phygnn.layers.handlers.HiddenLayers

class HiddenLayers(hidden_layers)

Bases: object

Class to handle TensorFlow hidden layers

Parameters:

hidden_layers (list) – List of dictionaries of key word arguments for each hidden layer in the NN. Dense linear layers can be input with their activations or separately for more explicit control over the layer ordering. For example, this is a valid input for hidden_layers that will yield 7 hidden layers:

[{‘units’: 64, ‘activation’: ‘relu’, ‘dropout’: 0.01},

{‘units’: 64}, {‘batch_normalization’: {‘axis’: -1}}, {‘activation’: ‘relu’}, {‘dropout’: 0.01}]

Methods

add_layer(layer_kwargs)	Add a hidden layer to the DNN.
add_layer_by_class(class_name, **kwargs)	Add a new layer by the class name, either from phygnn.layers.custom_layers or tf.keras.layers
add_skip_layer(name)	Add a skip layer, looking for a prior skip connection start point if already in the layer list.
compile(model, hidden_layers)	Add hidden layers to model
parse_repeats(hidden_layers)	Parse repeat layers.

Attributes

bias_weights	Get a list of the NN bias weights (tensors)
hidden_layer_kwargs	List of dictionaries of key word arguments for each hidden layer in the NN.
kernel_weights	Get a list of the NN kernel weights (tensors)
layers	TensorFlow keras layers
skip_layers	Get a dictionary of unique SkipConnection objects in the layers list keyed by SkipConnection name.
weights	Get a list of layer weights for gradient calculations.

static parse_repeats(hidden_layers)

Parse repeat layers. Must have “repeat” and “n” to repeat one or more layers.

Parameters:

hidden_layers (list) – Hidden layer kwargs including possibly entries with {‘n’: 2, ‘repeat’: [{…}, {…}]} that will duplicate the list sub entry n times.

Returns:

hidden_layers (list) – Hidden layer kwargs exploded for ‘repeat’ entries.

property layers

TensorFlow keras layers

Returns:

list

property skip_layers

Get a dictionary of unique SkipConnection objects in the layers list keyed by SkipConnection name.

Returns:

list

property hidden_layer_kwargs

List of dictionaries of key word arguments for each hidden layer in the NN. This is a copy of the hidden_layers input arg that can be used to reconstruct the network.

Returns:

list

property weights

Get a list of layer weights for gradient calculations.

Returns:

list

property kernel_weights

Get a list of the NN kernel weights (tensors)

(can be used for kernel regularization).

Does not include input layer or dropout layers. Does include the output layer.

Returns:

list

property bias_weights

Get a list of the NN bias weights (tensors)

(can be used for bias regularization).

Does not include input layer or dropout layers. Does include the output layer.

Returns:

list

add_skip_layer(name)

Add a skip layer, looking for a prior skip connection start point if already in the layer list.

Parameters:

name (str) – Unique string identifier of the skip connection. The skip endpoint should have the same name.

add_layer_by_class(class_name, **kwargs)

Add a new layer by the class name, either from phygnn.layers.custom_layers or tf.keras.layers

Parameters:

• class_name (str) – Class name from phygnn.layers.custom_layers or tf.keras.layers

• kwargs (dict) – Key word arguments to initialize the class.

add_layer(layer_kwargs)

Add a hidden layer to the DNN.

Parameters:

layer_kwargs (dict) – Dictionary of key word arguments for list layer. For example, any of the following are valid inputs:

{‘units’: 64, ‘activation’: ‘relu’, ‘dropout’: 0.05} {‘units’: 64, ‘name’: ‘relu1’} {‘activation’: ‘relu’} {‘batch_normalization’: {‘axis’: -1}} {‘dropout’: 0.1}

classmethod compile(model, hidden_layers)

Add hidden layers to model

Parameters:

• model (tensorflow.keras) – Model to add hidden layers too

• hidden_layers (list) – List of dictionaries of key word arguments for each hidden layer in the NN. Dense linear layers can be input with their activations or separately for more explicit control over the layer ordering. For example, this is a valid input for hidden_layers that will yield 7 hidden layers:

  [{‘units’: 64, ‘activation’: ‘relu’, ‘dropout’: 0.01},

  {‘units’: 64}, {‘batch_normalization’: {‘axis’: -1}}, {‘activation’: ‘relu’}, {‘dropout’: 0.01}]

Returns:

model (tensorflow.keras) – Model with layers added