CNN1DArch

class ketos.neural_networks.cnn.CNN1DArch(*args, **kwargs)[source]

Implement an 1D (temporal) 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.

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.

pre_trained_base: instance of CNN1DArch

A pre-trained CNN 1D 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_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)[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.

clone_with_new_top(n_classes=None, freeze_base=True)[source]

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 CNN1DArch

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

freeze_conv_block()[source]

Freeze the convolutional block

freeze_top()[source]

Freeze the classification (dense) block

get_feature_extraction_base()[source]

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

Returns:

list containing the feature extraction layers

unfreeze_conv_block()[source]

Unfreeze the convolutional block

unfreeze_top()[source]

Unfreeze the classification (dense) block