From 538a42c3bc7bcda76d08294deb258cbead47cbc4 Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Tue, 28 Apr 2020 13:16:03 +0200
Subject: [PATCH] refac but now docs yet
---
src/model_modules/flatten.py | 15 ++++++++++++++-
src/model_modules/inception_model.py | 25 ++++++++++++++-----------
2 files changed, 28 insertions(+), 12 deletions(-)
diff --git a/src/model_modules/flatten.py b/src/model_modules/flatten.py
index bbe92472..2c692041 100644
--- a/src/model_modules/flatten.py
+++ b/src/model_modules/flatten.py
@@ -9,7 +9,20 @@ import keras
def flatten_tail(input_X: keras.layers, name: str, bound_weight: bool = False, dropout_rate: float = 0.0,
window_lead_time: int = 4, activation: Callable = keras.activations.relu,
reduction_filter: int = 64, first_dense: int = 64):
-
+ """
+ Flatten output of
+
+ :param input_X:
+ :param name:
+ :param bound_weight:
+ :param dropout_rate:
+ :param window_lead_time:
+ :param activation:
+ :param reduction_filter:
+ :param first_dense:
+
+ :return:
+ """
X_in = keras.layers.Conv2D(reduction_filter, (1, 1), padding='same', name='{}_Conv_1x1'.format(name))(input_X)
X_in = activation(name='{}_conv_act'.format(name))(X_in)
diff --git a/src/model_modules/inception_model.py b/src/model_modules/inception_model.py
index 6467b324..dbf644cf 100644
--- a/src/model_modules/inception_model.py
+++ b/src/model_modules/inception_model.py
@@ -5,7 +5,8 @@ import logging
import keras
import keras.layers as layers
-from src.model_modules.advanced_paddings import PadUtils, ReflectionPadding2D, SymmetricPadding2D, Padding2D
+
+from src.model_modules.advanced_paddings import PadUtils, ReflectionPadding2D, Padding2D
class InceptionModelBase:
@@ -22,6 +23,7 @@ class InceptionModelBase:
def block_part_name(self):
"""
Use unicode due to some issues of keras with normal strings
+
:return:
"""
return chr(self.ord_base + self.part_of_block)
@@ -41,6 +43,7 @@ class InceptionModelBase:
"""
This function creates a "convolution tower block" containing a 1x1 convolution to reduce filter size followed by
convolution with given filter and kernel size
+
:param input_x: Input to network part
:param reduction_filter: Number of filters used in 1x1 convolution to reduce overall filter size before conv.
:param tower_filter: Number of filters for n x m convolution
@@ -79,8 +82,8 @@ class InceptionModelBase:
# name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
# )(tower)
tower = Padding2D(padding)(padding=padding_size,
- name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
- )(tower)
+ name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad'
+ )(tower)
tower = layers.Conv2D(tower_filter,
tower_kernel,
@@ -137,6 +140,7 @@ class InceptionModelBase:
def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs):
"""
This function creates a "MaxPooling tower block"
+
:param input_x: Input to network part
:param pool_kernel: size of pooling kernel
:param tower_filter: Number of filters used in 1x1 convolution to reduce filter size
@@ -160,11 +164,11 @@ class InceptionModelBase:
pooling = layers.AveragePooling2D
# tower = self.padding_layer(padding)(padding=padding_size, name=block_name+'Pad')(input_x)
- tower = Padding2D(padding)(padding=padding_size, name=block_name+'Pad')(input_x)
- tower = pooling(pool_kernel, strides=(1, 1), padding='valid', name=block_name+block_type)(tower)
+ tower = Padding2D(padding)(padding=padding_size, name=block_name + 'Pad')(input_x)
+ tower = pooling(pool_kernel, strides=(1, 1), padding='valid', name=block_name + block_type)(tower)
# convolution block
- tower = layers.Conv2D(tower_filter, (1, 1), padding='valid', name=block_name+"1x1")(tower)
+ tower = layers.Conv2D(tower_filter, (1, 1), padding='valid', name=block_name + "1x1")(tower)
tower = self.act(tower, activation, **act_settings)
return tower
@@ -172,6 +176,7 @@ class InceptionModelBase:
def inception_block(self, input_x, tower_conv_parts, tower_pool_parts, **kwargs):
"""
Crate a inception block
+
:param input_x: Input to block
:param tower_conv_parts: dict containing settings for parts of inception block; Example:
tower_conv_parts = {'tower_1': {'reduction_filter': 32,
@@ -211,7 +216,7 @@ class InceptionModelBase:
tower_build['avgpool'] = self.create_pool_tower(input_x, **tower_pool_parts, **kwargs, max_pooling=False)
block = keras.layers.concatenate(list(tower_build.values()), axis=3,
- name=block_name+"_Co")
+ name=block_name + "_Co")
return block
@@ -258,7 +263,7 @@ if __name__ == '__main__':
conv_settings_dict = {'tower_1': {'reduction_filter': 64,
'tower_filter': 64,
'tower_kernel': (3, 3),
- 'activation': LeakyReLU,},
+ 'activation': LeakyReLU, },
'tower_2': {'reduction_filter': 64,
'tower_filter': 64,
'tower_kernel': (5, 5),
@@ -295,12 +300,10 @@ if __name__ == '__main__':
# compile
epochs = 1
lrate = 0.01
- decay = lrate/epochs
+ decay = lrate / epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print(X_train.shape)
keras.utils.plot_model(model, to_file='model.pdf', show_shapes=True, show_layer_names=True)
# model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test))
print('test')
-
-
--
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