parser.add_argument("checkpoint_file",
help='Path to single model checkpoint (.pkl) file.')
args = parser.parse_args()
checkpoint_file = args.checkpoint_file
fold = int(args.split)
dataset_path = os.path.join(data_paths.tfd_data_path, 'npy_files/TFD_96/split_'+str(fold))
print 'Checkpoint: %s' % checkpoint_file
print 'Testing on split %d\n' % fold
# Load model
model = SupervisedModel('evaluation', './')
# Load dataset
supervised_data_loader = SupervisedDataLoader(dataset_path)
test_data_container = supervised_data_loader.load(2)
test_data_container.X = numpy.float32(test_data_container.X)
test_data_container.X /= 255.0
test_data_container.X *= 2.0
# Construct evaluator
preprocessor = [util.Normer3(filter_size=5, num_channels=1)]
evaluator = util.Evaluator(model, test_data_container,
checkpoint_file, preprocessor)
# For the inputted checkpoint, compute the overall test accuracy
accuracies = []
print 'Checkpoint: %s' % os.path.split(checkpoint_file)[1]
evaluator.set_checkpoint(checkpoint_file)
f.write(str(pid)+'\n')
f.close()
# Load model
model = SupervisedModel('experiment', './', learning_rate=1e-2)
monitor = util.Monitor(model,
checkpoint_directory='checkpoints_'+str(train_split),
save_steps=1000)
# Add dropout flag to fully-connected layer
model.fc4.dropout = 0.5
model._compile()
# Loading TFD dataset
print('Loading Data')
supervised_data_loader = SupervisedDataLoader(
os.path.join(data_paths.tfd_data_path, 'npy_files/TFD_96/split_'+str(train_split)))
train_data_container = supervised_data_loader.load(0)
val_data_container = supervised_data_loader.load(1)
test_data_container = supervised_data_loader.load(2)
X_train = train_data_container.X
y_train = train_data_container.y
X_val = val_data_container.X
y_val = val_data_container.y
X_test = test_data_container.X
y_test = test_data_container.y
X_train = numpy.float32(X_train)
X_train /= 255.0
X_train *= 2.0
