Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
N
neural_filters
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Build
Pipelines
Jobs
Pipeline schedules
Artifacts
Deploy
Releases
Model registry
Operate
Environments
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
software
neural_filters
Commits
321f76a2
Commit
321f76a2
authored
7 years ago
by
Francois Marelli
Browse files
Options
Downloads
Patches
Plain Diff
First order all-pole
parent
304edcb9
No related branches found
No related tags found
No related merge requests found
Changes
3
Hide whitespace changes
Inline
Side-by-side
Showing
3 changed files
.gitignore
+4
-0
4 additions, 0 deletions
.gitignore
neural_filters/NeuralFilter1P.py
+115
-0
115 additions, 0 deletions
neural_filters/NeuralFilter1P.py
neural_filters/__init__.py
+1
-0
1 addition, 0 deletions
neural_filters/__init__.py
with
120 additions
and
0 deletions
.gitignore
+
4
−
0
View file @
321f76a2
*~
docs/html
docs/sphinx/_build
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
...
...
This diff is collapsed.
Click to expand it.
neural_filters/NeuralFilter1P.py
0 → 100644
+
115
−
0
View file @
321f76a2
"""
NeuralFilter1P
**************
This module implements a trainable all-pole first order filter using pyTorch
Copyright (c) 2018 Idiap Research Institute, http://www.idiap.ch/
Written by Francois Marelli <Francois.Marelli@idiap.ch>
This file is part of neural_filters.
neural_filters is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
neural_filters is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with neural_filters. If not, see <http://www.gnu.org/licenses/>.
"""
import
torch
from
torch.nn
import
Parameter
from
torch.nn
import
functional
as
F
import
math
class
NeuralFilter1P
(
torch
.
nn
.
Module
):
"""
A trainable first-order all-pole filter :math:`
\\
frac{K}{1 - P z^{-1}}`
* **input_size** (int) - the size of the input vector
* **hidden_size** (int) - the size of the output vector
"""
def
__init__
(
self
,
input_size
,
hidden_size
):
super
(
NeuralFilter1P
,
self
).
__init__
()
self
.
input_size
=
input_size
self
.
hidden_size
=
hidden_size
self
.
weight_ih
=
Parameter
(
torch
.
Tensor
(
hidden_size
,
input_size
))
self
.
bias_ih
=
Parameter
(
torch
.
Tensor
(
2
*
hidden_size
))
self
.
reset_parameters
()
def
reset_parameters
(
self
):
stdv
=
1.0
/
math
.
sqrt
(
self
.
hidden_size
)
for
weight
in
self
.
parameters
():
weight
.
data
.
uniform_
(
-
stdv
,
stdv
)
def
forward
(
self
,
input
,
hx
=
None
):
if
hx
is
None
:
vhx
=
torch
.
autograd
.
Variable
(
input
.
data
.
new
(
input
.
size
(
1
),
self
.
hidden_size
).
zero_
(),
requires_grad
=
False
)
hx
=
(
vhx
,
vhx
)
self
.
check_forward_input
(
input
)
self
.
check_forward_hidden
(
input
,
hx
[
0
],
'
[0]
'
)
self
.
check_forward_hidden
(
input
,
hx
[
1
],
'
[1]
'
)
hidden
=
hx
output
=
[]
steps
=
range
(
input
.
size
(
0
))
for
i
in
steps
:
hidden
=
self
.
step
(
input
[
i
],
hidden
)
output
.
append
(
hidden
[
0
])
output
=
torch
.
cat
(
output
,
0
).
view
(
input
.
size
(
0
),
*
output
[
0
].
size
())
return
output
,
hidden
def
__repr__
(
self
):
s
=
'
{name}({input_size}, {hidden_size}
'
return
s
.
format
(
name
=
self
.
__class__
.
__name__
,
**
self
.
__dict__
)
def
check_forward_input
(
self
,
input
):
if
input
.
size
(
-
1
)
!=
self
.
input_size
:
raise
RuntimeError
(
"
input has inconsistent input_size(-1): got {}, expected {}
"
.
format
(
input
.
size
(
1
),
self
.
input_size
))
def
check_forward_hidden
(
self
,
input
,
hx
,
hidden_label
=
''
):
if
input
.
size
(
1
)
!=
hx
.
size
(
0
):
raise
RuntimeError
(
"
Input batch size {} doesn
'
t match hidden{} batch size {}
"
.
format
(
input
.
size
(
1
),
hidden_label
,
hx
.
size
(
0
)))
if
hx
.
size
(
1
)
!=
self
.
hidden_size
:
raise
RuntimeError
(
"
hidden{} has inconsistent hidden_size: got {}, expected {}
"
.
format
(
hidden_label
,
hx
.
size
(
1
),
self
.
hidden_size
))
def
step
(
self
,
input
,
hidden
):
hx
,
cx
=
hidden
gates
=
F
.
linear
(
input
,
self
.
weight_ih
,
self
.
bias_ih
)
forgetgate
,
cellgate
=
gates
.
chunk
(
2
,
1
)
forgetgate
=
F
.
sigmoid
(
forgetgate
)
cy
=
(
forgetgate
*
cx
)
+
cellgate
hy
=
cy
return
hy
,
cy
test
=
NeuralFilter1P
(
2
,
2
)
This diff is collapsed.
Click to expand it.
neural_filters/__init__.py
0 → 100644
+
1
−
0
View file @
321f76a2
from
.NeuralFilter1P
import
*
\ No newline at end of file
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment