Added scratch load NN

bob/learn/tensorflow/test/test_cnn_scratch.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 numpy
+import bob.io.base
+import os
+from bob.learn.tensorflow.datashuffler import Memory, ImageAugmentation
+from bob.learn.tensorflow.initialization import Xavier, Constant
+from bob.learn.tensorflow.network import SequenceNetwork
+from bob.learn.tensorflow.loss import BaseLoss
+from bob.learn.tensorflow.trainers import Trainer
+from bob.learn.tensorflow.util import load_mnist
+from bob.learn.tensorflow.layers import Conv2D, FullyConnected, MaxPooling
+import tensorflow as tf
+import shutil
+
+"""
+Some unit tests that create networks on the fly
+"""
+
+batch_size = 16
+validation_batch_size = 400
+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"
+
+    # Creating a random network
+    scratch = SequenceNetwork()
+    scratch.add(Conv2D(name="conv1", kernel_size=3,
+                       filters=10,
+                       activation=tf.nn.tanh,
+                       weights_initialization=Xavier(seed=seed, use_gpu=False),
+                       bias_initialization=Constant(use_gpu=False)))
+    scratch.add(FullyConnected(name="fc1", output_dim=10,
+                               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)
+
+    del scratch
+
+    # Testing
+    validation_data_shuffler = Memory(validation_data, validation_labels,
+                                      input_shape=[28, 28, 1],
+                                      batch_size=validation_batch_size)
+    with tf.Session() as session:
+
+        validation_shape = [400, 28, 28, 1]
+        scratch = SequenceNetwork()
+        scratch.load(bob.io.base.HDF5File(os.path.join(directory, "model.hdf5")),
+                     shape=validation_shape, session=session)
+        [data, labels] = validation_data_shuffler.get_batch()
+        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)
+