From c937d9be09a7c0ff7266005074b0a63f28c80d57 Mon Sep 17 00:00:00 2001
From: Felix Kleinert <f.kleinert@fz-juelich.de>
Date: Tue, 28 Apr 2020 13:00:57 +0200
Subject: [PATCH] update docstrings
---
src/model_modules/advanced_paddings.py | 22 ++++++++++++++++++----
src/model_modules/flatten.py | 11 +++++++++++
2 files changed, 29 insertions(+), 4 deletions(-)
diff --git a/src/model_modules/advanced_paddings.py b/src/model_modules/advanced_paddings.py
index d9e55c78..ea16e5b8 100644
--- a/src/model_modules/advanced_paddings.py
+++ b/src/model_modules/advanced_paddings.py
@@ -254,10 +254,24 @@ class SymmetricPadding2D(_ZeroPadding):
class Padding2D:
- '''
- This class combines the implemented padding methods. You can call this method by defining a specific padding type.
- The __call__ method will return the corresponding Padding layer.
- '''
+ """
+ Combine all implemented padding methods.
+
+ You can call this method by defining a specific padding type. The __call__ method will return the corresponding
+ Padding layer.
+
+ .. code-block:: python
+
+ input_x = ... # your input data
+ kernel_size = (5, 1)
+ padding_size = PadUtils.get_padding_for_same(kernel_size)
+
+ tower = layers.Conv2D(...)(input_x)
+ tower = layers.Activation(...)(tower)
+ tower = Padding2D('ZeroPad2D')(padding=padding_size, name=f'Custom_Pad')(tower)
+
+ Padding type can either be set by a string or directly by using an instance of a valid padding class.
+ """
allowed_paddings = {
**dict.fromkeys(("RefPad2D", "ReflectionPadding2D"), ReflectionPadding2D),
diff --git a/src/model_modules/flatten.py b/src/model_modules/flatten.py
index efb0e977..e2dde448 100644
--- a/src/model_modules/flatten.py
+++ b/src/model_modules/flatten.py
@@ -47,6 +47,17 @@ def flatten_tail(input_x: keras.layers, inner_neurons: int, activation: Union[Ca
:param kernel_regularizer:
:return:
+
+ .. code-block:: python
+
+ input_x = ... # your input data
+ conv_out = Conv2D(*args)(input_x) # your convolutional stack
+ out = flatten_tail(conv_out, inner_neurons=64, activation=keras.layers.advanced_activations.ELU,
+ output_neurons=4
+ output_activation='linear', reduction_filter=64,
+ name='Main', bound_weight=False, dropout_rate=.3,
+ kernel_regularizer=keras.regularizers.l2()
+ )
"""
# compression layer
if reduction_filter is None:
--
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