Pretrained model test

bob/learn/tensorflow/test/test_cnn_pretrained_model.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.loss import BaseLoss
+from bob.learn.tensorflow.trainers import Trainer, constant
+from bob.learn.tensorflow.util import load_mnist
+import tensorflow as tf
+import shutil
+
+"""
+Some unit tests that create networks on the fly and load variables
+"""
+
+batch_size = 16
+validation_batch_size = 400
+iterations = 50
+seed = 10
+
+from test_cnn_scratch import scratch_network, validate_network
+
+
+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"
+    directory2 = "./temp/cnn2"
+
+    # Creating a random 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,
+                      learning_rate=constant(0.05, name="lr"),
+                      temp_dir=directory)
+    trainer.train(train_data_shuffler)
+
+    accuracy = validate_network(validation_data, validation_labels, directory)
+    assert accuracy > 85
+
+    del scratch
+    del loss
+    # Training the network using a pre trained model
+    loss2 = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean, name="loss2")
+    scratch = scratch_network()
+    trainer2 = Trainer(architecture=scratch,
+                       loss=loss2,
+                       iterations=iterations,
+                       analizer=None,
+                       prefetch=False,
+                       learning_rate=constant(0.05, name="lr2"),
+                       temp_dir=directory2,
+                       model_from_file=os.path.join(directory, "model.hdf5"))
+
+    trainer2.train(train_data_shuffler)
+    accuracy = validate_network(validation_data, validation_labels, directory)
+    assert accuracy > 90