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bob
bob.learn.tensorflow
Commits
9da6ef05
Commit
9da6ef05
authored
Oct 11, 2016
by
Tiago de Freitas Pereira
Browse files
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Organizing the trainer
parent
2efd0e68
Changes
7
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7 changed files
with
370 additions
and
185 deletions
+370
-185
bob/learn/tensorflow/analyzers/SoftmaxAnalizer.py
bob/learn/tensorflow/analyzers/SoftmaxAnalizer.py
+51
-5
bob/learn/tensorflow/data/BaseDataShuffler.py
bob/learn/tensorflow/data/BaseDataShuffler.py
+37
-0
bob/learn/tensorflow/script/train_mnist.py
bob/learn/tensorflow/script/train_mnist.py
+2
-2
bob/learn/tensorflow/script/train_mnist_siamese.py
bob/learn/tensorflow/script/train_mnist_siamese.py
+10
-8
bob/learn/tensorflow/trainers/SiameseTrainer.py
bob/learn/tensorflow/trainers/SiameseTrainer.py
+189
-134
bob/learn/tensorflow/trainers/Trainer.py
bob/learn/tensorflow/trainers/Trainer.py
+80
-36
setup.py
setup.py
+1
-0
No files found.
bob/learn/tensorflow/analyzers/SoftmaxAnalizer.py
View file @
9da6ef05
...
...
@@ -31,24 +31,70 @@ class SoftmaxAnalizer(object):
"""
self
.
data_shuffler
=
None
self
.
machine
=
None
self
.
trainer
=
None
self
.
session
=
None
def
__call__
(
self
,
data_shuffler
,
machine
,
session
):
def
__call__
(
self
,
data_shuffler
,
trainer
,
session
):
if
self
.
data_shuffler
is
None
:
self
.
data_shuffler
=
data_shuffler
self
.
machine
=
machine
self
.
trainer
=
trainer
self
.
session
=
session
# Creating the graph
feature_batch
,
label_batch
=
self
.
data_shuffler
.
get_placeholders
(
name
=
"validation_accuracy"
)
data
,
labels
=
self
.
data_shuffler
.
get_batch
()
graph
=
self
.
machine
.
compute_graph
(
feature_batch
)
graph
=
self
.
trainer
.
architecture
.
compute_graph
(
feature_batch
)
predictions
=
numpy
.
argmax
(
self
.
session
.
run
(
graph
,
feed_dict
=
{
feature_batch
:
data
[:]}),
1
)
accuracy
=
100.
*
numpy
.
sum
(
predictions
==
labels
)
/
predictions
.
shape
[
0
]
summaries
=
[]
summaries
.
append
(
summary_pb2
.
Summary
.
Value
(
tag
=
"accuracy_validation"
,
simple_value
=
float
(
accuracy
)))
return
summary_pb2
.
Summary
(
value
=
summaries
)
class
SoftmaxSiameseAnalizer
(
object
):
"""
Analizer.
"""
def
__init__
(
self
):
"""
Softmax analizer
** Parameters **
data_shuffler:
graph:
session:
convergence_threshold:
convergence_reference: References to analize the convergence. Possible values are `eer`, `far10`, `far10`
"""
self
.
data_shuffler
=
None
self
.
trainer
=
None
self
.
session
=
None
def
__call__
(
self
,
data_shuffler
,
machine
,
session
):
if
self
.
data_shuffler
is
None
:
self
.
data_shuffler
=
data_shuffler
self
.
trainer
=
trainer
self
.
session
=
session
# Creating the graph
#feature_batch, label_batch = self.data_shuffler.get_placeholders(name="validation_accuracy")
feature_left_batch
,
feature_right_batch
label_batch
=
self
.
data_shuffler
.
get_placeholders_pair
(
name
=
"validation_accuracy"
)
batch_left
,
batch_right
,
labels
=
self
.
data_shuffler
.
get_batch
()
left
=
self
.
machine
.
compute_graph
(
feature_batch
)
predictions
=
numpy
.
argmax
(
self
.
session
.
run
(
graph
,
feed_dict
=
{
feature_batch
:
data
[:]}),
1
)
accuracy
=
100.
*
numpy
.
sum
(
predictions
==
labels
)
/
predictions
.
shape
[
0
]
summaries
=
[]
summaries
.
append
(
summary_pb2
.
Summary
.
Value
(
tag
=
"accuracy_validation"
,
simple_value
=
float
(
accuracy
)))
return
summary_pb2
.
Summary
(
value
=
summaries
)
\ No newline at end of file
return
summary_pb2
.
Summary
(
value
=
summaries
)
bob/learn/tensorflow/data/BaseDataShuffler.py
View file @
9da6ef05
...
...
@@ -47,7 +47,10 @@ class BaseDataShuffler(object):
self
.
indexes
=
numpy
.
array
(
range
(
self
.
n_samples
))
numpy
.
random
.
shuffle
(
self
.
indexes
)
# TODO: Reorganize the datas hufflers for siamese and triplets
self
.
data_placeholder
=
None
self
.
data2_placeholder
=
None
self
.
data3_placeholder
=
None
self
.
label_placeholder
=
None
def
get_placeholders_forprefetch
(
self
,
name
=
""
):
...
...
@@ -59,6 +62,21 @@ class BaseDataShuffler(object):
self
.
label_placeholder
=
tf
.
placeholder
(
tf
.
int64
,
shape
=
[
None
,
])
return
self
.
data_placeholder
,
self
.
label_placeholder
def
get_placeholders_pair_forprefetch
(
self
,
name
=
""
):
"""
Returns a place holder with the size of your batch
"""
if
self
.
data_placeholder
is
None
:
self
.
data_placeholder
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
tuple
([
None
]
+
list
(
self
.
shape
[
1
:])),
name
=
name
)
if
self
.
data2_placeholder
is
None
:
self
.
data2_placeholder
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
tuple
([
None
]
+
list
(
self
.
shape
[
1
:])),
name
=
name
)
if
self
.
label_placeholder
:
self
.
label_placeholder
=
tf
.
placeholder
(
tf
.
int64
,
shape
=
[
None
,
])
return
self
.
data_placeholder
,
self
.
data2_placeholder
,
self
.
label_placeholder
def
get_placeholders
(
self
,
name
=
""
):
"""
Returns a place holder with the size of your batch
...
...
@@ -66,9 +84,28 @@ class BaseDataShuffler(object):
if
self
.
data_placeholder
is
None
:
self
.
data_placeholder
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
self
.
shape
,
name
=
name
)
if
self
.
label_placeholder
is
None
:
self
.
label_placeholder
=
tf
.
placeholder
(
tf
.
int64
,
shape
=
self
.
shape
[
0
])
return
self
.
data_placeholder
,
self
.
label_placeholder
def
get_placeholders_pair
(
self
,
name
=
""
):
"""
Returns a place holder with the size of your batch
"""
if
self
.
data_placeholder
is
None
:
self
.
data_placeholder
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
self
.
shape
,
name
=
name
+
"_right"
)
if
self
.
data2_placeholder
is
None
:
self
.
data2_placeholder
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
self
.
shape
,
name
=
name
+
"_left"
)
if
self
.
label_placeholder
is
None
:
self
.
label_placeholder
=
tf
.
placeholder
(
tf
.
int64
,
shape
=
self
.
shape
[
0
],
name
=
"label"
)
return
self
.
data_placeholder
,
self
.
data2_placeholder
,
self
.
label_placeholder
def
get_genuine_or_not
(
self
,
input_data
,
input_labels
,
genuine
=
True
):
if
genuine
:
...
...
bob/learn/tensorflow/script/train_mnist.py
View file @
9da6ef05
...
...
@@ -87,13 +87,13 @@ def main():
batch_size
=
VALIDATION_BATCH_SIZE
)
# Preparing the architecture
cnn
=
Fals
e
cnn
=
Tru
e
if
cnn
:
architecture
=
Chopra
(
seed
=
SEED
)
#architecture = Lenet(seed=SEED)
#architecture = Dummy(seed=SEED)
loss
=
BaseLoss
(
tf
.
nn
.
sparse_softmax_cross_entropy_with_logits
,
tf
.
reduce_mean
)
trainer
=
Trainer
(
architecture
=
architecture
,
loss
=
loss
,
iterations
=
ITERATIONS
,
prefetch
=
False
,
temp_dir
=
"cnn"
)
trainer
=
Trainer
(
architecture
=
architecture
,
loss
=
loss
,
iterations
=
ITERATIONS
,
prefetch
=
False
,
temp_dir
=
"
./LOGS/
cnn"
)
trainer
.
train
(
train_data_shuffler
,
validation_data_shuffler
)
#trainer.train(train_data_shuffler)
else
:
...
...
bob/learn/tensorflow/script/train_mnist_siamese.py
View file @
9da6ef05
...
...
@@ -112,28 +112,29 @@ def main():
# batch_size=VALIDATION_BATCH_SIZE)
# Preparing the architecture
#
n_classes = len(train_data_shuffler.possible_labels)
n_classes
=
50
n_classes
=
len
(
train_data_shuffler
.
possible_labels
)
#
n_classes = 50
cnn
=
True
if
cnn
:
# LENET PAPER CHOPRA
architecture
=
Chopra
(
seed
=
SEED
)
architecture
=
Chopra
(
seed
=
SEED
,
fc1_output
=
n_classes
)
#architecture = Lenet(default_feature_layer="fc2", n_classes=n_classes, conv1_output=8, conv2_output=16,use_gpu=USE_GPU)
#architecture = VGG(n_classes=n_classes, use_gpu=USE_GPU)
#architecture = Dummy(seed=SEED)
#architecture = LenetDropout(default_feature_layer="fc2", n_classes=n_classes, conv1_output=4, conv2_output=8, use_gpu=USE_GPU)
loss
=
ContrastiveLoss
(
contrastive_margin
=
3
.
)
optimizer
=
tf
.
train
.
GradientDescentOptimizer
(
0.00001
)
loss
=
ContrastiveLoss
(
contrastive_margin
=
4
.
)
optimizer
=
tf
.
train
.
GradientDescentOptimizer
(
0.0000
0
1
)
trainer
=
SiameseTrainer
(
architecture
=
architecture
,
loss
=
loss
,
iterations
=
ITERATIONS
,
snapshot
=
VALIDATION_TEST
,
optimizer
=
optimizer
)
optimizer
=
optimizer
,
temp_dir
=
"./LOGS/siamese-cnn"
)
#import ipdb; ipdb.set_trace();
trainer
.
train
(
train_data_shuffler
,
validation_data_shuffler
)
#trainer.train(train_data_shuffler)
else
:
...
...
@@ -143,5 +144,6 @@ def main():
trainer
=
SiameseTrainer
(
architecture
=
mlp
,
loss
=
loss
,
iterations
=
ITERATIONS
,
snapshot
=
VALIDATION_TEST
)
snapshot
=
VALIDATION_TEST
,
temp_dir
=
"./LOGS/siamese-dnn"
)
trainer
.
train
(
train_data_shuffler
,
validation_data_shuffler
)
bob/learn/tensorflow/trainers/SiameseTrainer.py
View file @
9da6ef05
This diff is collapsed.
Click to expand it.
bob/learn/tensorflow/trainers/Trainer.py
View file @
9da6ef05
...
...
@@ -4,21 +4,42 @@
# @date: Tue 09 Aug 2016 15:25:22 CEST
import
logging
logger
=
logging
.
getLogger
(
"bob.learn.tensorflow"
)
import
tensorflow
as
tf
from
..network
import
SequenceNetwork
import
threading
import
numpy
import
os
import
bob.io.base
import
bob.core
from
..analyzers
import
SoftmaxAnalizer
from
tensorflow.core.framework
import
summary_pb2
import
time
logger
=
bob
.
core
.
log
.
setup
(
"bob.learn.tensorflow"
)
class
Trainer
(
object
):
class
Trainer
(
object
):
"""
One graph trainer.
Use this trainer when your CNN is composed by one graph
**Parameters**
architecture: The architecture that you want to run. Should be a :py:class`bob.learn.tensorflow.network.SequenceNetwork`
optimizer: One of the tensorflow optimizers https://www.tensorflow.org/versions/r0.10/api_docs/python/train.html
use_gpu: Use GPUs in the training
loss: Loss
temp_dir: The output directory
base_learning_rate: Initial learning rate
weight_decay:
convergence_threshold:
iterations: Maximum number of iterations
snapshot: Will take a snapshot of the network at every `n` iterations
prefetch: Use extra Threads to deal with the I/O
analizer: Neural network analizer :py:mod:`bob.learn.tensorflow.analyzers`
verbosity_level:
"""
def
__init__
(
self
,
architecture
,
optimizer
=
tf
.
train
.
AdamOptimizer
(),
...
...
@@ -37,22 +58,10 @@ class Trainer(object):
prefetch
=
False
,
## Analizer
analizer
=
SoftmaxAnalizer
(),
analizer
=
SoftmaxAnalizer
(),
verbosity_level
=
2
):
"""
**Parameters**
architecture: The architecture that you want to run. Should be a :py:class`bob.learn.tensorflow.network.SequenceNetwork`
optimizer: One of the tensorflow optimizers https://www.tensorflow.org/versions/r0.10/api_docs/python/train.html
use_gpu: Use GPUs in the training
loss: Loss
temp_dir:
iterations:
snapshot:
convergence_threshold:
"""
if
not
isinstance
(
architecture
,
SequenceNetwork
):
raise
ValueError
(
"`architecture` should be instance of `SequenceNetwork`"
)
...
...
@@ -78,6 +87,7 @@ class Trainer(object):
self
.
train_data_shuffler
=
None
self
.
summaries_train
=
None
self
.
train_summary_writter
=
None
self
.
thread_pool
=
None
# Validation data
self
.
validation_graph
=
None
...
...
@@ -91,24 +101,22 @@ class Trainer(object):
bob
.
core
.
log
.
set_verbosity_level
(
logger
,
verbosity_level
)
def
compute_graph
(
self
,
data_shuffler
,
name
=
""
):
def
compute_graph
(
self
,
data_shuffler
,
prefetch
=
False
,
name
=
""
):
"""
Computes the graph for the trainer.
** Parameters **
data_shuffler: Data shuffler
prefetch:
name: Name of the graph
"""
# Defining place holders
if
self
.
prefetch
:
if
prefetch
:
placeholder_data
,
placeholder_labels
=
data_shuffler
.
get_placeholders_forprefetch
(
name
=
name
)
#if validation_data_shuffler is not None:
# validation_placeholder_data, validation_placeholder_labels = \
# validation_data_shuffler.get_placeholders(name="validation")
# Defining a placeholder queue for prefetching
queue
=
tf
.
FIFOQueue
(
capacity
=
10
,
dtypes
=
[
tf
.
float32
,
tf
.
int64
],
...
...
@@ -133,10 +141,9 @@ class Trainer(object):
def
get_feed_dict
(
self
,
data_shuffler
):
"""
Computes the feed_dict for
the graph
Given a data shuffler prepared the dictionary to be injected in
the graph
** Parameters **
data_shuffler:
"""
...
...
@@ -147,7 +154,16 @@ class Trainer(object):
label_placeholder
:
labels
}
return
feed_dict
def
__fit
(
self
,
session
,
step
):
def
fit
(
self
,
session
,
step
):
"""
Run one iteration (`forward` and `backward`)
** Parameters **
session: Tensorflow session
step: Iteration number
"""
if
self
.
prefetch
:
_
,
l
,
lr
,
summary
=
session
.
run
([
self
.
optimizer
,
self
.
training_graph
,
self
.
learning_rate
,
self
.
summaries_train
])
...
...
@@ -159,7 +175,16 @@ class Trainer(object):
logger
.
info
(
"Loss training set step={0} = {1}"
.
format
(
step
,
l
))
self
.
train_summary_writter
.
add_summary
(
summary
,
step
)
def
__compute_validation
(
self
,
session
,
data_shuffler
,
step
):
def
compute_validation
(
self
,
session
,
data_shuffler
,
step
):
"""
Computes the loss in the validation set
** Parameters **
session: Tensorflow session
data_shuffler: The data shuffler to be used
step: Iteration number
"""
if
self
.
validation_summary_writter
is
None
:
self
.
validation_summary_writter
=
tf
.
train
.
SummaryWriter
(
os
.
path
.
join
(
self
.
temp_dir
,
'validation'
),
session
.
graph
)
...
...
@@ -173,16 +198,27 @@ class Trainer(object):
self
.
validation_summary_writter
.
add_summary
(
summary_pb2
.
Summary
(
value
=
summaries
),
step
)
logger
.
info
(
"Loss VALIDATION set step={0} = {1}"
.
format
(
step
,
l
))
def
__create_general_summary
(
self
):
def
create_general_summary
(
self
):
"""
Creates a simple tensorboard summary with the value of the loss and learning rate
"""
# Train summary
tf
.
scalar_summary
(
'loss'
,
self
.
training_graph
,
name
=
"train"
)
tf
.
scalar_summary
(
'lr'
,
self
.
learning_rate
,
name
=
"train"
)
return
tf
.
merge_all_summaries
()
def
start_thread
(
self
,
session
):
"""
Start pool of threads for pre-fetching
**Parameters**
session: Tensorflow session
"""
threads
=
[]
for
n
in
range
(
1
):
t
=
threading
.
Thread
(
target
=
self
.
load_and_enqueue
,
args
=
(
session
,
))
for
n
in
range
(
3
):
t
=
threading
.
Thread
(
target
=
self
.
load_and_enqueue
,
args
=
(
session
,))
t
.
daemon
=
True
# thread will close when parent quits
t
.
start
()
threads
.
append
(
t
)
...
...
@@ -191,6 +227,9 @@ class Trainer(object):
def
load_and_enqueue
(
self
,
session
):
"""
Injecting data in the place holder queue
**Parameters**
session: Tensorflow session
"""
while
not
self
.
thread_pool
.
should_stop
():
...
...
@@ -204,8 +243,7 @@ class Trainer(object):
def
train
(
self
,
train_data_shuffler
,
validation_data_shuffler
=
None
):
"""
Do the loop forward --> backward --|
^--------------------|
Train the network
"""
# Creating directory
...
...
@@ -220,14 +258,14 @@ class Trainer(object):
self
.
weight_decay
# Decay step
)
self
.
training_graph
=
self
.
compute_graph
(
train_data_shuffler
,
name
=
"train"
)
self
.
training_graph
=
self
.
compute_graph
(
train_data_shuffler
,
prefetch
=
self
.
prefetch
,
name
=
"train"
)
# Preparing the optimizer
self
.
optimizer_class
.
_learning_rate
=
self
.
learning_rate
self
.
optimizer
=
self
.
optimizer_class
.
minimize
(
self
.
training_graph
,
global_step
=
tf
.
Variable
(
0
))
# Train summary
self
.
summaries_train
=
self
.
__
create_general_summary
()
self
.
summaries_train
=
self
.
create_general_summary
()
logger
.
info
(
"Initializing !!"
)
# Training
...
...
@@ -247,13 +285,19 @@ class Trainer(object):
self
.
train_summary_writter
=
tf
.
train
.
SummaryWriter
(
os
.
path
.
join
(
self
.
temp_dir
,
'train'
),
session
.
graph
)
for
step
in
range
(
self
.
iterations
):
self
.
__fit
(
session
,
step
)
start
=
time
.
time
()
self
.
fit
(
session
,
step
)
end
=
time
.
time
()
summary
=
summary_pb2
.
Summary
.
Value
(
tag
=
"elapsed_time"
,
simple_value
=
float
(
end
-
start
))
self
.
train_summary_writter
.
add_summary
(
summary_pb2
.
Summary
(
value
=
[
summary
]),
step
)
if
validation_data_shuffler
is
not
None
and
step
%
self
.
snapshot
==
0
:
self
.
__
compute_validation
(
session
,
validation_data_shuffler
,
step
)
self
.
compute_validation
(
session
,
validation_data_shuffler
,
step
)
if
self
.
analizer
is
not
None
:
self
.
validation_summary_writter
.
add_summary
(
self
.
analizer
(
validation_data_shuffler
,
self
.
architecture
,
session
),
step
)
validation_data_shuffler
,
self
,
session
),
step
)
logger
.
info
(
"Training finally finished"
)
...
...
setup.py
View file @
9da6ef05
...
...
@@ -79,6 +79,7 @@ setup(
'train_mnist_triplet.py = bob.learn.tensorflow.script.train_mnist_triplet:main'
,
'train_siamese_casia_webface.py = bob.learn.tensorflow.script.train_siamese_casia_webface:main'
,
'train_mobio.py = bob.learn.tensorflow.script.train_mobio:main'
,
],
...
...
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