Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
bob.learn.tensorflow
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Build
Pipelines
Jobs
Pipeline schedules
Artifacts
Deploy
Releases
Model registry
Operate
Environments
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
This is an archived project. Repository and other project resources are read-only.
Show more breadcrumbs
bob
bob.learn.tensorflow
Commits
d1853331
Commit
d1853331
authored
7 years ago
by
Amir MOHAMMADI
Browse files
Options
Downloads
Patches
Plain Diff
improve the architecture definition
parent
a2006eaf
Branches
Branches containing commit
Tags
Tags containing commit
1 merge request
!47
Many changes
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
bob/learn/tensorflow/network/PatchCNN.py
+96
-136
96 additions, 136 deletions
bob/learn/tensorflow/network/PatchCNN.py
with
96 additions
and
136 deletions
bob/learn/tensorflow/network/PatchCNN.py
+
96
−
136
View file @
d1853331
...
...
@@ -44,170 +44,130 @@ from __future__ import print_function
import
tensorflow
as
tf
def
create_conv_layer
(
inputs
,
mode
,
data_format
,
endpoints
,
number
,
filters
,
kernel_size
,
pool_size
,
pool_strides
,
skip_pool
=
False
):
bn_axis
=
1
if
data_format
.
lower
()
==
'
channels_first
'
else
3
training
=
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
name
=
'
Conv-{}
'
.
format
(
number
)
conv
=
tf
.
layers
.
conv2d
(
inputs
=
inputs
,
filters
=
filters
,
kernel_size
=
kernel_size
,
padding
=
"
same
"
,
activation
=
None
,
data_format
=
data_format
,
name
=
name
)
endpoints
[
name
]
=
conv
name
=
'
BN-{}
'
.
format
(
number
)
bn
=
tf
.
layers
.
batch_normalization
(
conv
,
axis
=
bn_axis
,
training
=
training
,
fused
=
True
,
name
=
name
)
endpoints
[
name
]
=
bn
name
=
'
Activation-{}
'
.
format
(
number
)
bn_act
=
tf
.
nn
.
relu
(
bn
,
name
=
name
)
endpoints
[
name
]
=
bn_act
name
=
'
MaxPooling-{}
'
.
format
(
number
)
if
skip_pool
:
pool
=
bn_act
else
:
pool
=
tf
.
layers
.
max_pooling2d
(
inputs
=
bn_act
,
pool_size
=
pool_size
,
strides
=
pool_strides
,
data_format
=
data_format
,
name
=
name
)
endpoints
[
name
]
=
pool
return
pool
def
create_dense_layer
(
inputs
,
mode
,
endpoints
,
number
,
units
):
training
=
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
name
=
'
FC-{}
'
.
format
(
number
)
fc
=
tf
.
layers
.
dense
(
inputs
=
inputs
,
units
=
units
,
activation
=
None
,
name
=
name
)
endpoints
[
name
]
=
fc
name
=
'
BN-{}
'
.
format
(
number
+
5
)
bn
=
tf
.
layers
.
batch_normalization
(
fc
,
axis
=
1
,
training
=
training
,
fused
=
True
,
name
=
name
)
endpoints
[
name
]
=
bn
name
=
'
Activation-{}
'
.
format
(
number
+
5
)
bn_act
=
tf
.
nn
.
relu
(
bn
,
name
=
name
)
endpoints
[
name
]
=
bn_act
return
bn_act
def
base_architecture
(
input_layer
,
mode
,
data_format
,
skip_first_two_pool
=
False
,
**
kwargs
):
training
=
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
# Keep track of all the endpoints
endpoints
=
{}
bn_axis
=
1
if
data_format
.
lower
()
==
'
channels_first
'
else
3
training
=
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
# ======================
# Convolutional Layer #1
conv1
=
tf
.
layers
.
conv2d
(
inputs
=
input_layer
,
filters
=
50
,
kernel_size
=
(
5
,
5
),
padding
=
"
same
"
,
activation
=
None
,
data_format
=
data_format
)
endpoints
[
'
Conv-1
'
]
=
conv1
# Batch Normalization #1
bn1
=
tf
.
layers
.
batch_normalization
(
conv1
,
axis
=
bn_axis
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-1
'
]
=
bn1
bn1_act
=
tf
.
nn
.
relu
(
bn1
)
endpoints
[
'
BN-1-activation
'
]
=
bn1_act
# Pooling Layer #1
if
skip_first_two_pool
:
pool1
=
bn1_act
else
:
pool1
=
tf
.
layers
.
max_pooling2d
(
inputs
=
bn1_act
,
pool_size
=
[
2
,
2
],
strides
=
2
,
data_format
=
data_format
)
endpoints
[
'
MaxPooling-1
'
]
=
pool1
pool1
=
create_conv_layer
(
inputs
=
input_layer
,
mode
=
mode
,
data_format
=
data_format
,
endpoints
=
endpoints
,
number
=
1
,
filters
=
50
,
kernel_size
=
(
5
,
5
),
pool_size
=
(
2
,
2
),
pool_strides
=
2
,
skip_pool
=
skip_first_two_pool
)
# ======================
# Convolutional Layer #2
conv2
=
tf
.
layers
.
conv2d
(
inputs
=
pool1
,
filters
=
100
,
kernel_size
=
(
3
,
3
),
padding
=
"
same
"
,
activation
=
None
,
data_format
=
data_format
)
endpoints
[
'
Conv-2
'
]
=
conv2
# Batch Normalization #2
bn2
=
tf
.
layers
.
batch_normalization
(
conv2
,
axis
=
bn_axis
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-2
'
]
=
bn2
bn2_act
=
tf
.
nn
.
relu
(
bn2
)
endpoints
[
'
BN-2-activation
'
]
=
bn2_act
# Pooling Layer #2
if
skip_first_two_pool
:
pool2
=
bn2_act
else
:
pool2
=
tf
.
layers
.
max_pooling2d
(
inputs
=
bn2_act
,
pool_size
=
[
2
,
2
],
strides
=
2
,
data_format
=
data_format
)
endpoints
[
'
MaxPooling-2
'
]
=
pool2
pool2
=
create_conv_layer
(
inputs
=
pool1
,
mode
=
mode
,
data_format
=
data_format
,
endpoints
=
endpoints
,
number
=
2
,
filters
=
100
,
kernel_size
=
(
3
,
3
),
pool_size
=
(
2
,
2
),
pool_strides
=
2
,
skip_pool
=
skip_first_two_pool
)
# ======================
# Convolutional Layer #3
conv3
=
tf
.
layers
.
conv2d
(
inputs
=
pool2
,
filters
=
150
,
kernel_size
=
(
3
,
3
),
padding
=
"
same
"
,
activation
=
None
,
data_format
=
data_format
)
endpoints
[
'
Conv-3
'
]
=
conv3
# Batch Normalization #3
bn3
=
tf
.
layers
.
batch_normalization
(
conv3
,
axis
=
bn_axis
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-3
'
]
=
bn3
bn3_act
=
tf
.
nn
.
relu
(
bn3
)
endpoints
[
'
BN-3-activation
'
]
=
bn3_act
# Pooling Layer #3
pool3
=
tf
.
layers
.
max_pooling2d
(
inputs
=
bn3_act
,
pool_size
=
[
3
,
3
],
strides
=
2
,
data_format
=
data_format
)
endpoints
[
'
MaxPooling-3
'
]
=
pool3
pool3
=
create_conv_layer
(
inputs
=
pool2
,
mode
=
mode
,
data_format
=
data_format
,
endpoints
=
endpoints
,
number
=
3
,
filters
=
150
,
kernel_size
=
(
3
,
3
),
pool_size
=
(
3
,
3
),
pool_strides
=
2
)
# ======================
# Convolutional Layer #4
conv4
=
tf
.
layers
.
conv2d
(
inputs
=
pool3
,
filters
=
200
,
kernel_size
=
(
3
,
3
),
padding
=
"
same
"
,
activation
=
None
,
data_format
=
data_format
)
endpoints
[
'
Conv-4
'
]
=
conv4
# Batch Normalization #4
bn4
=
tf
.
layers
.
batch_normalization
(
conv4
,
axis
=
bn_axis
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-4
'
]
=
bn4
bn4_act
=
tf
.
nn
.
relu
(
bn4
)
endpoints
[
'
BN-4-activation
'
]
=
bn4_act
# Pooling Layer #4
pool4
=
tf
.
layers
.
max_pooling2d
(
inputs
=
bn4_act
,
pool_size
=
[
2
,
2
],
strides
=
2
,
data_format
=
data_format
)
endpoints
[
'
MaxPooling-4
'
]
=
pool4
pool4
=
create_conv_layer
(
inputs
=
pool3
,
mode
=
mode
,
data_format
=
data_format
,
endpoints
=
endpoints
,
number
=
4
,
filters
=
200
,
kernel_size
=
(
3
,
3
),
pool_size
=
(
2
,
2
),
pool_strides
=
2
)
# ======================
# Convolutional Layer #5
conv5
=
tf
.
layers
.
conv2d
(
inputs
=
pool4
,
filters
=
250
,
kernel_size
=
(
3
,
3
),
padding
=
"
same
"
,
activation
=
None
,
data_format
=
data_format
)
endpoints
[
'
Conv-5
'
]
=
conv5
# Batch Normalization #5
bn5
=
tf
.
layers
.
batch_normalization
(
conv5
,
axis
=
bn_axis
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-5
'
]
=
bn5
bn5_act
=
tf
.
nn
.
relu
(
bn5
)
endpoints
[
'
BN-5-activation
'
]
=
bn5_act
# Pooling Layer #5
pool5
=
tf
.
layers
.
max_pooling2d
(
inputs
=
bn5_act
,
pool_size
=
[
2
,
2
],
strides
=
2
,
data_format
=
data_format
)
endpoints
[
'
MaxPooling-5
'
]
=
pool5
pool5
=
create_conv_layer
(
inputs
=
pool4
,
mode
=
mode
,
data_format
=
data_format
,
endpoints
=
endpoints
,
number
=
5
,
filters
=
250
,
kernel_size
=
(
3
,
3
),
pool_size
=
(
2
,
2
),
pool_strides
=
2
)
# ========================
# Flatten tensor into a batch of vectors
pool5_flat
=
tf
.
layers
.
flatten
(
pool5
)
endpoints
[
'
MaxPooling-5-Flat
'
]
=
pool5_flat
name
=
'
MaxPooling-5-Flat
'
pool5_flat
=
tf
.
layers
.
flatten
(
pool5
,
name
=
name
)
endpoints
[
name
]
=
pool5_flat
# ========================
# Fully Connected Layer #1
fc_1
=
tf
.
layers
.
dense
(
inputs
=
pool5_flat
,
units
=
1000
,
activation
=
None
)
endpoints
[
'
FC-1
'
]
=
fc_1
# Batch Normalization #6
bn6
=
tf
.
layers
.
batch_normalization
(
fc_1
,
axis
=
1
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-6
'
]
=
bn6
bn6_act
=
tf
.
nn
.
relu
(
bn6
)
endpoints
[
'
BN-6-activation
'
]
=
bn6_act
fc1
=
create_dense_layer
(
inputs
=
pool5_flat
,
mode
=
mode
,
endpoints
=
endpoints
,
number
=
1
,
units
=
1000
)
# ========================
# Dropout
dropout
=
tf
.
layers
.
dropout
(
inputs
=
bn6_act
,
rate
=
0.5
,
training
=
training
)
endpoints
[
'
dropout
'
]
=
dropout
name
=
'
dropout
'
dropout
=
tf
.
layers
.
dropout
(
inputs
=
fc1
,
rate
=
0.5
,
training
=
training
,
name
=
name
)
endpoints
[
name
]
=
dropout
# ========================
# Fully Connected Layer #2
fc_2
=
tf
.
layers
.
dense
(
inputs
=
dropout
,
units
=
400
,
activation
=
None
)
endpoints
[
'
FC-2
'
]
=
fc_2
# Batch Normalization #7
bn7
=
tf
.
layers
.
batch_normalization
(
fc_2
,
axis
=
1
,
training
=
training
,
fused
=
True
)
endpoints
[
'
BN-7
'
]
=
bn7
bn7_act
=
tf
.
nn
.
relu
(
bn7
)
endpoints
[
'
BN-7-activation
'
]
=
bn7_act
fc2
=
create_dense_layer
(
inputs
=
dropout
,
mode
=
mode
,
endpoints
=
endpoints
,
number
=
2
,
units
=
400
)
return
bn7_act
,
endpoints
return
fc2
,
endpoints
def
architecture
(
input_layer
,
...
...
@@ -222,11 +182,11 @@ def architecture(input_layer,
with
tf
.
variable_scope
(
'
PatchCNN
'
,
reuse
=
reuse
,
regularizer
=
regularizer
):
bn7_act
,
endpoints
=
base_architecture
(
fc2
,
endpoints
=
base_architecture
(
input_layer
=
input_layer
,
mode
=
mode
,
data_format
=
data_format
,
skip_first_two_pool
=
skip_first_two_pool
)
# Logits layer
logits
=
tf
.
layers
.
dense
(
inputs
=
bn7_act
,
units
=
n_classes
)
logits
=
tf
.
layers
.
dense
(
inputs
=
fc2
,
units
=
n_classes
)
endpoints
[
'
FC-3
'
]
=
logits
endpoints
[
'
logits
'
]
=
logits
...
...
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment