learning_rate = 0.001 batch_size = 2 config_model["chunk_size"] = chunk_size config_model["alpha"] = learning_rate config_model["batch_size"] = batch_size folder = 'DPCL_finetuning' model = Adapt(config_model=config_model, pretraining=False) model.create_saver() path = os.path.join(config.model_root, 'log', 'DPCL_train_front') model.restore_model(path, full_id) model.connect_front_back_to_separator(DPCL) with model.graph.as_default(): model.create_saver() model.restore_model(path, full_id) # model.freeze_front() model.optimize model.tensorboard_init() init = model.non_initialized_variables() model.sess.run(init) print 'Total name :' print model.runID
#### config_model["type"] = "L41_finetuning" learning_rate = 0.001 batch_size = 1 config_model["chunk_size"] = chunk_size config_model["alpha"] = learning_rate config_model["batch_size"] = batch_size model = Adapt(config_model=config_model, pretraining=False) model.create_saver() path = os.path.join(config.model_root, 'log', 'L41_train_front') model.restore_model(path, full_id) model.connect_front_back_to_separator(L41Model) with model.graph.as_default(): model.create_saver() model.restore_model(path, full_id) # model.freeze_front() model.optimize model.tensorboard_init() init = model.non_initialized_variables() model.sess.run(init) print 'Total name :' print model.runID