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Data Augmentation for Metonymy Resolution
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friebolin
Data Augmentation for Metonymy Resolution
Commits
67fbbea7
Commit
67fbbea7
authored
2 years ago
by
kulcsar
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710dfaba
# loss visualization
import
matplotlib.pyplot
as
plt
import
re
import
numpy
as
np
from
statistics
import
mean
with
open
(
"
output_loss_epoch_data.txt
"
,
"
r
"
)
as
f
:
loss_file
=
f
.
read
()
loss_pattern
=
r
"
\((\d+\.\d+)
"
epochs_pattern
=
r
"
Epoche: *(\d+)
"
loss_values
=
re
.
findall
(
loss_pattern
,
loss_file
)
eps
=
[
1
,
2
,
3
,
4
,
5
]
#epochs = re.findall(epochs_pattern, loss_file)
# test test test
single_loss_batch
=
int
(
len
(
loss_values
)
/
5
)
loss_1
=
loss_values
[:
single_loss_batch
]
loss_1
=
[
eval
(
i
)
for
i
in
loss_1
]
loss_1_1
=
loss_1
[:
84
]
loss_1_2
=
loss_1
[
len
(
loss_1_1
):
len
(
loss_1_1
)
+
84
]
loss_1_3
=
loss_1
[
len
(
loss_1_2
):
len
(
loss_1_2
)
+
84
]
loss_1_4
=
loss_1
[
len
(
loss_1_3
):
len
(
loss_1_3
)
+
84
]
loss_1_5
=
loss_1
[
len
(
loss_1_4
):
len
(
loss_1_4
)
+
84
]
mean_1_1
=
mean
(
loss_1_1
)
mean_1_2
=
mean
(
loss_1_2
)
mean_1_3
=
mean
(
loss_1_3
)
mean_1_4
=
mean
(
loss_1_4
)
mean_1_5
=
mean
(
loss_1_5
)
loss_2
=
loss_values
[
len
(
loss_1
):
len
(
loss_1
)
+
single_loss_batch
]
loss_2
=
[
eval
(
i
)
for
i
in
loss_2
]
loss_2_1
=
loss_2
[:
84
]
loss_2_2
=
loss_2
[
len
(
loss_2_1
):
len
(
loss_2_1
)
+
84
]
loss_2_3
=
loss_2
[
len
(
loss_2_2
):
len
(
loss_2_2
)
+
84
]
loss_2_4
=
loss_2
[
len
(
loss_2_3
):
len
(
loss_2_3
)
+
84
]
loss_2_5
=
loss_2
[
len
(
loss_2_4
):
len
(
loss_2_4
)
+
84
]
mean_2_1
=
mean
(
loss_2_1
)
mean_2_2
=
mean
(
loss_2_2
)
mean_2_3
=
mean
(
loss_2_3
)
mean_2_4
=
mean
(
loss_2_4
)
mean_2_5
=
mean
(
loss_2_5
)
loss_3
=
loss_values
[
len
(
loss_2
):
len
(
loss_2
)
+
single_loss_batch
]
loss_3
=
[
eval
(
i
)
for
i
in
loss_3
]
loss_3_1
=
loss_3
[:
84
]
loss_3_2
=
loss_3
[
len
(
loss_3_1
):
len
(
loss_3_1
)
+
84
]
loss_3_3
=
loss_3
[
len
(
loss_3_2
):
len
(
loss_3_2
)
+
84
]
loss_3_4
=
loss_3
[
len
(
loss_3_3
):
len
(
loss_3_3
)
+
84
]
loss_3_5
=
loss_3
[
len
(
loss_3_4
):
len
(
loss_3_4
)
+
84
]
mean_3_1
=
mean
(
loss_3_1
)
mean_3_2
=
mean
(
loss_3_2
)
mean_3_3
=
mean
(
loss_3_3
)
mean_3_4
=
mean
(
loss_3_4
)
mean_3_5
=
mean
(
loss_3_5
)
loss_4
=
loss_values
[
len
(
loss_3
):
len
(
loss_3
)
+
single_loss_batch
]
loss_4
=
[
eval
(
i
)
for
i
in
loss_4
]
loss_4_1
=
loss_4
[:
84
]
loss_4_2
=
loss_4
[
len
(
loss_4_1
):
len
(
loss_4_1
)
+
84
]
loss_4_3
=
loss_4
[
len
(
loss_4_2
):
len
(
loss_4_2
)
+
84
]
loss_4_4
=
loss_4
[
len
(
loss_4_3
):
len
(
loss_4_3
)
+
84
]
loss_4_5
=
loss_4
[
len
(
loss_4_4
):
len
(
loss_4_4
)
+
84
]
mean_4_1
=
mean
(
loss_4_1
)
mean_4_2
=
mean
(
loss_4_2
)
mean_4_3
=
mean
(
loss_4_3
)
mean_4_4
=
mean
(
loss_4_4
)
mean_4_5
=
mean
(
loss_4_5
)
loss_5
=
loss_values
[
len
(
loss_4
):
len
(
loss_4
)
+
single_loss_batch
]
loss_5
=
[
eval
(
i
)
for
i
in
loss_5
]
loss_5_1
=
loss_2
[:
84
]
loss_5_2
=
loss_5
[
len
(
loss_5_1
):
len
(
loss_5_1
)
+
84
]
loss_5_3
=
loss_5
[
len
(
loss_5_2
):
len
(
loss_5_2
)
+
84
]
loss_5_4
=
loss_5
[
len
(
loss_5_3
):
len
(
loss_5_3
)
+
84
]
loss_5_5
=
loss_5
[
len
(
loss_5_4
):
len
(
loss_5_4
)
+
84
]
mean_5_1
=
mean
(
loss_5_1
)
mean_5_2
=
mean
(
loss_5_2
)
mean_5_3
=
mean
(
loss_5_3
)
mean_5_4
=
mean
(
loss_5_4
)
mean_5_5
=
mean
(
loss_5_5
)
plt
.
ylabel
(
"
Loss
"
)
plt
.
xlabel
(
"
Epochs
"
)
plt
.
title
(
"
Loss of Baselines
"
)
plt
.
plot
(
eps
,
[
mean_1_1
,
mean_1_2
,
mean_1_3
,
mean_1_4
,
mean_1_5
],
label
=
"
loss set 1
"
)
plt
.
plot
(
eps
,
[
mean_2_1
,
mean_2_2
,
mean_2_3
,
mean_2_4
,
mean_2_5
],
label
=
"
loss set 2
"
)
plt
.
plot
(
eps
,
[
mean_3_1
,
mean_3_2
,
mean_3_3
,
mean_3_4
,
mean_3_5
],
label
=
"
loss set 3
"
)
plt
.
plot
(
eps
,
[
mean_4_1
,
mean_4_2
,
mean_4_3
,
mean_4_4
,
mean_4_5
],
label
=
"
loss set 4
"
)
plt
.
plot
(
eps
,
[
mean_5_1
,
mean_5_2
,
mean_5_3
,
mean_5_4
,
mean_5_5
],
label
=
"
loss set 5
"
)
plt
.
legend
()
plt
.
show
()
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