from model_ML import create_model_Conv3D from keras import Sequential from keras.applications import MobileNetV2 dim = (224, 224) n_sequence = 10 n_channels = 3 n_output = 6 detail_weight = 'BUPT-Conv3D-KARD-transfer' # new_weight = np.zeros((3,3,3,3,32)) model = create_model_Conv3D(dim, n_sequence, n_channels, n_output) mobile_model = Sequential() mobile_model.add(MobileNetV2(weights='imagenet', include_top=False)) weights = model.layers[0].get_weights() # first layer weight_mobile = mobile_model.layers[0].get_weights()[ 0] # first layer, first weight for i in range(3): weights[0][:, :, i, :, :] = weight_mobile model.layers[0].set_weights(weights) # model.layers[2].set_weights(weights) model.save_weights(detail_weight + '-0-0-0.hdf5') print('test')
# # Generators training_generator = DataGeneratorBKB(train_keys, labels, **params, type_gen='train') validation_generator = DataGeneratorBKB(test_keys, labels, **params, type_gen='test') # # Design model if model_type == 'Conv3D': model = create_model_Conv3D(dim, n_sequence, n_channels, n_output, set_pretrain=True) else: model = create_model_pretrain(dim, n_sequence, n_channels, n_output, 1.0) load_model = True start_epoch = 0 if load_model: # weights_path = 'pretrain/mobileNetV2-BKB-3ds-48-0.55.hdf5' # weights_path = 'BUPT-Conv3D-dataset02-transfer-0-0-0.hdf5' #'KARD-aug-RGBdif-01-0.13-0.17.hdf5' weights_path = 'KARD-Conv3D-RGBdiff-crop-224-650-0.75-0.75.hdf5' #'BUPT-Conv3D-KARD-transfer-0-0-0.hdf5' start_epoch = 650 model.load_weights(weights_path) ## Set callback