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Ability to pass list of activations in Functional and added rowdy_net parameter to control the behaviour of list of activations. #56

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Ability to pass list of activations in Functional and added rowdy_net parameter to control the behaviour of list of activations. #56

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27 changes: 22 additions & 5 deletions sciann/functionals/functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@
e.g. [10, 100, 20] is a for hidden layers with 10, 100, 20, respectively.
activation: defaulted to "tanh".
Activation function for the hidden layers.
Also can be a list of activations to supply different activations for each hidden layer.
If list then length should be equal to hidden_layers.
Last layer will have a linear output.
output_activation: defaulted to "linear".
Activation function to be applied to the network output.
Expand All @@ -57,6 +59,9 @@
trainable: Boolean.
False if network is not trainable, True otherwise.
Default value is True.
rowdy_net: Boolean.
Only applicable when list of activations is passed in the activation parameter.
Default value is False.

# Raises
ValueError:
Expand All @@ -74,6 +79,7 @@ def Functional(
kernel_regularizer=None,
bias_regularizer=None,
trainable=True,
rowdy_net=False,
**kwargs):
# prepare hidden layers.
if hidden_layers is None:
Expand All @@ -83,31 +89,42 @@ def Functional(
if not all([isinstance(n, int) for n in hidden_layers]):
raise TypeError("Enter a list of integers as the third input assigning layer widths, e.g. [10,10,10]. ")
# prepare kernel initializers.
if isinstance(activation,str):
acts = len(hidden_layers) * [activation] + [output_activation]
elif isinstance(activation,list):
if rowdy_net:
acts = len(hidden_layers) * [activation] + [output_activation]
else:
if not (len(activation) == len(hidden_layers)):
raise TypeError("If activation is list then list length should be equal to hidden layer list, or if you want the network to rowdy net -> pass rowdy_net=True")
else:
acts = activation + [output_activation]

activations, def_biasinit, def_kerinit = \
prepare_default_activations_and_initializers(
len(hidden_layers) * [activation] + [output_activation]
acts
)
if kernel_initializer is None:
kernel_initializer = def_kerinit
elif isinstance(kernel_initializer, (float, int)):
kernel_initializer = default_weight_initializer(
len(hidden_layers) * [activation] + [output_activation],
acts,
'constant',
scale=kernel_initializer
)
else:
kernel_initializer = [kernel_initializer for l in len(hidden_layers) * [activation] + [output_activation]]
kernel_initializer = [kernel_initializer for l in acts]
# prepare bias initializers.
if bias_initializer is None:
bias_initializer = def_biasinit
elif isinstance(bias_initializer, (float, int)):
bias_initializer = default_weight_initializer(
len(hidden_layers) * [activation] + [output_activation],
acts,
'constant',
scale=bias_initializer
)
else:
bias_initializer = [bias_initializer for l in len(hidden_layers) * [activation] + [output_activation]]
bias_initializer = [bias_initializer for l in acts]
# prepare regularizers.
kernel_regularizer = default_regularizer(kernel_regularizer)
bias_regularizer = default_regularizer(bias_regularizer)
Expand Down