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bob
bob.learn.tensorflow
Commits
cb9744bc
Commit
cb9744bc
authored
Feb 07, 2020
by
Amir MOHAMMADI
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new keras models
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bob/learn/tensorflow/models/autoencoder_face.py
bob/learn/tensorflow/models/autoencoder_face.py
+99
-0
bob/learn/tensorflow/models/autoencoder_yz.py
bob/learn/tensorflow/models/autoencoder_yz.py
+305
-0
bob/learn/tensorflow/models/mlp.py
bob/learn/tensorflow/models/mlp.py
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bob/learn/tensorflow/models/autoencoder_face.py
0 → 100644
View file @
cb9744bc
import
tensorflow
as
tf
from
.densenet
import
densenet161
def
_get_l2_kw
(
weight_decay
):
l2_kw
=
{}
if
weight_decay
is
not
None
:
l2_kw
=
{
"kernel_regularizer"
:
tf
.
keras
.
regularizers
.
l2
(
weight_decay
)}
return
l2_kw
class
ConvDecoder
(
tf
.
keras
.
Sequential
):
"""The decoder similar to the one in
https://github.com/google/compare_gan/blob/master/compare_gan/architectures/sndcgan.py
"""
def
__init__
(
self
,
z_dim
,
decoder_layers
=
(
(
512
,
7
,
7
,
0
),
(
256
,
4
,
2
,
1
),
(
128
,
4
,
2
,
1
),
(
64
,
4
,
2
,
1
),
(
32
,
4
,
2
,
1
),
(
16
,
4
,
2
,
1
),
(
3
,
1
,
1
,
0
),
),
weight_decay
=
1e-5
,
name
=
"Decoder"
,
**
kwargs
,
):
self
.
z_dim
=
z_dim
self
.
data_format
=
data_format
=
"channels_last"
l2_kw
=
_get_l2_kw
(
weight_decay
)
layers
=
[
tf
.
keras
.
layers
.
Reshape
((
1
,
1
,
z_dim
),
input_shape
=
(
z_dim
,),
name
=
"reshape"
)
]
for
i
,
(
filters
,
kernel_size
,
strides
,
cropping
)
in
enumerate
(
decoder_layers
):
dconv
=
tf
.
keras
.
layers
.
Conv2DTranspose
(
filters
,
kernel_size
,
strides
=
strides
,
use_bias
=
i
==
len
(
decoder_layers
)
-
1
,
data_format
=
data_format
,
name
=
f"dconv_
{
i
}
"
,
**
l2_kw
,
)
crop
=
tf
.
keras
.
layers
.
Cropping2D
(
cropping
=
cropping
,
data_format
=
data_format
,
name
=
f"crop_
{
i
}
"
)
if
i
==
len
(
decoder_layers
)
-
1
:
act
=
tf
.
keras
.
layers
.
Activation
(
"tanh"
,
name
=
f"tanh_
{
i
}
"
)
bn
=
None
else
:
act
=
tf
.
keras
.
layers
.
Activation
(
"relu"
,
name
=
f"relu_
{
i
}
"
)
bn
=
tf
.
keras
.
layers
.
BatchNormalization
(
scale
=
False
,
fused
=
False
,
name
=
f"bn_
{
i
}
"
)
if
bn
is
not
None
:
layers
.
extend
([
dconv
,
crop
,
bn
,
act
])
else
:
layers
.
extend
([
dconv
,
crop
,
act
])
with
tf
.
name_scope
(
name
):
super
().
__init__
(
layers
=
layers
,
name
=
name
,
**
kwargs
)
class
Autoencoder
(
tf
.
keras
.
Model
):
"""
A class defining a simple convolutional autoencoder.
Attributes
----------
data_format : str
channels_last is only supported
decoder : object
The encoder part
encoder : object
The decoder part
"""
def
__init__
(
self
,
encoder
,
decoder
,
name
=
"Autoencoder"
,
**
kwargs
):
super
().
__init__
(
name
=
name
,
**
kwargs
)
self
.
encoder
=
encoder
self
.
decoder
=
decoder
def
call
(
self
,
x
,
training
=
None
):
z
=
self
.
encoder
(
x
,
training
=
training
)
x_hat
=
self
.
decoder
(
z
,
training
=
training
)
return
z
,
x_hat
def
autoencoder_face
(
z_dim
=
256
,
weight_decay
=
1e-9
):
encoder
=
densenet161
(
output_classes
=
z_dim
,
weight_decay
=
weight_decay
,
weights
=
None
,
name
=
"DenseNet"
)
decoder
=
ConvDecoder
(
z_dim
=
z_dim
,
weight_decay
=
weight_decay
,
name
=
"Decoder"
)
autoencoder
=
Autoencoder
(
encoder
,
decoder
,
name
=
"Autoencoder"
)
return
autoencoder
bob/learn/tensorflow/models/autoencoder_yz.py
0 → 100644
View file @
cb9744bc
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bob/learn/tensorflow/models/mlp.py
0 → 100644
View file @
cb9744bc
import
tensorflow
as
tf
class
MLP
(
tf
.
keras
.
Model
):
"""An MLP that can be trained with center loss and cross entropy."""
def
__init__
(
self
,
n_classes
=
1
,
hidden_layers
=
(
256
,
128
,
64
,
32
),
weight_decay
=
1e-5
,
name
=
"MLP"
,
**
kwargs
,
):
super
().
__init__
(
name
=
name
,
**
kwargs
)
dense_kw
=
{}
if
weight_decay
is
not
None
:
dense_kw
[
"kernel_regularizer"
]
=
tf
.
keras
.
regularizers
.
l2
(
weight_decay
)
sequential_layers
=
[]
for
i
,
n
in
enumerate
(
hidden_layers
,
start
=
1
):
sequential_layers
.
extend
(
[
tf
.
keras
.
layers
.
Dense
(
n
,
use_bias
=
False
,
name
=
f"dense_
{
i
}
"
,
**
dense_kw
),
tf
.
keras
.
layers
.
BatchNormalization
(
scale
=
False
,
name
=
f"bn_
{
i
}
"
),
tf
.
keras
.
layers
.
Activation
(
"relu"
,
name
=
f"relu_
{
i
}
"
),
]
)
sequential_layers
.
append
(
tf
.
keras
.
layers
.
Dense
(
n_classes
,
name
=
"logits"
,
**
dense_kw
)
)
self
.
hidden_layers
=
hidden_layers
self
.
n_classes
=
n_classes
self
.
sequential_layers
=
sequential_layers
self
.
prelogits_shape
=
hidden_layers
[
-
1
]
def
call
(
self
,
x
,
training
=
None
):
assert
hasattr
(
x
,
"_keras_history"
),
"The input must be wrapped inside a keras Input layer."
for
i
,
layer
in
enumerate
(
self
.
sequential_layers
):
try
:
x
=
layer
(
x
,
training
=
training
)
except
TypeError
:
x
=
layer
(
x
)
return
x
@
property
def
prelogits
(
self
):
return
self
.
layers
[
-
2
].
output
class
MLPDropout
(
tf
.
keras
.
Model
):
"""An MLP that can be trained with center loss and cross entropy."""
def
__init__
(
self
,
n_classes
=
1
,
hidden_layers
=
(
256
,
128
,
64
,
32
),
weight_decay
=
1e-5
,
drop_rate
=
0.5
,
name
=
"MLP"
,
**
kwargs
,
):
super
().
__init__
(
name
=
name
,
**
kwargs
)
dense_kw
=
{}
if
weight_decay
is
not
None
:
dense_kw
[
"kernel_regularizer"
]
=
tf
.
keras
.
regularizers
.
l2
(
weight_decay
)
sequential_layers
=
[]
for
i
,
n
in
enumerate
(
hidden_layers
,
start
=
1
):
sequential_layers
.
extend
(
[
tf
.
keras
.
layers
.
Dense
(
n
,
use_bias
=
False
,
name
=
f"dense_
{
i
}
"
,
**
dense_kw
),
tf
.
keras
.
layers
.
Activation
(
"relu"
,
name
=
f"relu_
{
i
}
"
),
tf
.
keras
.
layers
.
Dropout
(
rate
=
drop_rate
,
name
=
f"drop_
{
i
}
"
),
]
)
sequential_layers
.
append
(
tf
.
keras
.
layers
.
Dense
(
n_classes
,
name
=
"logits"
,
**
dense_kw
)
)
self
.
hidden_layers
=
hidden_layers
self
.
n_classes
=
n_classes
self
.
drop_rate
=
drop_rate
self
.
sequential_layers
=
sequential_layers
self
.
prelogits_shape
=
hidden_layers
[
-
1
]
def
call
(
self
,
x
,
training
=
None
):
assert
hasattr
(
x
,
"_keras_history"
),
"The input must be wrapped inside a keras Input layer."
for
i
,
layer
in
enumerate
(
self
.
sequential_layers
):
try
:
x
=
layer
(
x
,
training
=
training
)
except
TypeError
:
x
=
layer
(
x
)
return
x
@
property
def
prelogits
(
self
):
return
self
.
layers
[
-
2
].
output
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