=64,
                                        filters_cap=512,
                                        )

# evaluate passing x
output = patchDiscriminator(x)

# the output shape is the same as the input shape
print(output.shape)

(1, 12, 12, 1)

Methods

__init__(input_res, min_res, kernel_size, …)	PatchDiscriminator used by pix2pix, when min_res=1 this is the same as a standard fully convolutional discriminator
call(inputs[, training, return_features])	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__(input_res, min_res, kernel_size, initial_filters, filters_cap, use_dropout=True, dropout_prob=0.3, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.LeakyReLU'>, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, use_attention=False)

PatchDiscriminator used by pix2pix, when min_res=1 this is the same as a standard fully convolutional discriminator

Parameters

• input_res (int) – Input Resolution

• min_res (int) – Minimum Resolution reached by the discriminator

• kernel_size (int) – Kernel Size used in Conv Layer

• initial_filters (int) – number of filters in the first convolutional layer

• filters_cap (int) – Maximum number of filters

• use_dropout (bool) – whether to use dropout

• dropout_prob (float) – probability of dropout

• non_linearity (tf.keras.layers.Layer) – non linearity used in the model

• normalization_layer (tf.keras.layers.Layer) – normalization layer used in the model

• use_attention (bool) – whether to use attention

_add_building_block(filters, use_bn=False)

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)

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

call(inputs, training=False, return_features=False)

Execute the model on input data.

Parameters

• inputs (tf.Tensor) – Input tensor(s).

• training (bool) – Training flag.

• return_features (bool) – If True returns the features.

Returns

tf.Tensor – The model output.