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9c10efd4
implemented residual network
· 9c10efd4
leufen1
authored
2 years ago
mlair/model_modules/residual_networks.py
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__author__
=
"
Lukas Leufen
"
__date__
=
"
2021-08-23
"
from
mlair.model_modules.branched_input_networks
import
BranchedInputCNN
import
tensorflow.keras
as
keras
class
BranchedInputResNet
(
BranchedInputCNN
):
"""
A convolutional neural network with multiple input branches and residual blocks (skip connections).
```python
input_shape = [(65,1,9)]
output_shape = [(4, )]
# model
layer_configuration=[
{
"
type
"
:
"
Conv2D
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (7, 1),
"
filters
"
: 32,
"
padding
"
:
"
same
"
},
{
"
type
"
:
"
MaxPooling2D
"
,
"
pool_size
"
: (2, 1),
"
strides
"
: (2, 1)},
{
"
type
"
:
"
residual_block
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (3, 1),
"
filters
"
: 32,
"
strides
"
: (1, 1),
"
kernel_regularizer
"
:
"
l2
"
},
{
"
type
"
:
"
residual_block
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (3, 1),
"
filters
"
: 32,
"
strides
"
: (1, 1),
"
kernel_regularizer
"
:
"
l2
"
},
{
"
type
"
:
"
residual_block
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (3, 1),
"
filters
"
: 64,
"
strides
"
: (1, 1),
"
kernel_regularizer
"
:
"
l2
"
,
"
use_1x1conv
"
: True},
{
"
type
"
:
"
residual_block
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (3, 1),
"
filters
"
: 64,
"
strides
"
: (1, 1),
"
kernel_regularizer
"
:
"
l2
"
},
{
"
type
"
:
"
residual_block
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (3, 1),
"
filters
"
: 128,
"
strides
"
: (1, 1),
"
kernel_regularizer
"
:
"
l2
"
,
"
use_1x1conv
"
: True},
{
"
type
"
:
"
residual_block
"
,
"
activation
"
:
"
relu
"
,
"
kernel_size
"
: (3, 1),
"
filters
"
: 128,
"
strides
"
: (1, 1),
"
kernel_regularizer
"
:
"
l2
"
},
{
"
type
"
:
"
MaxPooling2D
"
,
"
pool_size
"
: (2, 1),
"
strides
"
: (2, 1)},
{
"
type
"
:
"
Dropout
"
,
"
rate
"
: 0.25},
{
"
type
"
:
"
Flatten
"
},
{
"
type
"
:
"
Concatenate
"
},
{
"
type
"
:
"
Dense
"
,
"
units
"
: 128,
"
activation
"
:
"
relu
"
}
]
model = BranchedInputResNet(input_shape, output_shape, layer_configuration)
```
"""
def
__init__
(
self
,
input_shape
:
list
,
output_shape
:
list
,
layer_configuration
:
list
,
optimizer
=
"
adam
"
,
**
kwargs
):
super
().
__init__
(
input_shape
,
output_shape
,
layer_configuration
,
optimizer
=
optimizer
,
**
kwargs
)
@staticmethod
def
residual_block
(
**
layer_kwargs
):
layer_name
=
layer_kwargs
.
pop
(
"
name
"
).
split
(
"
_
"
)
layer_name
=
"
_
"
.
join
([
*
layer_name
[
0
:
2
],
"
%s
"
,
*
layer_name
[
2
:]])
act
=
layer_kwargs
.
pop
(
"
activation
"
)
act_name
=
act
.
__name__
use_1x1conv
=
layer_kwargs
.
pop
(
"
use_1x1conv
"
,
False
)
def
block
(
x
):
layer_kwargs
.
update
({
"
strides
"
:
2
if
use_1x1conv
else
1
})
y
=
keras
.
layers
.
Conv2D
(
**
layer_kwargs
,
padding
=
'
same
'
,
name
=
layer_name
%
"
Conv1
"
)(
x
)
y
=
act
(
name
=
layer_name
%
f
"
{
act_name
}
1
"
)(
y
)
layer_kwargs
.
update
({
"
strides
"
:
1
})
y
=
keras
.
layers
.
Conv2D
(
**
layer_kwargs
,
padding
=
'
same
'
,
name
=
layer_name
%
"
Conv2
"
)(
y
)
y
=
keras
.
layers
.
BatchNormalization
(
name
=
layer_name
%
"
BN2
"
)(
y
)
if
use_1x1conv
is
True
:
layer_kwargs
.
update
({
"
strides
"
:
2
})
layer_kwargs
.
update
({
"
kernel_size
"
:
1
})
x
=
keras
.
layers
.
Conv2D
(
**
layer_kwargs
,
padding
=
'
same
'
,
name
=
layer_name
%
"
Conv1x1
"
)(
x
)
out
=
keras
.
layers
.
Add
(
name
=
layer_name
%
"
Add
"
)([
x
,
y
])
out
=
act
(
name
=
layer_name
%
f
"
{
act_name
}
2
"
)(
out
)
return
out
return
block
def
_extract_layer_conf
(
self
,
layer_opts
):
follow_up_layer
=
None
layer_type
=
layer_opts
.
pop
(
"
type
"
)
activation_type
=
layer_opts
.
pop
(
"
activation
"
,
None
)
if
activation_type
is
not
None
:
activation
=
self
.
_activation
.
get
(
activation_type
)
kernel_initializer
=
self
.
_initializer
.
get
(
activation_type
,
"
glorot_uniform
"
)
layer_opts
[
"
kernel_initializer
"
]
=
kernel_initializer
follow_up_layer
=
activation
regularizer_type
=
layer_opts
.
pop
(
"
kernel_regularizer
"
,
None
)
if
regularizer_type
is
not
None
:
layer_opts
[
"
kernel_regularizer
"
]
=
self
.
_set_regularizer
(
regularizer_type
,
**
self
.
kwargs
)
if
layer_type
.
lower
()
==
"
residual_block
"
:
layer
=
self
.
residual_block
layer_opts
[
"
activation
"
]
=
follow_up_layer
follow_up_layer
=
None
else
:
layer
=
getattr
(
keras
.
layers
,
layer_type
,
None
)
return
layer
,
layer_opts
,
follow_up_layer
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