ResNetBlock¶

Inheritance Diagram

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)[source]¶

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)[source]¶

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)[source]¶

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.