PatchDiscriminator¶
Inheritance Diagram
-
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)[source]¶ Bases:
ashpy.models.convolutional.encoders.Encoder
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, …)Patch Discriminator used by pix2pix.
call
(inputs[, training, return_features])Forward pass of the PatchDiscriminator.
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)[source]¶ Patch Discriminator 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)[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]¶ Prepare the results of
_add_building_block()
for the final output.- Parameters
output_shape (int) – Amount of units of the last
tf.keras.layers.Dense
-