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bob
bob.learn.tensorflow
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9f006a6b
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9f006a6b
authored
8 years ago
by
Tiago de Freitas Pereira
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#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
# @date: Wed 11 May 2016 09:39:36 CEST
"""
Simple script that trains MNIST with LENET using Tensor flow
Usage:
train_mnist.py [--batch-size=<arg> --iterations=<arg> --validation-interval=<arg> --use-gpu]
train_mnist.py -h | --help
Options:
-h --help Show this screen.
--batch-size=<arg> [default: 1]
--iterations=<arg> [default: 30000]
--validation-interval=<arg> [default: 100]
"""
from
docopt
import
docopt
import
tensorflow
as
tf
from
..
import
util
from
..DataShuffler
import
*
from
..lenet
import
Lenet
from
matplotlib.backends.backend_pdf
import
PdfPages
import
sys
SEED
=
10
from
..DataShuffler
import
*
def
compute_euclidean_distance
(
x
,
y
):
"""
Computes the euclidean distance between two tensorflow variables
"""
#d = tf.square(tf.sub(x, y))
#d = tf.sqrt(tf.reduce_sum(d)) # What about the axis ???
d
=
tf
.
sqrt
(
tf
.
reduce_sum
(
tf
.
square
(
tf
.
sub
(
x
,
y
)),
1
))
return
d
def
compute_contrastive_loss
(
left_feature
,
right_feature
,
label
,
margin
):
"""
Compute the contrastive loss as in
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
L = 0.5 * (Y) * D^2 + 0.5 * (1-Y) * {max(0, margin - D)}^2
**Parameters**
left_feature: First element of the pair
right_feature: Second element of the pair
label: Label of the pair (0 or 1)
margin: Contrastive margin
**Returns**
Return the loss operation
"""
#label = tf.to_float(label)
#one = tf.constant(1.0)
#zero = tf.constant(0.0)
#half = tf.constant(0.5)
#m = tf.constant(margin)
#d = compute_euclidean_distance(left_feature, right_feature)
#first_part = tf.mul(label, tf.square(d))# (Y)*(d^2)
#max_part = tf.square(tf.maximum(m-d, zero))
#second_part = tf.mul(one-label, max_part) # (1-Y) * max(margin - d, 0)
#loss = half * tf.reduce_sum(first_part + second_part)
#return loss
# Stack overflow "fix"
label
=
tf
.
to_float
(
label
)
one
=
tf
.
constant
(
1.0
)
d
=
compute_euclidean_distance
(
left_feature
,
right_feature
)
first_part
=
tf
.
mul
(
one
-
label
,
tf
.
square
(
d
))
# (Y-1)*(d^2)
max_part
=
tf
.
square
(
tf
.
maximum
(
margin
-
d
,
0
))
second_part
=
tf
.
mul
(
label
,
max_part
)
# (Y) * max((margin - d)^2, 0)
loss
=
0.5
*
tf
.
reduce_mean
(
first_part
+
second_part
)
return
loss
def
main
():
args
=
docopt
(
__doc__
,
version
=
'
Mnist training with TensorFlow
'
)
BATCH_SIZE
=
int
(
args
[
'
--batch-size
'
])
ITERATIONS
=
int
(
args
[
'
--iterations
'
])
VALIDATION_TEST
=
int
(
args
[
'
--validation-interval
'
])
perc_train
=
0.9
CONTRASTIVE_MARGIN
=
0.1
USE_GPU
=
args
[
'
--use-gpu
'
]
#print("Load data")
#sys.stdout.flush()
data
,
labels
=
util
.
load_mnist
(
data_dir
=
"
./src/bob.db.mnist/bob/db/mnist/
"
)
data_shuffler
=
DataShuffler
(
data
,
labels
,
scale
=
True
)
#print("A")
#sys.stdout.flush()
# Siamease place holders
train_left_data
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
BATCH_SIZE
*
2
,
28
,
28
,
1
),
name
=
"
left
"
)
train_right_data
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
BATCH_SIZE
*
2
,
28
,
28
,
1
),
name
=
"
right
"
)
labels_data
=
tf
.
placeholder
(
tf
.
int32
,
shape
=
BATCH_SIZE
*
2
)
validation_data
=
tf
.
placeholder
(
tf
.
float32
,
shape
=
(
data_shuffler
.
validation_data
.
shape
[
0
],
28
,
28
,
1
))
#print("B")
#sys.stdout.flush()
# Creating the architecture
lenet_architecture
=
Lenet
(
seed
=
SEED
,
use_gpu
=
USE_GPU
)
lenet_train_left
=
lenet_architecture
.
create_lenet
(
train_left_data
)
lenet_train_right
=
lenet_architecture
.
create_lenet
(
train_right_data
)
lenet_validation
=
lenet_architecture
.
create_lenet
(
validation_data
,
train
=
False
)
#print("C")
#sys.stdout.flush()
# Defining the constrastive loss
#left_output = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(lenet_train_left, labels_data))
#right_output = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(lenet_train_right, labels_data))
#loss = compute_contrastive_loss(tf.nn.softmax(lenet_train_left), tf.nn.softmax(lenet_train_right), labels_data, CONTRASTIVE_MARGIN)
loss
=
compute_contrastive_loss
(
lenet_train_left
,
lenet_train_right
,
labels_data
,
CONTRASTIVE_MARGIN
)
#print("D")
#sys.stdout.flush()
#regularizer = (tf.nn.l2_loss(lenet_architecture.W_fc1) + tf.nn.l2_loss(lenet_architecture.b_fc1) +
# tf.nn.l2_loss(lenet_architecture.W_fc2) + tf.nn.l2_loss(lenet_architecture.b_fc2))
#loss += 5e-4 * regularizer
# Defining training parameters
batch
=
tf
.
Variable
(
0
)
learning_rate
=
tf
.
train
.
exponential_decay
(
0.001
,
# Learning rate
batch
*
BATCH_SIZE
,
data_shuffler
.
train_data
.
shape
[
0
],
0.95
# Decay step
)
#print("E")
#sys.stdout.flush()
#optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=batch)
optimizer
=
tf
.
train
.
MomentumOptimizer
(
learning_rate
,
momentum
=
0.99
,
use_locking
=
False
,
name
=
'
Momentum
'
).
minimize
(
loss
,
global_step
=
batch
)
#validation_prediction = tf.nn.softmax(lenet_validation)
#print("Initializing")
#sys.stdout.flush()
# Training
with
tf
.
Session
()
as
session
:
#print("INITIALIZE ALL VARIABLES")
#sys.stdout.flush()
tf
.
initialize_all_variables
().
run
()
#print("INITIALIZE ALL VARIABLES - OK")
#sys.stdout.flush()
#pp = PdfPages("groups.pdf")
for
step
in
range
(
ITERATIONS
):
batch_left
,
batch_right
,
labels
=
data_shuffler
.
get_pair
(
BATCH_SIZE
)
#print("FEED DICT")
#sys.stdout.flush()
feed_dict
=
{
train_left_data
:
batch_left
,
train_right_data
:
batch_right
,
labels_data
:
labels
}
#print("Run")
#sys.stdout.flush()
_
,
l
,
lr
=
session
.
run
([
optimizer
,
loss
,
learning_rate
],
feed_dict
=
feed_dict
)
#print("Ok")
#sys.stdout.flush()
if
step
%
VALIDATION_TEST
==
0
:
batch_train_data
,
batch_train_labels
=
data_shuffler
.
get_batch
(
data_shuffler
.
validation_data
.
shape
[
0
],
train_dataset
=
True
)
features_train
=
session
.
run
(
lenet_validation
,
feed_dict
=
{
validation_data
:
batch_train_data
[:]})
batch_validation_data
,
batch_validation_labels
=
data_shuffler
.
get_batch
(
data_shuffler
.
validation_data
.
shape
[
0
],
train_dataset
=
False
)
features_validation
=
session
.
run
(
lenet_validation
,
feed_dict
=
{
validation_data
:
batch_validation_data
[:]})
#eer = util.compute_eer(features_train, batch_train_labels, features_validation, batch_validation_labels, 10)
#print("Step {0}. Loss = {1}, Lr={2}, EER = {3}".
# format(step, l, lr, eer))
accuracy
=
util
.
compute_accuracy
(
features_train
,
batch_train_labels
,
features_validation
,
batch_validation_labels
,
10
)
print
(
"
Step {0}. Loss = {1}, Lr={2}, Acc = {3}
"
.
format
(
step
,
l
,
lr
,
accuracy
))
sys
.
stdout
.
flush
()
#fig = util.plot_embedding_lda(features_validation, batch_validation_labels)
#pp.savefig(fig)
#accuracy_train = util.evaluate_softmax(batch_train_data, batch_train_labels, session,
# tf.nn.softmax(lenet_validation),
# validation_data)
#accuracy_validation = util.evaluate_softmax(batch_validation_data, batch_validation_labels, session,
# tf.nn.softmax(lenet_validation), validation_data)
#
#print("Step {0}. Loss = {1}, Lr={2}, Accuracy train = {3}, Accuracy validation = {4}".
# format(step, l, lr, accuracy_train, accuracy_validation))
#print("EER = {0}".format(eer))
#print("Step {0}. Loss = {1}, Lr={2}, Accuracy train = {3}, Accuracy validation = {4}".
# format(step, l, lr, accuracy_train, accuracy_validation))
print
(
"
Step {0}. Loss = {1}, Lr={2}, Acc = {3}
"
.
format
(
step
,
l
,
lr
,
accuracy
))
#pp.close()
print
(
"
End !!
"
)
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