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