diff --git a/bob/learn/tensorflow/network/SequenceNetwork.py b/bob/learn/tensorflow/network/SequenceNetwork.py
index 8c7ecf954da355ddf66f4a3bc00d773256acbff2..595a9283e9dadc74dac79b6818950755e9edf52b 100644
--- a/bob/learn/tensorflow/network/SequenceNetwork.py
+++ b/bob/learn/tensorflow/network/SequenceNetwork.py
@@ -225,17 +225,32 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
self.sequence_net[k].weights_initialization.use_gpu = state
self.sequence_net[k].bias_initialization.use_gpu = state
- def load(self, hdf5, shape=None, session=None, batch=1):
+ def load_variables_only(self, hdf5, session):
"""
- Load the network
+ Load the variables of the model
+ """
+ hdf5.cd('/tensor_flow')
+ for k in self.sequence_net:
+ # TODO: IT IS NOT SMART TESTING ALONG THIS PAGE
+ if not isinstance(self.sequence_net[k], MaxPooling):
+ self.sequence_net[k].W.assign(hdf5.read(self.sequence_net[k].W.name)).eval(session=session)
+ session.run(self.sequence_net[k].W)
+ self.sequence_net[k].b.assign(hdf5.read(self.sequence_net[k].b.name)).eval(session=session)
+ session.run(self.sequence_net[k].b)
+ hdf5.cd("..")
+
+ def load(self, hdf5, shape=None, session=None, batch=1, use_gpu=False):
+ """
+ Load the network from scratch.
+ This will build the graphs
**Parameters**
hdf5: The saved network in the :py:class:`bob.io.base.HDF5File` format
-
- shape: Input shape of the network
-
- session: tensorflow `session <https://www.tensorflow.org/versions/r0.11/api_docs/python/client.html#Session>`_
+ shape: Input shape of the network. If `None`, the internal shape will be assumed
+ session: An opened tensorflow `session <https://www.tensorflow.org/versions/r0.11/api_docs/python/client.html#Session>`_. If `None`, a new one will be opened
+ batch: The size of the batch
+ use_gpu: Load all the variables in the GPU?
"""
if session is None:
@@ -249,7 +264,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
self.sequence_net = pickle.loads(hdf5.read('architecture'))
self.deployment_shape = hdf5.read('deployment_shape')
- self.turn_gpu_onoff(False)
+ self.turn_gpu_onoff(use_gpu)
if shape is None:
shape = self.deployment_shape
@@ -259,17 +274,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
place_holder = tf.placeholder(tf.float32, shape=shape, name="load")
self.compute_graph(place_holder)
tf.initialize_all_variables().run(session=session)
-
- hdf5.cd('/tensor_flow')
- for k in self.sequence_net:
- # TODO: IT IS NOT SMART TESTING ALONG THIS PAGE
- if not isinstance(self.sequence_net[k], MaxPooling):
- #self.sequence_net[k].W.assign(hdf5.read(self.sequence_net[k].W.name))
- self.sequence_net[k].W.assign(hdf5.read(self.sequence_net[k].W.name)).eval(session=session)
- session.run(self.sequence_net[k].W)
- self.sequence_net[k].b.assign(hdf5.read(self.sequence_net[k].b.name)).eval(session=session)
- session.run(self.sequence_net[k].b)
-
+ self.load_variables_only(hdf5, session)
"""
def save(self, session, path, step=None):
diff --git a/bob/learn/tensorflow/test/test_cnn_scratch.py b/bob/learn/tensorflow/test/test_cnn_scratch.py
index 469c3def57a5e5fbbd544f7ceea223af66af31fd..66b813f846157133eb0cfe0bd34dd3e684a74460 100644
--- a/bob/learn/tensorflow/test/test_cnn_scratch.py
+++ b/bob/learn/tensorflow/test/test_cnn_scratch.py
@@ -26,20 +26,7 @@ iterations = 50
seed = 10
-def test_cnn_trainer_scratch():
- train_data, train_labels, validation_data, validation_labels = load_mnist()
- train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
-
- # Creating datashufflers
- data_augmentation = ImageAugmentation()
- train_data_shuffler = Memory(train_data, train_labels,
- input_shape=[28, 28, 1],
- batch_size=batch_size,
- data_augmentation=data_augmentation)
- validation_data = numpy.reshape(validation_data, (validation_data.shape[0], 28, 28, 1))
-
- directory = "./temp/cnn"
-
+def scratch_network():
# Creating a random network
scratch = SequenceNetwork()
scratch.add(Conv2D(name="conv1", kernel_size=3,
@@ -51,20 +38,11 @@ def test_cnn_trainer_scratch():
activation=None,
weights_initialization=Xavier(seed=seed, use_gpu=False),
bias_initialization=Constant(use_gpu=False)))
- # Loss for the softmax
- loss = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
- # One graph trainer
- trainer = Trainer(architecture=scratch,
- loss=loss,
- iterations=iterations,
- analizer=None,
- prefetch=False,
- temp_dir=directory)
- trainer.train(train_data_shuffler)
+ return scratch
- del scratch
+def validate_network(validation_data, validation_labels, directory):
# Testing
validation_data_shuffler = Memory(validation_data, validation_labels,
input_shape=[28, 28, 1],
@@ -79,7 +57,40 @@ def test_cnn_trainer_scratch():
predictions = scratch(data, session=session)
accuracy = 100. * numpy.sum(numpy.argmax(predictions, 1) == labels) / predictions.shape[0]
- # At least 80% of accuracy
- assert accuracy > 80.
- shutil.rmtree(directory)
+ return accuracy
+
+
+def test_cnn_trainer_scratch():
+ train_data, train_labels, validation_data, validation_labels = load_mnist()
+ train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
+
+ # Creating datashufflers
+ data_augmentation = ImageAugmentation()
+ train_data_shuffler = Memory(train_data, train_labels,
+ input_shape=[28, 28, 1],
+ batch_size=batch_size,
+ data_augmentation=data_augmentation)
+ validation_data = numpy.reshape(validation_data, (validation_data.shape[0], 28, 28, 1))
+
+ directory = "./temp/cnn"
+
+ # Create scratch network
+ scratch = scratch_network()
+
+ # Loss for the softmax
+ loss = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
+
+ # One graph trainer
+ trainer = Trainer(architecture=scratch,
+ loss=loss,
+ iterations=iterations,
+ analizer=None,
+ prefetch=False,
+ temp_dir=directory)
+ trainer.train(train_data_shuffler)
+
+ accuracy = validate_network(validation_data, validation_labels, directory)
+ assert accuracy > 80
+ del scratch
+
diff --git a/bob/learn/tensorflow/trainers/SiameseTrainer.py b/bob/learn/tensorflow/trainers/SiameseTrainer.py
index c7cd48d9a4408f61a6ddf2f31d57e04a25bc6512..7cf1f20628d0cc9d0c38ed45698b8b62d6763a81 100644
--- a/bob/learn/tensorflow/trainers/SiameseTrainer.py
+++ b/bob/learn/tensorflow/trainers/SiameseTrainer.py
@@ -62,6 +62,8 @@ class SiameseTrainer(Trainer):
## Analizer
analizer=ExperimentAnalizer(),
+ model_from_file="",
+
verbosity_level=2):
super(SiameseTrainer, self).__init__(
@@ -86,6 +88,8 @@ class SiameseTrainer(Trainer):
## Analizer
analizer=analizer,
+ model_from_file=model_from_file,
+
verbosity_level=verbosity_level
)
diff --git a/bob/learn/tensorflow/trainers/Trainer.py b/bob/learn/tensorflow/trainers/Trainer.py
index 6cce163cdb55a10bceef05ce0e9f5cb05f948f16..68538236b3d069ce7c53c19aba525a04ea83ddc3 100644
--- a/bob/learn/tensorflow/trainers/Trainer.py
+++ b/bob/learn/tensorflow/trainers/Trainer.py
@@ -65,6 +65,9 @@ class Trainer(object):
## Analizer
analizer=SoftmaxAnalizer(),
+ ### Pretrained model
+ model_from_file="",
+
verbosity_level=2):
if not isinstance(architecture, SequenceNetwork):
@@ -107,6 +110,8 @@ class Trainer(object):
self.enqueue_op = None
self.global_step = None
+ self.model_from_file = model_from_file
+
bob.core.log.set_verbosity_level(logger, verbosity_level)
def __del__(self):
@@ -289,6 +294,12 @@ class Trainer(object):
with tf.Session(config=config) as session:
tf.initialize_all_variables().run()
+ # Loading a pretrained model
+ if self.model_from_file != "":
+ logger.info("Loading pretrained model from {0}".format(self.model_from_file))
+ hdf5 = bob.io.base.HDF5File(self.model_from_file)
+ self.architecture.load_variables_only(hdf5, session)
+
if isinstance(train_data_shuffler, OnLineSampling):
train_data_shuffler.set_feature_extractor(self.architecture, session=session)
diff --git a/bob/learn/tensorflow/trainers/TripletTrainer.py b/bob/learn/tensorflow/trainers/TripletTrainer.py
index 2379510f46e6a957bdb62d5220d1d9d17ffe6c2e..9452d5c8091c7b5c23ac4f5a1e08bff12d218cfa 100644
--- a/bob/learn/tensorflow/trainers/TripletTrainer.py
+++ b/bob/learn/tensorflow/trainers/TripletTrainer.py
@@ -62,6 +62,8 @@ class TripletTrainer(Trainer):
## Analizer
analizer=ExperimentAnalizer(),
+ model_from_file="",
+
verbosity_level=2):
super(TripletTrainer, self).__init__(
@@ -85,6 +87,7 @@ class TripletTrainer(Trainer):
## Analizer
analizer=analizer,
+ model_from_file=model_from_file,
verbosity_level=verbosity_level
)