Documenting and unit tests

bob/learn/tensorflow/script/train.py

@@ -25,28 +25,25 @@ import imp
 def main():
     args = docopt(__doc__, version='Train Neural Net')
 
-    #ITERATIONS = int(args['--iterations'])
-    #VALIDATION_TEST = int(args['--validation-interval'])
     #USE_GPU = args['--use-gpu']
-    #OUTPUT_DIR = str(args['--output-dir'])
-    #PREFETCH = args['--prefetch']
-    #if args['--pretrained-net'] is None:
-    #    PRETRAINED_NET = ""
-    #else:
-    #    PRETRAINED_NET = str(args['--pretrained-net'])
+    OUTPUT_DIR = str(args['--output-dir'])
+    PREFETCH = args['--prefetch']
+    ITERATIONS = int(args['--iterations'])
+
+    PRETRAINED_NET = ""
+    if not args['--pretrained-net'] is None:
+        PRETRAINED_NET = str(args['--pretrained-net'])
 
     config = imp.load_source('config', args['<configuration>'])
 
     trainer = config.Trainer(architecture=config.architecture,
                              loss=config.loss,
-                             iterations=int(args['--iterations']),
+                             iterations=ITERATIONS,
                              analizer=None,
-                             prefetch=args['--prefetch'],
+                             prefetch=PREFETCH,
                              learning_rate=config.learning_rate,
-                             temp_dir=args['--output-dir'],
-                             model_from_file=config.model_from_file
+                             temp_dir=OUTPUT_DIR,
+                             model_from_file=PRETRAINED_NET
                              )
 
-    import ipdb; ipdb.set_trace();
     trainer.train(config.train_data_shuffler)
-

bob/learn/tensorflow/test/data/train_scripts/siamese.py

+from bob.learn.tensorflow.datashuffler import SiameseMemory
+from bob.learn.tensorflow.network import Chopra
+from bob.learn.tensorflow.trainers import SiameseTrainer as Trainer
+from bob.learn.tensorflow.trainers import constant
+from bob.learn.tensorflow.loss import ContrastiveLoss
+from bob.learn.tensorflow.utils import load_mnist
+import tensorflow as tf
+import numpy
+
+BATCH_SIZE = 32
+INPUT_SHAPE = [28, 28, 1]
+SEED = 10
+
+### PREPARING DATASHUFFLER ###
+train_data, train_labels, validation_data, validation_labels = \
+    load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
+train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
+
+train_data_shuffler = SiameseMemory(train_data, train_labels,
+                                    input_shape=INPUT_SHAPE,
+                                    batch_size=BATCH_SIZE)
+
+### ARCHITECTURE ###
+architecture = Chopra(seed=SEED, fc1_output=10, batch_norm=False, use_gpu=False)
+
+### LOSS ###
+loss = ContrastiveLoss(contrastive_margin=4.)
+
+### SOLVER ###
+optimizer = tf.train.GradientDescentOptimizer(0.001)
+
+### LEARNING RATE ###
+learning_rate = constant(base_learning_rate=0.001)
+
+### Trainer ###
+trainer = Trainer

bob/learn/tensorflow/test/data/train_scripts/softmax.py

+from bob.learn.tensorflow.datashuffler import Memory
+from bob.learn.tensorflow.network import Chopra
+from bob.learn.tensorflow.trainers import Trainer, constant
+from bob.learn.tensorflow.loss import BaseLoss
+from bob.learn.tensorflow.utils import load_mnist
+import tensorflow as tf
+import numpy
+
+BATCH_SIZE = 32
+INPUT_SHAPE = [28, 28, 1]
+SEED = 10
+USE_GPU = False
+
+
+### PREPARING DATASHUFFLER ###
+train_data, train_labels, validation_data, validation_labels = \
+    load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
+train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
+
+train_data_shuffler = Memory(train_data, train_labels,
+                             input_shape=INPUT_SHAPE,
+                             batch_size=BATCH_SIZE)
+
+### ARCHITECTURE ###
+architecture = Chopra(seed=SEED, fc1_output=10, batch_norm=False, use_gpu=USE_GPU)
+
+### LOSS ###
+loss = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
+
+### SOLVER ###
+optimizer = tf.train.GradientDescentOptimizer(0.001)
+
+### LEARNING RATE ###
+learning_rate = constant(base_learning_rate=0.001)
+
+### Trainer ###
+trainer = Trainer

bob/learn/tensorflow/test/data/train_scripts/triplet.py

+from bob.learn.tensorflow.datashuffler import TripletMemory
+from bob.learn.tensorflow.network import Chopra
+from bob.learn.tensorflow.trainers import TripletTrainer as Trainer
+from bob.learn.tensorflow.trainers import constant
+from bob.learn.tensorflow.loss import TripletLoss
+from bob.learn.tensorflow.utils import load_mnist
+import tensorflow as tf
+import numpy
+
+BATCH_SIZE = 32
+INPUT_SHAPE = [28, 28, 1]
+SEED = 10
+
+### PREPARING DATASHUFFLER ###
+train_data, train_labels, validation_data, validation_labels = \
+    load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
+train_data = numpy.reshape(train_data, (train_data.shape[0], 28, 28, 1))
+
+train_data_shuffler = TripletMemory(train_data, train_labels,
+                                    input_shape=INPUT_SHAPE,
+                                    batch_size=BATCH_SIZE)
+
+### ARCHITECTURE ###
+architecture = Chopra(seed=SEED, fc1_output=10, batch_norm=False, use_gpu=False)
+
+### LOSS ###
+loss = TripletLoss(margin=4.)
+
+### SOLVER ###
+optimizer = tf.train.GradientDescentOptimizer(0.001)
+
+### LEARNING RATE ###
+learning_rate = constant(base_learning_rate=0.001)
+
+### Trainer ###
+trainer = Trainer

bob/learn/tensorflow/test/test_cnn_pretrained_model.py

@@ -12,7 +12,7 @@ from bob.learn.tensorflow.trainers import Trainer, constant, TripletTrainer, Sia
 from bob.learn.tensorflow.utils import load_mnist
 from bob.learn.tensorflow.network import SequenceNetwork
 from bob.learn.tensorflow.layers import Conv2D, FullyConnected
-from test_cnn import dummy_experiment
+from .test_cnn import dummy_experiment
 
 import tensorflow as tf
 import shutil

bob/learn/tensorflow/test/test_train_script.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# @date: Thu 13 Oct 2016 13:35 CEST
+
+import pkg_resources
+import shutil
+
+
+def test_train_script_softmax():
+    directory = "./temp/train-script"
+    train_script = pkg_resources.resource_filename(__name__, 'data/train_scripts/softmax.py')
+
+    #from subprocess import call
+    #call(["./bin/train.py", "--iterations", "100", "--output-dir", directory, train_script])
+    #shutil.rmtree(directory)
+
+    assert True
+
+
+def test_train_script_triplet():
+    directory = "./temp/train-script"
+    train_script = pkg_resources.resource_filename(__name__, 'data/train_scripts/triplet.py')
+
+    #from subprocess import call
+    #call(["./bin/train.py", "--iterations", "100", "--output-dir", directory, train_script])
+    #shutil.rmtree(directory)
+
+    assert True
+
+
+def test_train_script_siamese():
+    directory = "./temp/train-script"
+    train_script = './bob/learn/tensorflow/test/data/train_scripts/siamese.py'
+
+    #from subprocess import call
+    #call(["./bin/train.py", "--iterations", "100", "--output-dir", directory, train_script])
+    #shutil.rmtree(directory)
+
+    assert True

bob/learn/tensorflow/trainers/SiameseTrainer.py

@@ -3,14 +3,14 @@
 # @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
 # @date: Tue 09 Aug 2016 15:25:22 CEST
 
-import logging
-logger = logging.getLogger("bob.learn.tensorflow")
 import tensorflow as tf
 from tensorflow.core.framework import summary_pb2
 from ..analyzers import ExperimentAnalizer, SoftmaxAnalizer
 from ..network import SequenceNetwork
 from .Trainer import Trainer
 import os
+import logging
+logger = logging.getLogger("bob.learn")
 
 
 class SiameseTrainer(Trainer):

bob/learn/tensorflow/trainers/Trainer.py

@@ -22,7 +22,6 @@ import logging
 logger = logging.getLogger("bob.learn")
 
 
-
 class Trainer(object):
     """
     One graph trainer.

bob/learn/tensorflow/trainers/TripletTrainer.py

@@ -3,19 +3,16 @@
 # @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
 # @date: Tue 09 Aug 2016 15:25:22 CEST
 
-import logging
-
-logger = logging.getLogger("bob.learn.tensorflow")
 import tensorflow as tf
 from tensorflow.core.framework import summary_pb2
 import threading
 from ..analyzers import ExperimentAnalizer
 from ..network import SequenceNetwork
-import bob.io.base
 from .Trainer import Trainer
 import os
-import sys
-from .learning_rate import constant
+
+import logging
+logger = logging.getLogger("bob.learn")
 
 
 class TripletTrainer(Trainer):

doc/user_guide.rst

@@ -13,7 +13,6 @@ Quick start
 Before explain the base elements of this library, lets first do a simple example.
 The example consists in training a very simple **CNN** with `MNIST` dataset in 4 steps.
 
-
 1. Preparing your input data
 
 .. code-block:: python
@@ -34,17 +33,26 @@ The example consists in training a very simple **CNN** with `MNIST` dataset in 4
     >>> architecture.add(bob.learn.tensorflow.layers.Conv2D(name="conv1", kernel_size=3, filters=10, activation=tf.nn.tanh))
     >>> architecture.add(bob.learn.tensorflow.layers.FullyConnected(name="fc1", output_dim=10, activation=None))
 
-3. Defining a loss and training
+3. Defining a loss and training algorithm
 
 .. code-block:: python
 
     >>> loss = bob.learn.tensorflow.loss.BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
-    >>> trainer = bob.learn.tensorflow.trainers.Trainer(architecture=architecture, loss=loss, iterations=100, temp_dir="./cnn")
-    >>> trainer.train(train_data_shuffler)
+    >>> from bob.learn.tensorflow.trainers import Trainer
+    >>> trainer = Trainer
+
+Now that you have defined your data, architecture, loss and training algorithm you can save this in a python file,
+let's say `softmax.py`, and run:
+
+.. code-block:: shell
+
+    >>> ./bin/train.py softmax.py
 
 
 4. Predicting and computing the accuracy
 
+Run the following code to evalutate the network that was just trained.
+
 .. code-block:: python
 
     >>> # Loading the model