From 39a6459c051d748bceffc1ad5ab1ca63c8b75b74 Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Wed, 29 Apr 2020 07:55:31 +0200
Subject: [PATCH] adjusted docstrings
---
src/model_modules/flatten.py | 18 +++++++++++-------
1 file changed, 11 insertions(+), 7 deletions(-)
diff --git a/src/model_modules/flatten.py b/src/model_modules/flatten.py
index 218b12ed..dd1e8e21 100644
--- a/src/model_modules/flatten.py
+++ b/src/model_modules/flatten.py
@@ -10,19 +10,24 @@ def get_activation(input_to_activate: keras.layers, activation: Union[Callable,
"""
Apply activation on a given input layer.
- This helper function is able to handle advanced keras activations as well as strings for standard activations
+ This helper function is able to handle advanced keras activations as well as strings for standard activations.
:param input_to_activate: keras layer to apply activation on
:param activation: activation to apply on `input_to_activate'. Can be a standard keras strings or activation layers
- :param kwargs:
- :return:
+ :param kwargs: keyword arguments used inside activation layer
+
+ :return: activation
.. code-block:: python
input_x = ... # your input data
x_in = keras.layer(<without activation>)(input_x)
+
+ # get activation via string
x_act_string = get_activation(x_in, 'relu')
+ # or get activation via layer callable
x_act_layer = get_activation(x_in, keras.layers.advanced_activations.ELU)
+
"""
if isinstance(activation, str):
name = kwargs.pop('name', None)
@@ -42,7 +47,7 @@ def flatten_tail(input_x: keras.layers, inner_neurons: int, activation: Union[Ca
kernel_regularizer: keras.regularizers = None
):
"""
- Flatten output of convolutional layers
+ Flatten output of convolutional layers.
:param input_x: Multidimensional keras layer (ConvLayer)
:param output_neurons: Number of neurons in the last layer (must fit the shape of labels)
@@ -55,12 +60,12 @@ def flatten_tail(input_x: keras.layers, inner_neurons: int, activation: Union[Ca
:param inner_neurons: Number of neurons in inner dense layer
:param kernel_regularizer: regularizer to apply on conv and dense layers
- :return:
+ :return: flatten branch with size n=output_neurons
.. code-block:: python
input_x = ... # your input data
- conv_out = Conv2D(*args)(input_x) # your convolutional stack
+ conv_out = Conv2D(*args)(input_x) # your convolution stack
out = flatten_tail(conv_out, inner_neurons=64, activation=keras.layers.advanced_activations.ELU,
output_neurons=4
output_activation='linear', reduction_filter=64,
@@ -69,7 +74,6 @@ def flatten_tail(input_x: keras.layers, inner_neurons: int, activation: Union[Ca
)
model = keras.Model(inputs=input_x, outputs=[out])
-
"""
# compression layer
if reduction_filter is None:
--
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