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)[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.
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__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.
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