def init(repo_dir, validate_mode, training):
np.random.seed(0)
common_params = dict(sampling_freq=fs, clip_duration=cd, frame_duration=512)
if training:
media_params = AudioParams(**common_params)
set_name = 'tain' if validate_mode else 'full'
data_dir = repo_dir
else:
media_params = AudioParams(**common_params)
set_name = 'eval' if validate_mode else 'test'
data_dir = repo_dir if validate_mode else repo_dir.replace('train', 'test') + '_new'
data_dir += '/'
return media_params, set_name, data_dir
from neon.initializers import Gaussian, GlorotUniform
from neon.layers import Conv, Pooling, GeneralizedCost, Affine, DeepBiRNN, RecurrentMean
from neon.optimizers import Adagrad
from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, Misclassification
from neon.models import Model
from neon.data import DataLoader, AudioParams
from neon.callbacks.callbacks import Callbacks
from util import create_index_files, display
parser = NeonArgparser(__doc__)
args = parser.parse_args()
train_idx, valid_idx = create_index_files(args.data_dir)
common_params = dict(sampling_freq=22050, clip_duration=16000, frame_duration=16)
train_params = AudioParams(**common_params)
valid_params = AudioParams(**common_params)
common = dict(target_size=1, nclasses=10, repo_dir=args.data_dir)
train = DataLoader(set_name='music-train', media_params=train_params,
index_file=train_idx, shuffle=True, **common)
valid = DataLoader(set_name='music-valid', media_params=valid_params,
index_file=valid_idx, shuffle=False, **common)
init = Gaussian(scale=0.01)
layers = [Conv((2, 2, 4), init=init, activation=Rectlin(),
strides=dict(str_h=2, str_w=4)),
Pooling(2, strides=2),
Conv((3, 3, 4), init=init, batch_norm=True, activation=Rectlin(),
strides=dict(str_h=1, str_w=2)),
DeepBiRNN(128, init=GlorotUniform(), batch_norm=True, activation=Rectlin(),
reset_cells=True, depth=3),
RecurrentMean(),
data_dir = args.data_dir
out_dir = args.out_dir
if not os.path.exists(out_dir):
os.makedirs(out_dir)
if data_dir[-1] != '/':
data_dir += '/'
subj = int(data_dir[-2])
assert subj in [1, 2, 3]
indexer = Indexer()
tain_idx, test_idx = indexer.run(data_dir, pattern, testing=args.test_mode)
fs = 400
cd = 240000 * 1000 / fs
common_params = dict(sampling_freq=fs, clip_duration=cd, frame_duration=512)
tain_params = AudioParams(random_scale_percent=5.0, **common_params)
test_params = AudioParams(**common_params)
common = dict(target_size=1, nclasses=2)
tain_set = 'full' if args.test_mode else 'tain'
test_set = 'test' if args.test_mode else 'eval'
test_dir = data_dir.replace('train', 'test') if args.test_mode else data_dir
tain = DataLoader(set_name=tain_set, media_params=tain_params, index_file=tain_idx,
repo_dir=data_dir, **common)
test = DataLoader(set_name=test_set, media_params=test_params, index_file=test_idx,
repo_dir=test_dir, **common)
gauss = Gaussian(scale=0.01)
glorot = GlorotUniform()
tiny = dict(str_h=1, str_w=1)
small = dict(str_h=1, str_w=2)
big = dict(str_h=1, str_w=4)
parser = NeonArgparser(__doc__)
args = parser.parse_args()
args.data_dir = '/home/auto-114/PycharmProjects/neon_study_05/data'
train_idx, val_idx, all_idx, test_idx, noise_idx = create_index_files(
args.data_dir)
args.epochs = 20
train_dir = os.path.join(args.data_dir, 'train')
common_params = dict(sampling_freq=2000,
clip_duration=1700,
frame_duration=64,
overlap_percent=50)
train_params = AudioParams(noise_index_file=noise_idx,
noise_dir=train_dir,
**common_params)
test_params = AudioParams(**common_params)
common = dict(target_size=1, nclasses=2)
# Validate...
train = DataLoader(set_name='train',
repo_dir=train_dir,
media_params=train_params,
index_file=train_idx,
**common)
test = DataLoader(set_name='val',
repo_dir=train_dir,
media_params=test_params,
index_file=val_idx,
**common)
for idx, filename in enumerate(files):
fd = train_fd if idx < train_count else val_fd
rel_path = os.path.join(os.path.basename(subdir),
os.path.basename(filename))
fd.write(rel_path + ',' + str(label) + '\n')
return train_idx, val_idx
parser = NeonArgparser(__doc__)
args = parser.parse_args()
train_idx, val_idx = create_index_files(args.data_dir)
common_params = dict(sampling_freq=22050,
clip_duration=31000,
frame_duration=16)
train_params = AudioParams(random_scale_percent=5, **common_params)
val_params = AudioParams(**common_params)
common = dict(target_size=1, nclasses=10, repo_dir=args.data_dir)
train = DataLoader(set_name='genres-train',
media_params=train_params,
index_file=train_idx,
shuffle=True,
**common)
val = DataLoader(set_name='genres-val',
media_params=val_params,
index_file=val_idx,
shuffle=False,
**common)
init = Gaussian(scale=0.01)
layers = [
Conv((7, 7, 32),