CNNArch

class ketos.neural_networks.cnn.CNNArch(*args, **kwargs)

Implement a Convolutional Neural Network

Note: in addition to the dense layers specified in the ‘dense_layers’ argument, an extra dense

layer will always be added to the end. The output of this layer is determined by the ‘n_classes’ parameter.

Args:

convolutional_layers: list

A list of dictionaries containing the detailed specification for the convolutional layers. Each layer is specified as a dictionary with the following format:

>>> {'n_filters':96, "filter_shape":(11,11), 'strides':4, 'padding':'valid', activation':'relu', 'max_pool': {'pool_size':(3,3) , 'strides':(2,2)}, 'batch_normalization':True} 

dense_layers: list

A list of dictionaries containing the detailed specification for the fully connected layers. Each layer is specified as a dictionary with the following format:

>>> {'n_hidden':4096, 'activation':'relu', 'batch_normalization':True, 'dropout':0.5} 

This list should not include the output layr, which will be automatically added based on the ‘n_classes’ parameter.

pre_trained_base: instance of CNNArch

A pre-trained CNN 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

n_classes:int

The number of classes the network will be used to classify. The output will be this number of values representing the scores for each class. Scores sum to 1.0.

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_conv_block()	Freeze the convolutional block
freeze_top()	Freeze the classification (dense) block
get_feature_extraction_base()	Retrive the feature extraction base (initial convolutional layer + residual blocks)
unfreeze_conv_block()	Unfreeze the convolutional block
unfreeze_top()	Unfreeze the classification (dense) 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)

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 CNNArch

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

freeze_conv_block()

Freeze the convolutional block

freeze_top()

Freeze the classification (dense) block

get_feature_extraction_base()

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

Returns:

list containing the feature extraction layers

unfreeze_conv_block()

Unfreeze the convolutional block

unfreeze_top()

Unfreeze the classification (dense) block