DenseNetArch

class ketos.neural_networks.densenet.DenseNetArch(*args, **kwargs)

Implements a DenseNet architecture, building on top of Dense and tansition blocks

Args:

block_sets: list of ints

A list specifying the block sets and how many blocks each set contains. Example: [6, 12, 24, 16] will create a DenseNet with 4 block sets containing 6, 12, 24 and 16 dense blocks, with a total of 58 blocks.

growth_rate:int

The factor by which the number of filters (i.e.: channels) within each dense block grows.

compression_factor: float

The factor by which transition blocks reduce the number of filters (i.e.: channels) between dense blocks (between 0 and 1).

dropout_rate: float

The droput rate (between 0 and 1) used in each transition block. Use 0 for no dropout.

n_classes:int

The number of classes. The output layer uses a Softmax activation and will contain this number of nodes, resulting in model outputs with this many values summing to 1.0.

pre_trained_base: instance of DenseNetArch

A pre-trained densenet model from which the residual blocks will be taken. Use by the the clone_with_new_top method when creating a clone for transfer learning

Methods

call(inputs[, training])	Calls the model on new inputs.
clone_with_new_top([n_classes, freeze_base])	Clone this instance but replace the original classification top with a new (untrained) one
freeze_block(block_ids)	Freeze specific dense blocks
freeze_init_layer()	Freeze the initial convolutional layer
freeze_top()	Freeze the classification block
get_feature_extraction_base()	Retrive the feature extraction base (initial convolutional layer + dense blocks)
unfreeze_block(block_ids)	Unfreeze specific dense blocks
unfreeze_init_layer()	Unfreeze the initial convolutional layer
unfreeze_top()	Unfreeze the classification 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=False)

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.

clone_with_new_top(n_classes=None, freeze_base=True)

Clone this instance but replace the original classification top with a new (untrained) one

Args:

n_classes:int

The number of classes the new classification top should output. If None(default), the original number of classes will be used.

freeze_base:bool

If True, the weights of the feature extraction base will be froze (untrainable) in the new model.

Returns:

cloned_model: instance of DenseNetArch

The new model with the old feature extraction base and new classification top.

freeze_block(block_ids)

Freeze specific dense blocks

Args:

blocks_ids: list of ints

The block numbers to be freezed (starting from zero)

freeze_init_layer()

Freeze the initial convolutional layer

freeze_top()

Freeze the classification block

get_feature_extraction_base()

Retrive the feature extraction base (initial convolutional layer + dense blocks)

Returns:

list containing the feature extraction layers

unfreeze_block(block_ids)

Unfreeze specific dense blocks

Args:

blocks_ids: list of ints

The block numbers to be freezed (starting from zero)

unfreeze_init_layer()

Unfreeze the initial convolutional layer

unfreeze_top()

Unfreeze the classification block