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