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33940965
first CNN class try
· 33940965
leufen1
authored
4 years ago
mlair/model_modules/convolutional_networks.py
0 → 100644
+
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__author__
=
"
Lukas Leufen
"
__date__
=
'
2021-02-
'
from
functools
import
reduce
,
partial
from
mlair.model_modules
import
AbstractModelClass
from
mlair.helpers
import
select_from_dict
from
mlair.model_modules.loss
import
var_loss
,
custom_loss
from
mlair.model_modules.advanced_paddings
import
PadUtils
,
Padding2D
,
SymmetricPadding2D
import
keras
class
CNN
(
AbstractModelClass
):
_activation
=
{
"
relu
"
:
keras
.
layers
.
ReLU
,
"
tanh
"
:
partial
(
keras
.
layers
.
Activation
,
"
tanh
"
),
"
sigmoid
"
:
partial
(
keras
.
layers
.
Activation
,
"
sigmoid
"
),
"
linear
"
:
partial
(
keras
.
layers
.
Activation
,
"
linear
"
),
"
selu
"
:
partial
(
keras
.
layers
.
Activation
,
"
selu
"
)}
_initializer
=
{
"
selu
"
:
keras
.
initializers
.
lecun_normal
()}
_optimizer
=
{
"
adam
"
:
keras
.
optimizers
.
adam
}
_regularizer
=
{
"
l1
"
:
keras
.
regularizers
.
l1
,
"
l2
"
:
keras
.
regularizers
.
l2
,
"
l1_l2
"
:
keras
.
regularizers
.
l1_l2
}
_requirements
=
[
"
lr
"
,
"
beta_1
"
,
"
beta_2
"
,
"
epsilon
"
,
"
decay
"
,
"
amsgrad
"
]
def
__init__
(
self
,
input_shape
:
list
,
output_shape
:
list
,
activation
=
"
relu
"
,
activation_output
=
"
linear
"
,
optimizer
=
"
adam
"
,
regularizer
=
None
,
**
kwargs
):
assert
len
(
input_shape
)
==
1
assert
len
(
output_shape
)
==
1
super
().
__init__
(
input_shape
[
0
],
output_shape
[
0
])
# settings
self
.
activation
=
self
.
_set_activation
(
activation
)
self
.
activation_name
=
activation
self
.
activation_output
=
self
.
_set_activation
(
activation_output
)
self
.
activation_output_name
=
activation_output
self
.
kernel_initializer
=
self
.
_initializer
.
get
(
activation
,
"
glorot_uniform
"
)
self
.
kernel_regularizer
=
self
.
_set_regularizer
(
regularizer
,
**
kwargs
)
self
.
optimizer
=
self
.
_set_optimizer
(
optimizer
,
**
kwargs
)
# apply to model
self
.
set_model
()
self
.
set_compile_options
()
self
.
set_custom_objects
(
loss
=
custom_loss
([
keras
.
losses
.
mean_squared_error
,
var_loss
]),
var_loss
=
var_loss
)
def
_set_activation
(
self
,
activation
):
try
:
return
self
.
_activation
.
get
(
activation
.
lower
())
except
KeyError
:
raise
AttributeError
(
f
"
Given activation
{
activation
}
is not supported in this model class.
"
)
def
_set_optimizer
(
self
,
optimizer
,
**
kwargs
):
try
:
opt_name
=
optimizer
.
lower
()
opt
=
self
.
_optimizer
.
get
(
opt_name
)
opt_kwargs
=
{}
if
opt_name
==
"
adam
"
:
opt_kwargs
=
select_from_dict
(
kwargs
,
[
"
lr
"
,
"
beta_1
"
,
"
beta_2
"
,
"
epsilon
"
,
"
decay
"
,
"
amsgrad
"
])
return
opt
(
**
opt_kwargs
)
except
KeyError
:
raise
AttributeError
(
f
"
Given optimizer
{
optimizer
}
is not supported in this model class.
"
)
def
_set_regularizer
(
self
,
regularizer
,
**
kwargs
):
if
regularizer
is
None
or
(
isinstance
(
regularizer
,
str
)
and
regularizer
.
lower
()
==
"
none
"
):
return
None
try
:
reg_name
=
regularizer
.
lower
()
reg
=
self
.
_regularizer
.
get
(
reg_name
)
reg_kwargs
=
{}
if
reg_name
in
[
"
l1
"
,
"
l2
"
]:
reg_kwargs
=
select_from_dict
(
kwargs
,
reg_name
,
remove_none
=
True
)
if
reg_name
in
reg_kwargs
:
reg_kwargs
[
"
l
"
]
=
reg_kwargs
.
pop
(
reg_name
)
elif
reg_name
==
"
l1_l2
"
:
reg_kwargs
=
select_from_dict
(
kwargs
,
[
"
l1
"
,
"
l2
"
],
remove_none
=
True
)
return
reg
(
**
reg_kwargs
)
except
KeyError
:
raise
AttributeError
(
f
"
Given regularizer
{
regularizer
}
is not supported in this model class.
"
)
def
set_model
(
self
):
"""
Build the model.
"""
x_input
=
keras
.
layers
.
Input
(
shape
=
self
.
_input_shape
)
kernel
=
(
1
,
1
)
pad_size
=
PadUtils
.
get_padding_for_same
(
kernel
)
x_in
=
Padding2D
(
"
SymPad2D
"
)(
padding
=
pad_size
,
name
=
"
SymPad
"
)(
x_input
)
x_in
=
keras
.
layers
.
Conv2D
(
filters
=
16
,
kernel_size
=
kernel
,
kernel_initializer
=
self
.
kernel_initializer
,
kernel_regularizer
=
self
.
kernel_regularizer
)(
x_in
)
x_in
=
self
.
activation
()(
x_in
)
x_in
=
keras
.
layers
.
Conv2D
(
filters
=
32
,
kernel_size
=
kernel
,
kernel_initializer
=
self
.
kernel_initializer
,
kernel_regularizer
=
self
.
kernel_regularizer
)(
x_in
)
x_in
=
self
.
activation
()(
x_in
)
x_in
=
Padding2D
(
"
SymPad2D
"
)(
padding
=
pad_size
,
name
=
"
SymPad
"
)(
x_in
)
x_in
=
keras
.
layers
.
Conv2D
(
filters
=
64
,
kernel_size
=
kernel
,
kernel_initializer
=
self
.
kernel_initializer
,
kernel_regularizer
=
self
.
kernel_regularizer
)(
x_in
)
x_in
=
self
.
activation
()(
x_in
)
x_in
=
keras
.
layers
.
Flatten
()(
x_in
)
x_in
=
keras
.
layers
.
Dense
(
64
,
kernel_initializer
=
self
.
kernel_initializer
,
kernel_regularizer
=
self
.
kernel_regularizer
)(
x_in
)
x_in
=
self
.
activation
()(
x_in
)
x_in
=
keras
.
layers
.
Dense
(
16
,
kernel_initializer
=
self
.
kernel_initializer
,
kernel_regularizer
=
self
.
kernel_regularizer
)(
x_in
)
x_in
=
self
.
activation
()(
x_in
)
x_in
=
keras
.
layers
.
Dense
(
self
.
_output_shape
)(
x_in
)
out
=
self
.
activation_output
(
name
=
f
"
{
self
.
activation_output_name
}
_output
"
)(
x_in
)
self
.
model
=
keras
.
Model
(
inputs
=
x_input
,
outputs
=
[
out
])
def
set_compile_options
(
self
):
self
.
compile_options
=
{
"
loss
"
:
[
custom_loss
([
keras
.
losses
.
mean_squared_error
,
var_loss
])],
"
metrics
"
:
[
"
mse
"
,
"
mae
"
,
var_loss
]}
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