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mnsc
supr
Commits
48793abd
Commit
48793abd
authored
3 years ago
by
mnsc
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parent
4cc5c3c5
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1
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1 changed file
supr/layers.py
+92
-12
92 additions, 12 deletions
supr/layers.py
with
92 additions
and
12 deletions
supr/layers.py
+
92
−
12
View file @
48793abd
...
...
@@ -7,9 +7,10 @@ from typing import List
# Data:
# N x V
# └───│── N: Data points
# └── V: Variables
# N x V x D
# └───│──│─ N: Data points
# └──│─ V: Variables
# └─ D: Dimensions
#
# Probability:
# N x T x V x C
...
...
@@ -23,6 +24,7 @@ class Supr(nn.Module):
super
().
__init__
()
def
sample
(
self
):
pass
class
SuprLayer
(
nn
.
Module
):
...
...
@@ -253,10 +255,15 @@ class TrackSum(ProductSumLayer):
class
NormalLeaf
(
SuprLayer
):
"""
NormalLeaf layer
"""
def
__init__
(
self
,
tracks
:
int
,
variables
:
int
,
channels
:
int
):
def
__init__
(
self
,
tracks
:
int
,
variables
:
int
,
channels
:
int
,
n
:
int
=
1
,
mu0
:
float
=
0.
,
nu0
:
float
=
0.
,
alpha0
:
float
=
0.
,
beta0
:
float
=
0.
):
super
().
__init__
()
# Dimensions
self
.
T
,
self
.
V
,
self
.
C
=
tracks
,
variables
,
channels
# Number of data points
self
.
n
=
n
# Prior
self
.
mu0
,
self
.
nu0
,
self
.
alpha0
,
self
.
beta0
=
mu0
,
nu0
,
alpha0
,
beta0
# Parametes
# self.mu = nn.Parameter(torch.randn(self.T, self.V, self.C))
# self.mu = nn.Parameter(torch.linspace(0, 1, self.C)[None, None, :].repeat((self.T, self.V, 1)))
...
...
@@ -279,13 +286,16 @@ class NormalLeaf(SuprLayer):
self
.
z_x_sq_acc
.
data
+=
torch
.
sum
(
self
.
z
.
grad
*
self
.
x
[:,
None
,
:,
None
]
**
2
,
dim
=
0
)
def
em_update
(
self
,
learning_rate
:
float
=
1.
):
#
Mean
#
Sum of weights
sum_z
=
torch
.
clamp
(
self
.
z_acc
,
self
.
epsilon
)
# Mean
mu_update
=
(
self
.
nu0
*
self
.
mu0
+
self
.
n
*
(
self
.
z_x_acc
/
sum_z
))
/
(
self
.
nu0
+
self
.
n
)
self
.
mu
.
data
*=
1.
-
learning_rate
self
.
mu
.
data
+=
learning_rate
*
self
.
z_x_acc
/
sum_z
self
.
mu
.
data
+=
learning_rate
*
mu_update
# Standard deviation
sig_update
=
(
self
.
n
*
(
self
.
z_x_sq_acc
/
sum_z
-
self
.
mu
**
2
)
+
2
*
self
.
beta0
+
self
.
nu0
*
(
self
.
mu0
-
self
.
mu
)
**
2
)
/
(
self
.
n
+
2
*
self
.
alpha0
+
3
)
self
.
sig
.
data
*=
1
-
learning_rate
self
.
sig
.
data
+=
learning_rate
*
torch
.
sqrt
(
torch
.
clamp
(
self
.
z_x_sq_acc
/
sum_z
-
self
.
mu
**
2
,
self
.
epsilon
+
0.01
))
self
.
sig
.
data
+=
learning_rate
*
sig_update
# Reset accumulators
self
.
z_acc
.
zero_
()
self
.
z_x_acc
.
zero_
()
...
...
@@ -296,7 +306,7 @@ class NormalLeaf(SuprLayer):
mu_marginalize
=
self
.
mu
[
track
,
self
.
marginalize
,
channel_per_variable
[
self
.
marginalize
]]
sig_marginalize
=
self
.
sig
[
track
,
self
.
marginalize
,
channel_per_variable
[
self
.
marginalize
]]
r
=
torch
.
empty_like
(
self
.
x
[
0
])
r
[
self
.
marginalize
]
=
mu_marginalize
+
torch
.
randn
(
variables_marginalize
).
to
(
self
.
x
.
device
)
*
sig_marginalize
r
[
self
.
marginalize
]
=
mu_marginalize
+
torch
.
randn
(
variables_marginalize
).
to
(
self
.
x
.
device
)
*
torch
.
sqrt
(
torch
.
clamp
(
sig_marginalize
,
self
.
epsilon
))
r
[
~
self
.
marginalize
]
=
self
.
x
[
0
][
~
self
.
marginalize
]
return
r
...
...
@@ -313,16 +323,21 @@ class NormalLeaf(SuprLayer):
x_valid
=
self
.
x
[:,
None
,
~
self
.
marginalize
,
None
]
# Evaluate log probability
self
.
z
.
data
[:,
:,
~
self
.
marginalize
,
:]
=
\
torch
.
distributions
.
Normal
(
mu_valid
,
sig_valid
).
log_prob
(
x_valid
).
float
()
torch
.
distributions
.
Normal
(
mu_valid
,
torch
.
sqrt
(
torch
.
clamp
(
sig_valid
,
self
.
epsilon
))
).
log_prob
(
x_valid
).
float
()
return
self
.
z
class
BernoulliLeaf
(
SuprLayer
):
"""
BernoulliLeaf layer
"""
def
__init__
(
self
,
tracks
:
int
,
variables
:
int
,
channels
:
int
):
def
__init__
(
self
,
tracks
:
int
,
variables
:
int
,
channels
:
int
,
n
:
int
=
1
,
alpha0
:
float
=
1.
,
beta0
:
float
=
1.
):
super
().
__init__
()
# Dimensions
self
.
T
,
self
.
V
,
self
.
C
=
tracks
,
variables
,
channels
# Number of data points
self
.
n
=
n
# Prior
self
.
alpha0
,
self
.
beta0
=
alpha0
,
beta0
# Parametes
self
.
p
=
nn
.
Parameter
(
torch
.
rand
(
self
.
T
,
self
.
V
,
self
.
C
))
# Which variables to marginalized
...
...
@@ -342,8 +357,9 @@ class BernoulliLeaf(SuprLayer):
def
em_update
(
self
,
learning_rate
:
float
=
1.
):
# Probability
sum_z
=
torch
.
clamp
(
self
.
z_acc
,
self
.
epsilon
)
p_update
=
(
self
.
n
*
self
.
z_x_acc
/
sum_z
+
self
.
alpha0
-
1
)
/
(
self
.
n
+
self
.
alpha0
+
self
.
beta0
-
2
)
self
.
p
.
data
*=
1.
-
learning_rate
self
.
p
.
data
+=
learning_rate
*
self
.
z_x_acc
/
sum_z
self
.
p
.
data
+=
learning_rate
*
p_update
# Reset accumulators
self
.
z_acc
.
zero_
()
self
.
z_x_acc
.
zero_
()
...
...
@@ -368,6 +384,70 @@ class BernoulliLeaf(SuprLayer):
x_valid
=
self
.
x
[:,
None
,
~
self
.
marginalize
,
None
]
# Evaluate log probability
self
.
z
.
data
[:,
:,
~
self
.
marginalize
,
:]
=
\
p_valid
*
(
x_valid
==
1
)
+
(
1
-
p_valid
)
*
(
x_valid
==
0
)
torch
.
distributions
.
Bernoulli
(
probs
=
p_valid
).
log_prob
(
x_valid
).
float
()
return
self
.
z
# TODO: This is not tested properly.
class
CategoricalLeaf
(
SuprLayer
):
"""
CategoricalLeaf layer
"""
def
__init__
(
self
,
tracks
:
int
,
variables
:
int
,
channels
:
int
,
dimensions
:
int
,
n
:
int
=
1
,
alpha0
:
float
=
1.
):
super
().
__init__
()
# Dimensions
self
.
T
,
self
.
V
,
self
.
C
,
self
.
D
=
tracks
,
variables
,
channels
,
dimensions
# Number of data points
self
.
n
=
n
# Prior
self
.
alpha0
=
alpha0
# Parametes
self
.
p
=
nn
.
Parameter
(
torch
.
rand
(
self
.
T
,
self
.
V
,
self
.
C
,
self
.
D
))
# Which variables to marginalized
self
.
register_buffer
(
'
marginalize
'
,
torch
.
zeros
(
variables
,
dtype
=
torch
.
bool
))
# Input
self
.
register_buffer
(
'
x
'
,
torch
.
Tensor
())
# Output
self
.
register_buffer
(
'
z
'
,
torch
.
Tensor
())
# EM accumulator
self
.
register_buffer
(
'
z_acc
'
,
torch
.
zeros
(
self
.
T
,
self
.
V
,
self
.
C
))
self
.
register_buffer
(
'
z_x_acc
'
,
torch
.
zeros
(
self
.
T
,
self
.
V
,
self
.
C
,
self
.
D
))
def
em_batch
(
self
):
self
.
z_acc
.
data
+=
torch
.
sum
(
self
.
z
.
grad
,
dim
=
0
)
x_onehot
=
torch
.
eye
(
self
.
D
,
dtype
=
bool
)[
self
.
x
]
self
.
z_x_acc
.
data
+=
torch
.
sum
(
self
.
z
.
grad
[:,
:,
:,
:,
None
]
*
x_onehot
[:,
None
,
:,
None
,
:],
dim
=
0
)
def
em_update
(
self
,
learning_rate
:
float
=
1.
):
# Probability
sum_z
=
torch
.
clamp
(
self
.
z_acc
,
self
.
epsilon
)
p_update
=
(
self
.
n
*
self
.
z_x_acc
/
sum_z
[:,:,:,
None
]
+
self
.
alpha0
-
1
)
/
(
self
.
n
+
self
.
D
*
(
self
.
alpha0
-
1
))
self
.
p
.
data
*=
1.
-
learning_rate
self
.
p
.
data
+=
learning_rate
*
p_update
# Reset accumulators
self
.
z_acc
.
zero_
()
self
.
z_x_acc
.
zero_
()
# XXX Implement this
def
sample
(
self
,
track
:
int
,
channel_per_variable
:
torch
.
Tensor
):
p_marginalize
=
self
.
p
[
track
,
self
.
marginalize
,
channel_per_variable
[
self
.
marginalize
],
:]
r
=
torch
.
empty_like
(
self
.
x
[
0
])
r_sample
=
torch
.
distributions
.
Categorical
(
probs
=
p_marginalize
).
sample
()
r
[
self
.
marginalize
]
=
r_sample
r
[
~
self
.
marginalize
]
=
self
.
x
[
0
][
~
self
.
marginalize
]
return
r
def
forward
(
self
,
x
:
torch
.
Tensor
):
# Get shape
batch_size
=
x
.
shape
[
0
]
# Store the data
self
.
x
=
x
# Compute the probability
self
.
z
=
torch
.
zeros
(
batch_size
,
self
.
T
,
self
.
V
,
self
.
C
,
requires_grad
=
True
,
device
=
x
.
device
)
# Get non-marginalized parameters and data
p_valid
=
self
.
p
[
None
,
:,
~
self
.
marginalize
,
:,
:]
x_valid
=
self
.
x
[:,
None
,
~
self
.
marginalize
,
None
]
# Evaluate log probability
self
.
z
.
data
[:,
:,
~
self
.
marginalize
,
:]
=
\
torch
.
distributions
.
Categorical
(
probs
=
p_valid
).
log_prob
(
x_valid
).
float
()
return
self
.
z
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