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Alejandro Riera Mainar
learning-autograd
Commits
91e2b02c
Commit
91e2b02c
authored
Nov 18, 2018
by
Alejandro Riera
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courseras logistic regresion example written with ptyroch's autograd
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"""
My attempt at reproducing Coursera's logistic regresion example with autograd
"""
import
numpy
as
np
import
torch
class
LogRegModel
():
def
__init__
(
self
):
self
.
w
=
None
self
.
b
=
None
def
train
(
self
,
X
,
Y
,
epochs
=
1000
,
learning_rate
=
0.5
):
self
.
w
=
torch
.
zeros
((
X
.
shape
[
0
],
1
),
requires_grad
=
True
,
dtype
=
torch
.
float64
)
self
.
b
=
torch
.
zeros
((
1
,
1
),
requires_grad
=
True
,
dtype
=
torch
.
double
)
m
=
X
.
shape
[
1
]
for
i
in
range
(
epochs
):
z
=
self
.
w
.
transpose
(
0
,
1
).
mm
(
X
).
add
(
self
.
b
)
A
=
torch
.
sigmoid
(
z
)
loss
=
Y
.
mul
(
A
.
log
())
+
(
1
-
Y
).
mul
((
1
-
A
).
log
())
loss
=
loss
.
sum
()
/
(
-
m
)
loss
.
backward
()
with
torch
.
no_grad
():
self
.
w
-=
learning_rate
*
self
.
w
.
grad
self
.
b
-=
learning_rate
*
self
.
b
.
grad
# Manually zero the gradients after running the backward pass
self
.
w
.
grad
.
zero_
()
self
.
b
.
grad
.
zero_
()
if
i
%
100
==
0
:
print
(
"Loss after iteration %i: %f"
%
(
i
,
loss
))
def
predict
(
self
,
X
):
with
torch
.
no_grad
():
z
=
self
.
w
.
transpose
(
0
,
1
).
mm
(
X
).
add
(
self
.
b
)
Y_pred
=
torch
.
sigmoid
(
z
)
Y_pred
[
Y_pred
<
0.5
]
=
0
Y_pred
[
Y_pred
>=
0.5
]
=
1
return
Y_pred
def
benchmark
(
self
,
X
,
Y
):
Y_pred
=
self
.
predict
(
X
)
accuracy
=
np
.
mean
(
np
.
abs
(
Y_pred
.
numpy
()
-
Y
.
numpy
()))
accuracy
=
100
-
100
*
accuracy
return
accuracy
def
predict
(
w
,
b
,
X
):
with
torch
.
no_grad
():
z
=
w
.
transpose
(
0
,
1
).
mm
(
X
).
add
(
b
)
Y_pred
=
torch
.
sigmoid
(
z
)
Y_pred
[
Y_pred
<
0.5
]
=
0
Y_pred
[
Y_pred
>=
0.5
]
=
1
return
Y_pred
def
model
(
X_train
,
Y_train
,
X_test
,
Y_test
,
num_iterations
=
2000
,
learning_rate
=
0.5
,
print_cost
=
False
):
w
=
torch
.
zeros
((
X_train
.
shape
[
0
],
1
),
requires_grad
=
True
,
dtype
=
torch
.
float64
)
b
=
torch
.
zeros
((
1
,
1
),
requires_grad
=
True
,
dtype
=
torch
.
double
)
m
=
X_train
.
shape
[
1
]
for
i
in
range
(
num_iterations
):
z
=
w
.
transpose
(
0
,
1
).
mm
(
X_train
).
add
(
b
)
A
=
torch
.
sigmoid
(
z
)
loss
=
Y_train
.
mul
(
A
.
log
())
+
(
1
-
Y_train
).
mul
((
1
-
A
).
log
())
loss
=
loss
.
sum
()
/
(
-
m
)
loss
.
backward
()
# learning_rate = 0.01
with
torch
.
no_grad
():
w
-=
learning_rate
*
w
.
grad
b
-=
learning_rate
*
b
.
grad
# Manually zero the gradients after running the backward pass
w
.
grad
.
zero_
()
b
.
grad
.
zero_
()
if
print_cost
and
i
%
100
==
0
:
print
(
"Loss after iteration %i: %f"
%
(
i
,
loss
))
Y_pred_train
=
predict
(
w
,
b
,
X_train
)
Y_pred_test
=
predict
(
w
,
b
,
X_test
)
print
(
"train accuracy: {} %"
.
format
(
100
-
np
.
mean
(
np
.
abs
(
Y_pred_train
.
detach
().
numpy
()
-
Y_train
.
detach
().
numpy
()))
*
100
))
print
(
"test accuracy: {} %"
.
format
(
100
-
np
.
mean
(
np
.
abs
(
Y_pred_test
.
detach
().
numpy
()
-
Y_test
.
detach
().
numpy
()))
*
100
))
return
w
,
b
if
__name__
==
"__main__"
:
from
coursera01w02.lr_utils
import
load_dataset
train_set_x_orig
,
train_set_y
,
test_set_x_orig
,
test_set_y
,
classes
=
load_dataset
()
train_set_x_flatten
=
train_set_x_orig
.
reshape
(
train_set_x_orig
.
shape
[
0
],
-
1
).
T
test_set_x_flatten
=
test_set_x_orig
.
reshape
(
test_set_x_orig
.
shape
[
0
],
-
1
).
T
train_set_x
=
train_set_x_flatten
/
255.
test_set_x
=
test_set_x_flatten
/
255.
train_set_x
=
torch
.
from_numpy
(
train_set_x
).
type
(
torch
.
float64
)
train_set_y
=
torch
.
from_numpy
(
train_set_y
).
type
(
torch
.
float64
)
test_set_x
=
torch
.
from_numpy
(
test_set_x
).
type
(
torch
.
float64
)
test_set_y
=
torch
.
from_numpy
(
test_set_y
).
type
(
torch
.
float64
)
# d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
logreg
=
LogRegModel
()
logreg
.
train
(
train_set_x
,
train_set_y
,
epochs
=
2000
,
learning_rate
=
0.005
)
train_accuracy
=
logreg
.
benchmark
(
train_set_x
,
train_set_y
)
test_accuracy
=
logreg
.
benchmark
(
test_set_x
,
test_set_y
)
print
(
f
"train accuracy:
{
train_accuracy
}
"
)
print
(
f
"test accuracy:
{
test_accuracy
}
"
)
\ No newline at end of file
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