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machine-learning
MLAir
Commits
57511d09
Commit
57511d09
authored
3 years ago
by
leufen1
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also be able to set pooling type
parent
fbffafa4
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5 merge requests
!430
update recent developments
,
!413
update release branch
,
!412
Resolve "release v2.0.0"
,
!406
Lukas issue368 feat prepare cnn class for filter benchmarking
,
!403
Resolve "prepare CNN class for filter benchmarking"
Pipeline
#93689
passed
3 years ago
Stage: test
Stage: docs
Stage: pages
Stage: deploy
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mlair/model_modules/convolutional_networks.py
+64
-1
64 additions, 1 deletion
mlair/model_modules/convolutional_networks.py
with
64 additions
and
1 deletion
mlair/model_modules/convolutional_networks.py
+
64
−
1
View file @
57511d09
...
...
@@ -26,9 +26,65 @@ class CNN(AbstractModelClass): # pragma: no cover
_regularizer
=
{
"
l1
"
:
keras
.
regularizers
.
l1
,
"
l2
"
:
keras
.
regularizers
.
l2
,
"
l1_l2
"
:
keras
.
regularizers
.
l1_l2
}
_requirements
=
[
"
lr
"
,
"
beta_1
"
,
"
beta_2
"
,
"
epsilon
"
,
"
decay
"
,
"
amsgrad
"
,
"
momentum
"
,
"
nesterov
"
,
"
l1
"
,
"
l2
"
]
_dropout
=
{
"
selu
"
:
keras
.
layers
.
AlphaDropout
}
_pooling
=
{
"
max
"
:
keras
.
layers
.
MaxPooling2D
,
"
average
"
:
keras
.
layers
.
AveragePooling2D
,
"
mean
"
:
keras
.
layers
.
AveragePooling2D
}
"""
Define CNN model as in the following examples:
* use same kernel for all layers and use in total 3 conv layers, no dropout or pooling is applied
```python
model=CNN,
kernel_size=5,
n_layer=3,
dense_layer_configuration=[128, 64],
```
* specify the kernel sizes, make sure len of kernel size parameter matches number of layers
```python
model=CNN,
kernel_size=[3, 7, 11],
n_layer=3,
dense_layer_configuration=[128, 64],
```
* use different number of filters in each layer (can be combined either with fixed or individual kernel sizes),
make sure that lengths match. Using layer_configuration always overwrites any value given to n_layers parameter.
```python
model=CNN,
kernel_size=[3, 7, 11],
layer_configuration=[24, 48, 48],
```
* now specify individual kernel sizes and number of filters for each layer
```python
model=CNN,
layer_configuration=[(16, 3), (32, 7), (64, 11)],
dense_layer_configuration=[128, 64],
```
* add also some dropout and pooling every 2nd layer, dropout is applied after the conv layer, pooling before. Note
that pooling will not used in the init layer whereas dropout is already applied there.
```python
model=CNN,
dropout_freq=2,
dropout=0.3,
pooling_type=
"
max
"
,
pooling_freq=2,
pooling_size=3,
layer_configuration=[(16, 3), (32, 7), (64, 11)],
dense_layer_configuration=[128, 64],
```
"""
def
__init__
(
self
,
input_shape
:
list
,
output_shape
:
list
,
activation
=
"
relu
"
,
activation_output
=
"
linear
"
,
optimizer
=
"
adam
"
,
regularizer
=
None
,
kernel_size
=
7
,
dropout
=
None
,
dropout_freq
=
None
,
pooling_freq
=
None
,
pooling_type
=
"
max
"
,
n_layer
=
1
,
n_filter
=
10
,
layer_configuration
=
None
,
pooling_size
=
None
,
dense_layer_configuration
=
None
,
**
kwargs
):
...
...
@@ -47,6 +103,7 @@ class CNN(AbstractModelClass): # pragma: no cover
self
.
optimizer
=
self
.
_set_optimizer
(
optimizer
,
**
kwargs
)
self
.
layer_configuration
=
(
n_layer
,
n_filter
,
self
.
kernel_size
)
if
layer_configuration
is
None
else
layer_configuration
self
.
dense_layer_configuration
=
dense_layer_configuration
or
[]
self
.
pooling
=
self
.
_set_pooling
(
pooling_type
)
self
.
pooling_size
=
pooling_size
self
.
dropout
,
self
.
dropout_rate
=
self
.
_set_dropout
(
activation
,
dropout
)
self
.
dropout_freq
=
self
.
_set_layer_freq
(
dropout_freq
)
...
...
@@ -57,6 +114,12 @@ class CNN(AbstractModelClass): # pragma: no cover
self
.
set_compile_options
()
self
.
set_custom_objects
(
loss
=
custom_loss
([
keras
.
losses
.
mean_squared_error
,
var_loss
]),
var_loss
=
var_loss
)
def
_set_pooling
(
self
,
pooling
):
try
:
return
self
.
_pooling
.
get
(
pooling
.
lower
())
except
KeyError
:
raise
AttributeError
(
f
"
Given pooling
{
pooling
}
is not supported in this model class.
"
)
def
_set_layer_freq
(
self
,
param
):
param
=
0
if
param
is
None
else
param
assert
0
<=
param
...
...
@@ -134,7 +197,7 @@ class CNN(AbstractModelClass): # pragma: no cover
x_in
=
x_input
for
layer
,
(
n_filter
,
kernel_size
)
in
enumerate
(
conf
):
if
self
.
pooling_size
is
not
None
and
self
.
pooling_freq
>
0
and
layer
%
self
.
pooling_freq
==
0
and
layer
>
0
:
x_in
=
keras
.
layers
.
MaxP
ooling
2D
((
self
.
pooling_size
,
1
),
strides
=
(
1
,
1
),
padding
=
'
valid
'
)(
x_in
)
x_in
=
self
.
p
ooling
((
self
.
pooling_size
,
1
),
strides
=
(
1
,
1
),
padding
=
'
valid
'
)(
x_in
)
x_in
=
keras
.
layers
.
Conv2D
(
filters
=
n_filter
,
kernel_size
=
(
kernel_size
,
1
),
kernel_initializer
=
self
.
kernel_initializer
,
kernel_regularizer
=
self
.
kernel_regularizer
)(
x_in
)
...
...
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