From ef1c03ce9a85055aaf849b98f84c801a541395e7 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Thu, 25 Aug 2016 16:45:04 +0200
Subject: [PATCH] Make Siamese network work
---
bob/learn/tensorflow/analyzers/Analizer.py | 5 +++--
bob/learn/tensorflow/data/DataShuffler.py | 2 +-
bob/learn/tensorflow/loss/ContrastiveLoss.py | 10 +++++-----
bob/learn/tensorflow/network/Lenet.py | 3 ++-
.../tensorflow/script/train_mnist_siamese.py | 2 +-
bob/learn/tensorflow/trainers/SiameseTrainer.py | 16 ++++++++++------
6 files changed, 22 insertions(+), 16 deletions(-)
diff --git a/bob/learn/tensorflow/analyzers/Analizer.py b/bob/learn/tensorflow/analyzers/Analizer.py
index f307f3cb..27ea52f0 100644
--- a/bob/learn/tensorflow/analyzers/Analizer.py
+++ b/bob/learn/tensorflow/analyzers/Analizer.py
@@ -46,6 +46,7 @@ class Analizer:
def extract_features(self):
data, labels = self.data_shuffler.get_batch(train_dataset=False)
feed_dict = {self.feature_placeholder: data}
+
return self.session.run([self.graph], feed_dict=feed_dict)[0], labels
def __call__(self):
@@ -98,7 +99,7 @@ class Analizer:
threshold = bob.measure.eer_threshold(negative_scores, positive_scores)
far, frr = bob.measure.farfrr(negative_scores, positive_scores, threshold)
eer = (far + frr) / 2.
- self.eer .append(eer)
+ self.eer.append(eer)
# Computing FAR 10
threshold = bob.measure.far_threshold(negative_scores, positive_scores, far_value=0.1)
@@ -115,4 +116,4 @@ class Analizer:
far, frr = bob.measure.farfrr(negative_scores, positive_scores, threshold)
self.far1000.append(frr)
- print eer
+ return
diff --git a/bob/learn/tensorflow/data/DataShuffler.py b/bob/learn/tensorflow/data/DataShuffler.py
index e0a93265..76d5d9d6 100644
--- a/bob/learn/tensorflow/data/DataShuffler.py
+++ b/bob/learn/tensorflow/data/DataShuffler.py
@@ -23,7 +23,7 @@ class DataShuffler(object):
"""
self.perc_train = perc_train
- self.scale = True
+ self.scale = scale
self.scale_value = 0.00390625
self.train_batch_size = train_batch_size
self.validation_batch_size = validation_batch_size
diff --git a/bob/learn/tensorflow/loss/ContrastiveLoss.py b/bob/learn/tensorflow/loss/ContrastiveLoss.py
index 740caec7..2609fe09 100644
--- a/bob/learn/tensorflow/loss/ContrastiveLoss.py
+++ b/bob/learn/tensorflow/loss/ContrastiveLoss.py
@@ -27,20 +27,20 @@ class ContrastiveLoss(BaseLoss):
"""
- def __init__(self):
- return
+ def __init__(self, contrastive_margin=1.0):
+ self.contrastive_margin = contrastive_margin
- def __call__(self, label, left_feature, right_feature, margin):
+ def __call__(self, label, left_feature, right_feature):
with tf.name_scope("contrastive_loss"):
label = tf.to_float(label)
one = tf.constant(1.0)
d = compute_euclidean_distance(left_feature, right_feature)
between_class = tf.exp(tf.mul(one - label, tf.square(d))) # (1-Y)*(d^2)
- max_part = tf.square(tf.maximum(margin - d, 0))
+ max_part = tf.square(tf.maximum(self.contrastive_margin - d, 0))
within_class = tf.mul(label, max_part) # (Y) * max((margin - d)^2, 0)
loss = 0.5 * tf.reduce_mean(within_class + between_class)
- return tf.reduce_mean(loss), tf.reduce_mean(between_class), tf.reduce_mean(within_class)
+ return loss, tf.reduce_mean(between_class), tf.reduce_mean(within_class)
diff --git a/bob/learn/tensorflow/network/Lenet.py b/bob/learn/tensorflow/network/Lenet.py
index 38972106..bb6d0e44 100644
--- a/bob/learn/tensorflow/network/Lenet.py
+++ b/bob/learn/tensorflow/network/Lenet.py
@@ -24,6 +24,7 @@ class Lenet(SequenceNetwork):
fc1_output=400,
n_classes=10,
+ feature_layer="fc2",
seed=10, use_gpu = False):
"""
@@ -41,7 +42,7 @@ class Lenet(SequenceNetwork):
seed = 10
"""
- super(Lenet, self).__init__(feature_layer="fc2")
+ super(Lenet, self).__init__(feature_layer=feature_layer)
self.add(Conv2D(name="conv1", kernel_size=conv1_kernel_size, filters=conv1_output, activation=tf.nn.tanh))
self.add(MaxPooling(name="pooling1"))
diff --git a/bob/learn/tensorflow/script/train_mnist_siamese.py b/bob/learn/tensorflow/script/train_mnist_siamese.py
index 9fd2398f..42804b9f 100644
--- a/bob/learn/tensorflow/script/train_mnist_siamese.py
+++ b/bob/learn/tensorflow/script/train_mnist_siamese.py
@@ -44,7 +44,7 @@ def main():
data_shuffler = PairDataShuffler(data, labels)
# Preparing the architecture
- lenet = Lenet()
+ lenet = Lenet(feature_layer="fc1")
loss = ContrastiveLoss()
trainer = SiameseTrainer(architecture=lenet, loss=loss, iterations=ITERATIONS, base_lr=0.00001)
diff --git a/bob/learn/tensorflow/trainers/SiameseTrainer.py b/bob/learn/tensorflow/trainers/SiameseTrainer.py
index 338db546..7b7adf5d 100644
--- a/bob/learn/tensorflow/trainers/SiameseTrainer.py
+++ b/bob/learn/tensorflow/trainers/SiameseTrainer.py
@@ -42,6 +42,7 @@ class SiameseTrainer(object):
self.weight_decay = weight_decay
self.convergence_threshold = convergence_threshold
+
def train(self, data_shuffler):
"""
Do the loop forward --> backward --|
@@ -64,10 +65,9 @@ class SiameseTrainer(object):
train_right_graph = self.architecture.compute_graph(train_placeholder_right_data)
feature_graph = self.architecture.compute_graph(feature_placeholder, cut=True)
- loss_train, between_class, within_class = self.loss(train_placeholder_labels,
+ loss_train, within_class, between_class = self.loss(train_placeholder_labels,
train_left_graph,
- train_right_graph,
- 0.2)
+ train_right_graph)
batch = tf.Variable(0)
learning_rate = tf.train.exponential_decay(
@@ -76,8 +76,10 @@ class SiameseTrainer(object):
data_shuffler.train_data.shape[0],
self.weight_decay # Decay step
)
- optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_train,
- global_step=batch)
+ #optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_train,
+ # global_step=batch)
+ optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.99, use_locking=False,
+ name='Momentum').minimize(loss_train, global_step=batch)
#train_prediction = tf.nn.softmax(train_graph)
#validation_prediction = tf.nn.softmax(validation_graph)
@@ -112,5 +114,7 @@ class SiameseTrainer(object):
if step % self.snapshot == 0:
analizer()
+ print str(step) + " - " + str(analizer.eer[-1])
+
- train_writer.close()
\ No newline at end of file
+ train_writer.close()
--
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