EXP_DIR, MODEL_PATH, LOG_PATH = get_exp_path('tvec',
args,
override=args.retrain)
stdio(LOG_PATH)
# ====== load data feeder ====== #
(train, valid, X_test_name, X_test_true, X_test_data,
labels) = prepare_data(feat=args.feat, label=args.task)
n_classes = len(labels)
# ===========================================================================
# Create model
# ===========================================================================
inputs = [
K.placeholder(shape=(None, ) + shape[1:],
dtype='float32',
name='input%d' % i)
for i, shape in enumerate(as_tuple_of_shape(train.shape))
]
X = inputs[0]
y = inputs[1]
print("Inputs:", ctext(inputs, 'cyan'))
# ====== create the networks ====== #
with N.args_scope([('Conv', 'Dense'),
dict(b_init=None, activation=K.linear, pad='same')],
['BatchNorm', dict(activation=K.relu)]):
f = N.Sequence([
N.Dimshuffle(pattern=(0, 1, 2, 'x')),
N.Conv(num_filters=32, filter_size=(9, 7)),
N.BatchNorm(),
N.Pool(pool_size=(3, 2), strides=2),
N.Conv(num_filters=64, filter_size=(5, 3)),
N.BatchNorm(),
stdio(LOG_PATH)
# ===========================================================================
# Create data feeder
# ===========================================================================
(train, valid,
test_ids, test_dat,
all_speakers) = prepare_dnn_data(
recipe=args.recipe, feat=FEAT, utt_length=args.l)
n_speakers = len(all_speakers) + 1
# ===========================================================================
# Create the network
# ===========================================================================
inputs = [K.placeholder(shape=(None,) + shape[1:],
dtype='float32',
name='input%d' % i)
for i, shape in enumerate(as_tuple_of_shape(train.shape))]
X = inputs[0]
y = inputs[1]
print("Inputs:", ctext(inputs, 'cyan'))
# ====== the network ====== #
if os.path.exists(MODEL_PATH):
x_vec = N.deserialize(path=MODEL_PATH, force_restore_vars=True)
else:
TRAIN_MODEL = True
with N.args_scope(
['TimeDelayedConv', dict(time_pool='none', activation=K.relu)],
['Dense', dict(activation=K.linear, b_init=None)],
['BatchNorm', dict(activation=K.relu)]
):
x_vec = N.Sequence([
N.Dropout(level=0.3),