ResNetBlock

class ketos.neural_networks.resnet.ResNetBlock(*args, **kwargs)[source]

Residual block for ResNet architectures.

Args:
filters: int

The number of filters in the block

strides: int

Strides used in convolutional layers within the block

kernel: (int,int)

Kernel used in convolutional layers within the block

residual_path: bool

Whether or not the block will contain a residual path

batch_norm_momentum: float between 0 and 1

Momentum for the moving average of the batch normalization layers. The default value is 0.99. For an explanation of how the momentum affects the batch normalisation operation, see <https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization>

dropout_rate: float between 0 and 1

Fraction of the input units to drop in the dropout layers. Set this parameter to 0 to disable dropout (default).

Returns:

A ResNetBlock object. The block itself is a tensorflow model and can be used as such.

Methods

call(inputs[, training])

Calls the model on new inputs.

set_batch_norm_momentum(momentum)

Set the momentum for the moving average of the batch normalization layers in the block.

set_dropout_rate(rate)

Set the fraction of the input units to drop in the dropout layers in the block.

Attributes

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer's computations.

distribute_strategy

The tf.distribute.Strategy this model was created under.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

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

InputSpec instance(s) describing the input format for this layer.

layers

losses

List of losses added using the add_loss() API.

metrics

Returns the model's metrics added using compile(), add_metric() APIs.

metrics_names

Returns the model's display labels for all outputs.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

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.

state_updates

Deprecated, do NOT use!

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

call(inputs, training=None)[source]

Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args:
inputs: Tensor or list of tensors

A tensor or list of tensors

training: Bool

Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.

Returns:

A tensor if there is a single output, or a list of tensors if there are more than one outputs.

set_batch_norm_momentum(momentum)[source]

Set the momentum for the moving average of the batch normalization layers in the block.

For an explanation of how the momentum affects the batch normalisation operation, see <https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization>

Args:
momentum: float between 0 and 1

Momentum for the moving average of the batch normalization layers.

Returns:

None

set_dropout_rate(rate)[source]

Set the fraction of the input units to drop in the dropout layers in the block.

Args:
rate: float between 0 and 1

Fraction of the input units to drop in the dropout layers.

Returns:

None