train_data = numpy.load(os.path.join(reduced_data_path, 'train_X_split_0.npy'))
train_labels = numpy.load(
os.path.join(reduced_data_path, 'train_y_split_0.npy'))
test_data = numpy.load('/data/cifar10/test_X.npy')
test_labels = numpy.load('/data/cifar10/test_y.npy')
train_dataset = supervised_dataset.SupervisedDataset(train_data, train_labels)
test_dataset = supervised_dataset.SupervisedDataset(test_data, test_labels)
train_iterator = train_dataset.iterator(mode='random_uniform',
batch_size=128,
num_batches=100000)
test_iterator = test_dataset.iterator(mode='random_uniform',
batch_size=128,
num_batches=100000)
normer = util.Normer2(filter_size=5, num_channels=3)
print('Training Model')
for x_batch, y_batch in train_iterator:
x_batch = x_batch.transpose(1, 2, 3, 0)
x_batch = normer.run(x_batch)
#y_batch = numpy.int64(numpy.argmax(y_batch, axis=1))
monitor.start()
log_prob, accuracy = model.train(x_batch, y_batch)
monitor.stop(1 - accuracy) # monitor takes error instead of accuracy
if monitor.test:
monitor.start()
x_test_batch, y_test_batch = test_iterator.next()
x_test_batch = x_test_batch.transpose(1, 2, 3, 0)
x_test_batch = normer.run(x_test_batch)
if not os.path.exists(checkpoint_dir):
raise Exception('Checkpoint directory does not exist.')
checkpoint_list = sorted(os.listdir(checkpoint_dir))
model = CNNModel('xxx', './')
model.fc4.dropout = 0.0
model._compile()
num_channels = model.conv1.filter_shape[0]
filter_size = model.conv1.filter_shape[1]
# Get iterators for cifar10 test set
test_iterator = load_cifar10_data()
# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer2(filter_size=filter_size, num_channels=num_channels)
test_accuracies = []
for i, checkpoint_file in enumerate(checkpoint_list):
print 'Loading Checkpoint %s' % checkpoint_file
checkpoint_path = os.path.join(checkpoint_dir, checkpoint_file)
util.load_checkpoint(model, checkpoint_path)
print 'Compute Test Accuracy'
test_accuracies.append(
compute_overall_accuracy(model, normer, 'test', test_iterator))
print '\n'
test_iterator.reset()
