full_id = 'soft-base-9900' + idd folder = 'Dense_train' model = Adapt(config_model=config_model, pretraining=False) model.create_saver() path = os.path.join(config.workdir, 'floydhub_model', "pretraining") # path = os.path.join(config.log_dir, "pretraining") model.restore_model(path, full_id) ## Connect DAS model to the front end from models.dense import Dense_net as Dense with model.graph.as_default(): model.connect_front(Dense) model.sepNet.output = model.sepNet.prediction model.back model.cost model.optimize # model.freeze_front() # model.optimize model.tensorboard_init() from itertools import compress with model.graph.as_default(): global_vars = tf.global_variables() is_not_initialized = model.sess.run([~(tf.is_variable_initialized(var)) \ for var in global_vars]) not_initialized_vars = list(compress(global_vars, is_not_initialized)) if len(not_initialized_vars):
#### #### NEW MODEL CONFIGURATION #### config_model["type"] = "L41_finetuning" learning_rate = 0.001 batch_size = 64 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) with model.graph.as_default(): model.connect_front(L41Model) var_list = [v for v in tf.global_variables() if ('front' in v.name)] model.create_saver(var_list) model.restore_model(path_adapt, full_id_adapt) model.sepNet.prediction model.sepNet.separate model.sepNet.output = model.sepNet.enhance var_list = [ v for v in tf.global_variables() if ('prediction' in v.name or 'speaker_centroids' in v.name or 'enhance' in v.name) ] model.create_saver(var_list) model.restore_model(path, full_id) model.separator model.back