def load_model(mid_idx):
"""Load one model and return it"""
assert 0 <= mid_idx <= 4
args = Namespace(test=False, data_percent=100, model_name='', tf_type='melgram',
normalize='no', decibel=True, fmin=0.0, fmax=6000,
n_mels=96, trainable_fb=False, trainable_kernel=False,
conv_until=mid_idx)
model = build_convnet_model(args, last_layer=False)
model.load_weights('weights_transfer/weights_layer{}_{}.hdf5'.format(mid_idx, K._backend),
by_name=True)
return model
def load_model(mid_idx):
"""Load one model and return it"""
assert 0 <= mid_idx <= 4
args = Namespace(test=False, data_percent=100, model_name='', tf_type='melgram',
normalize='no', decibel=True, fmin=0.0, fmax=6000,
n_mels=96, trainable_fb=False, trainable_kernel=False,
conv_until=mid_idx)
model = build_convnet_model(args, last_layer=False)
model.load_weights('weights_transfer/weights_layer{}_{}.hdf5'.format(mid_idx, K._backend),
by_name=True)
return model
def load_model_for_mid(mid_idx):
assert 0 <= mid_idx <= 4
args = Namespace(test=False, data_percent=100, model_name='', tf_type='melgram',
normalize='no', decibel=True, fmin=0.0, fmax=6000,
n_mels=96, trainable_fb=False, trainable_kernel=False,
conv_until=mid_idx)
model = build_convnet_model(args, last_layer=False)
model.load_weights(os.path.join(FOLDER_WEIGHTS, 'weights_layer{}_{}.hdf5'.format(mid_idx, K._backend)),
by_name=True)
print('----- model {} weights are loaded. (NO ELM!!!) -----'.format(mid_idx))
return model
def load_model_for_mid(mid_idx):
assert 0 <= mid_idx <= 4
args = Namespace(test=False,
data_percent=100,
model_name='',
tf_type='melgram',
normalize='no',
decibel=True,
fmin=0.0,
fmax=6000,
n_mels=96,
trainable_fb=False,
trainable_kernel=False,
conv_until=mid_idx)
model = build_convnet_model(args, last_layer=False)
model.load_weights(os.path.join(
FOLDER_WEIGHTS, 'weights_layer{}_{}.hdf5'.format(mid_idx, K._backend)),
by_name=True)
print(
'----- model {} weights are loaded. (NO ELM!!!) -----'.format(mid_idx))
return model