ResNetBlock

Inheritance Diagram

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class ashpy.models.convolutional.pix2pixhd.ResNetBlock(filters, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.ReLU'>, kernel_size=3, num_blocks=2)

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

ResNet Blocks.

The input filters is the same as the output filters.

Methods

__init__(filters[, normalization_layer, …])	Build the ResNet block composed by num_blocks.
call(inputs[, training])	Forward pass.

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__(filters, normalization_layer=<class 'ashpy.layers.instance_normalization.InstanceNormalization'>, non_linearity=<class 'tensorflow.python.keras.layers.advanced_activations.ReLU'>, kernel_size=3, num_blocks=2)

Build the ResNet block composed by num_blocks.

Each block is composed by

• Conv2D with strides 1 and padding “same”
• Normalization Layer
• Non Linearity

The final result is the output of the ResNet + input.

Parameters:

• filters (int) – initial filters (same as the output filters).
• normalization_layer (tf.keras.layers.Layer) – layer of normalization used by the residual block.
• non_linearity (tf.keras.layers.Layer) – non linearity used in the resnet block.
• kernel_size (int) – kernel size used in the resnet block.
• num_blocks (int) – number of blocks, each block is composed by conv, normalization and non linearity.

call(inputs, training=False)

Forward pass.

Parameters:

• inputs – input tensor.
• training – whether is training or not.

Returns:

A Tensor of the same shape as the inputs. The input passed through num_blocks blocks.