def run_imagenet_test():
""" Runs the a test that trains a CNN to classify ImageNet data.
Returns:
A tuple containing the total elapsed time, and the average number of
training iterations per second. """
batch_size = 128
# How many batches to have loaded into VRAM at once.
load_batches = 5
# Learning rate hyperparameters.
learning_rate = 0.00001
decay_steps = 10000
decay_rate = 1
momentum = 0.9
weight_decay = 0.0005
rho = 0.9
epsilon = 1e-6
# Where we save the network.
save_file = "/home/theano/training_data/alexnet.pkl"
synsets_save_file = "/home/theano/training_data/synsets.pkl"
# Where we load the synsets to use from.
synset_list = "/job_files/ilsvrc16_synsets.txt"
# Where to load and save datasets.
dataset_path = "/home/theano/training_data/ilsvrc16_dataset"
# Where to cache image data.
cache_path = "/home/theano/training_data/cache"
# Where to save downloaded synset info.
synset_dir = "/home/theano/training_data/synsets"
data = data_loader.ImagenetLoader(batch_size, load_batches, cache_path,
dataset_path, synset_dir, synset_list)
if os.path.exists(synsets_save_file):
data.load(synsets_save_file)
train = data.get_train_set()
test = data.get_test_set()
cpu_labels = data.get_non_shared_test_set()
if os.path.exists(save_file):
# Load from the file.
print "Theano: Loading network from file..."
network = AlexNet.load(save_file, train, test, batch_size,
learning_rate=learning_rate)
else:
# Build new network.
network = AlexNet(train, test, batch_size,
patch_separation=batch_size * load_batches)
network.use_sgd_trainer(learning_rate, momentum=momentum,
weight_decay=weight_decay,
decay_rate=decay_rate,
decay_steps=decay_steps)
#network.use_rmsprop_trainer(learning_rate, rho, epsilon,
# decay_rate=decay_rate,
# decay_steps=decay_steps)
print "Theano: Starting ImageNet test..."
accuracy = 0
start_time = time.time()
iterations = 0
train_batch_index = 0
test_batch_index = 0
while iterations < 150000:
logger.debug("Train index, size: %d, %d" % (train_batch_index,
data.get_train_batch_size()))
logger.debug("Test index, size: %d, %d" % (test_batch_index,
data.get_test_batch_size()))
# Swap in new data if we need to.
if (train_batch_index + 1) * batch_size > data.get_train_batch_size():
train_batch_index = 0
logger.info("Getting train set.")
train = data.get_train_set()
logger.info("Got train set.")
# Swap in new data if we need to.
test_set_one_patch = data.get_test_batch_size() / 10
if (test_batch_index + 1) * batch_size > test_set_one_patch:
test_batch_index = 0
logger.info("Getting test set.")
test = data.get_test_set()
cpu_labels = data.get_non_shared_test_set()[:]
logger.info("Got test set.")
if iterations % 100 == 0:
# cpu_labels contains labels for every batch currently loaded in VRAM,
# without duplicates for additional patches.
label_index = test_batch_index * batch_size
top_one, top_five = network.test(test_batch_index,
cpu_labels[label_index:label_index + \
batch_size])
logger.info("Step %d, testing top 1: %f, testing top 5: %f" % \
(iterations, top_one, top_five))
test_batch_index += 1
cost, rate, step = network.train(train_batch_index)
logger.info("Training cost: %f, learning rate: %f, step: %d" % \
(cost, rate, step))
if iterations % 100 == 0:
print "Saving network..."
network.save(save_file)
# Save synset data as well.
data.save(synsets_save_file)
iterations += 1
train_batch_index += 1
elapsed = time.time() - start_time
speed = iterations / elapsed
print("Theano: Ran %d training iterations. (%f iter/s)" % \
(iterations, speed))
print("Theano: Imagenet test completed in %f seconds." % (elapsed))
data.exit_gracefully()
return (elapsed, speed)
