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/
2015104192
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Authored by
윤영빈
2020-10-16 02:11:35 +0900
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Commit
e3b6e3b4947b3fe3c66c5fbbf6602dbfe1e26b99
e3b6e3b4
1 parent
e9ac668c
NetVLAD model test
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2 deletions
README.md
web/backend/yt8m/frame_level_models.py
web/backend/yt8m/video_level_models.py
README.md
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e3b6e3b
...
...
@@ -20,8 +20,6 @@
![
profit_hunter
](
/img/profit_hunter.png
)
*
팀명
**Profit Hunter**
*
윤영빈(컴퓨터공학과, 2015104192)
*
윤준현(컴퓨터공학과, 2015104193)
*
이현규(컴퓨터공학과, 2015104209)
*
이태현(컴퓨터공학과, 2015104208)
## Links
...
...
web/backend/yt8m/frame_level_models.py
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e3b6e3b
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web/backend/yt8m/video_level_models.py
View file @
e3b6e3b
...
...
@@ -25,6 +25,21 @@ FLAGS = flags.FLAGS
flags
.
DEFINE_integer
(
"moe_num_mixtures"
,
2
,
"The number of mixtures (excluding the dummy 'expert') used for MoeModel."
)
# flags.DEFINE_integer(
# "moe_num_mixtures", 2,
# "The number of mixtures (excluding the dummy 'expert') used for MoeModel.")
flags
.
DEFINE_float
(
"moe_l2"
,
1e-8
,
"L2 penalty for MoeModel."
)
flags
.
DEFINE_integer
(
"moe_low_rank_gating"
,
-
1
,
"Low rank gating for MoeModel."
)
flags
.
DEFINE_bool
(
"moe_prob_gating"
,
True
,
"Prob gating for MoeModel."
)
flags
.
DEFINE_string
(
"moe_prob_gating_input"
,
"prob"
,
"input Prob gating for MoeModel."
)
class
LogisticModel
(
models
.
BaseModel
):
...
...
@@ -111,3 +126,109 @@ class MoeModel(models.BaseModel):
final_probabilities
=
tf
.
reshape
(
final_probabilities_by_class_and_batch
,
[
-
1
,
vocab_size
])
return
{
"predictions"
:
final_probabilities
}
class
willow_MoeModel
(
models
.
BaseModel
):
"""A softmax over a mixture of logistic models (with L2 regularization)."""
def
create_model
(
self
,
model_input
,
vocab_size
,
is_training
,
num_mixtures
=
None
,
l2_penalty
=
1e-8
,
**
unused_params
):
"""Creates a Mixture of (Logistic) Experts model.
It also includes the possibility of gating the probabilities
The model consists of a per-class softmax distribution over a
configurable number of logistic classifiers. One of the classifiers in the
mixture is not trained, and always predicts 0.
Args:
model_input: 'batch_size' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
is_training: Is this the training phase ?
num_mixtures: The number of mixtures (excluding a dummy 'expert' that
always predicts the non-existence of an entity).
l2_penalty: How much to penalize the squared magnitudes of parameter
values.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes.
"""
num_mixtures
=
8
low_rank_gating
=
FLAGS
.
moe_low_rank_gating
l2_penalty
=
FLAGS
.
moe_l2
gating_probabilities
=
FLAGS
.
moe_prob_gating
gating_input
=
FLAGS
.
moe_prob_gating_input
input_size
=
model_input
.
get_shape
()
.
as_list
()[
1
]
remove_diag
=
False
if
low_rank_gating
==
-
1
:
gate_activations
=
slim
.
fully_connected
(
model_input
,
vocab_size
*
(
num_mixtures
+
1
),
activation_fn
=
None
,
biases_initializer
=
None
,
weights_regularizer
=
slim
.
l2_regularizer
(
l2_penalty
),
scope
=
"gates"
)
else
:
gate_activations1
=
slim
.
fully_connected
(
model_input
,
low_rank_gating
,
activation_fn
=
None
,
biases_initializer
=
None
,
weights_regularizer
=
slim
.
l2_regularizer
(
l2_penalty
),
scope
=
"gates1"
)
gate_activations
=
slim
.
fully_connected
(
gate_activations1
,
vocab_size
*
(
num_mixtures
+
1
),
activation_fn
=
None
,
biases_initializer
=
None
,
weights_regularizer
=
slim
.
l2_regularizer
(
l2_penalty
),
scope
=
"gates2"
)
expert_activations
=
slim
.
fully_connected
(
model_input
,
vocab_size
*
num_mixtures
,
activation_fn
=
None
,
weights_regularizer
=
slim
.
l2_regularizer
(
l2_penalty
),
scope
=
"experts"
)
gating_distribution
=
tf
.
nn
.
softmax
(
tf
.
reshape
(
gate_activations
,
[
-
1
,
num_mixtures
+
1
]))
# (Batch * #Labels) x (num_mixtures + 1)
expert_distribution
=
tf
.
nn
.
sigmoid
(
tf
.
reshape
(
expert_activations
,
[
-
1
,
num_mixtures
]))
# (Batch * #Labels) x num_mixtures
probabilities_by_class_and_batch
=
tf
.
reduce_sum
(
gating_distribution
[:,
:
num_mixtures
]
*
expert_distribution
,
1
)
probabilities
=
tf
.
reshape
(
probabilities_by_class_and_batch
,
[
-
1
,
vocab_size
])
if
gating_probabilities
:
if
gating_input
==
'prob'
:
gating_weights
=
tf
.
get_variable
(
"gating_prob_weights"
,
[
vocab_size
,
vocab_size
],
initializer
=
tf
.
random_normal_initializer
(
stddev
=
1
/
math
.
sqrt
(
vocab_size
)))
gates
=
tf
.
matmul
(
probabilities
,
gating_weights
)
else
:
gating_weights
=
tf
.
get_variable
(
"gating_prob_weights"
,
[
input_size
,
vocab_size
],
initializer
=
tf
.
random_normal_initializer
(
stddev
=
1
/
math
.
sqrt
(
vocab_size
)))
gates
=
tf
.
matmul
(
model_input
,
gating_weights
)
if
remove_diag
:
# removes diagonals coefficients
diagonals
=
tf
.
matrix_diag_part
(
gating_weights
)
gates
=
gates
-
tf
.
multiply
(
diagonals
,
probabilities
)
gates
=
slim
.
batch_norm
(
gates
,
center
=
True
,
scale
=
True
,
is_training
=
is_training
,
scope
=
"gating_prob_bn"
)
gates
=
tf
.
sigmoid
(
gates
)
probabilities
=
tf
.
multiply
(
probabilities
,
gates
)
return
{
"predictions"
:
probabilities
}
\ No newline at end of file
...
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