我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用model.model()。
def maybe_save_model(savedir, container, state): """This function checkpoints the model and state of the training algorithm.""" if savedir is None: return start_time = time.time() model_dir = "model-{}".format(state["num_iters"]) U.save_state(os.path.join(savedir, model_dir, "saved")) if container is not None: container.put(os.path.join(savedir, model_dir), model_dir) relatively_safe_pickle_dump(state, os.path.join(savedir, 'training_state.pkl.zip'), compression=True) if container is not None: container.put(os.path.join(savedir, 'training_state.pkl.zip'), 'training_state.pkl.zip') relatively_safe_pickle_dump(state["monitor_state"], os.path.join(savedir, 'monitor_state.pkl')) if container is not None: container.put(os.path.join(savedir, 'monitor_state.pkl'), 'monitor_state.pkl') logger.log("Saved model in {} seconds\n".format(time.time() - start_time))
def maybe_load_model(savedir, container): """Load model if present at the specified path.""" if savedir is None: return state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip')) if container is not None: logger.log("Attempting to download model from Azure") found_model = container.get(savedir, 'training_state.pkl.zip') else: found_model = os.path.exists(state_path) if found_model: state = pickle_load(state_path, compression=True) model_dir = "model-{}".format(state["num_iters"]) if container is not None: container.get(savedir, model_dir) U.load_state(os.path.join(savedir, model_dir, "saved")) logger.log("Loaded models checkpoint at {} iterations".format(state["num_iters"])) return state
def __init__(self, reddit_api): self.model1 = model(reddit_api) self.view1 = view() self.view1.run() self.model1.set_username(self.view1.user_name) if len(self.model1.reddit_username) == 0: print "Application Terminated" exit(1)
def parse_args(): parser = argparse.ArgumentParser("DQN experiments for Atari games") # Environment parser.add_argument("--env", type=str, default="Pong", help="name of the game") parser.add_argument("--seed", type=int, default=42, help="which seed to use") # Core DQN parameters parser.add_argument("--replay-buffer-size", type=int, default=int(1e6), help="replay buffer size") parser.add_argument("--lr", type=float, default=1e-4, help="learning rate for Adam optimizer") parser.add_argument("--num-steps", type=int, default=int(2e8), help="total number of steps to run the environment for") parser.add_argument("--batch-size", type=int, default=32, help="number of transitions to optimize at the same time") parser.add_argument("--learning-freq", type=int, default=4, help="number of iterations between every optimization step") parser.add_argument("--target-update-freq", type=int, default=40000, help="number of iterations between every target network update") # Bells and whistles boolean_flag(parser, "double-q", default=True, help="whether or not to use double q learning") boolean_flag(parser, "dueling", default=False, help="whether or not to use dueling model") boolean_flag(parser, "prioritized", default=False, help="whether or not to use prioritized replay buffer") parser.add_argument("--prioritized-alpha", type=float, default=0.6, help="alpha parameter for prioritized replay buffer") parser.add_argument("--prioritized-beta0", type=float, default=0.4, help="initial value of beta parameters for prioritized replay") parser.add_argument("--prioritized-eps", type=float, default=1e-6, help="eps parameter for prioritized replay buffer") # Checkpointing parser.add_argument("--save-dir", type=str, default=None, help="directory in which training state and model should be saved.") parser.add_argument("--save-azure-container", type=str, default=None, help="It present data will saved/loaded from Azure. Should be in format ACCOUNT_NAME:ACCOUNT_KEY:CONTAINER") parser.add_argument("--save-freq", type=int, default=1e6, help="save model once every time this many iterations are completed") boolean_flag(parser, "load-on-start", default=True, help="if true and model was previously saved then training will be resumed") return parser.parse_args()
def parse_args(): parser = argparse.ArgumentParser("DQN experiments for Atari games") # Environment parser.add_argument("--env", type=str, default="Pong", help="name of the game") parser.add_argument("--seed", type=int, default=42, help="which seed to use") # Core DQN parameters parser.add_argument("--replay-buffer-size", type=int, default=int(1e6), help="replay buffer size") parser.add_argument("--lr", type=float, default=1e-4, help="learning rate for Adam optimizer") parser.add_argument("--num-steps", type=int, default=int(2e8), help="total number of steps to run the environment for") parser.add_argument("--batch-size", type=int, default=32, help="number of transitions to optimize at the same time") parser.add_argument("--learning-freq", type=int, default=4, help="number of iterations between every optimization step") parser.add_argument("--target-update-freq", type=int, default=40000, help="number of iterations between every target network update") # Bells and whistles boolean_flag(parser, "noisy", default=False, help="whether or not to NoisyNetwork") boolean_flag(parser, "double-q", default=True, help="whether or not to use double q learning") boolean_flag(parser, "dueling", default=False, help="whether or not to use dueling model") boolean_flag(parser, "prioritized", default=False, help="whether or not to use prioritized replay buffer") parser.add_argument("--prioritized-alpha", type=float, default=0.6, help="alpha parameter for prioritized replay buffer") parser.add_argument("--prioritized-beta0", type=float, default=0.4, help="initial value of beta parameters for prioritized replay") parser.add_argument("--prioritized-eps", type=float, default=1e-6, help="eps parameter for prioritized replay buffer") # Checkpointing parser.add_argument("--save-dir", type=str, default=None, required=True, help="directory in which training state and model should be saved.") parser.add_argument("--save-azure-container", type=str, default=None, help="It present data will saved/loaded from Azure. Should be in format ACCOUNT_NAME:ACCOUNT_KEY:CONTAINER") parser.add_argument("--save-freq", type=int, default=1e6, help="save model once every time this many iterations are completed") boolean_flag(parser, "load-on-start", default=True, help="if true and model was previously saved then training will be resumed") return parser.parse_args()
def __init__(self, model_dir=None, gpu_fraction=0.7): config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.per_process_gpu_memory_fraction=gpu_fraction self.sess = tf.Session(config=config) self.imgs_ph, self.bn, self.output_tensors, self.pred_labels, self.pred_locs = model.model(self.sess) total_boxes = self.pred_labels.get_shape().as_list()[1] self.positives_ph, self.negatives_ph, self.true_labels_ph, self.true_locs_ph, self.total_loss, self.class_loss, self.loc_loss = \ model.loss(self.pred_labels, self.pred_locs, total_boxes) out_shapes = [out.get_shape().as_list() for out in self.output_tensors] c.out_shapes = out_shapes c.defaults = model.default_boxes(out_shapes) # variables in model are already initialized, so only initialize those declared after with tf.variable_scope("optimizer"): self.global_step = tf.Variable(0) self.lr_ph = tf.placeholder(tf.float32, shape=[]) self.optimizer = tf.train.AdamOptimizer(1e-3).minimize(self.total_loss, global_step=self.global_step) new_vars = tf.get_collection(tf.GraphKeys.VARIABLES, scope="optimizer") self.sess.run(tf.initialize_variables(new_vars)) if model_dir is None: model_dir = FLAGS.model_dir ckpt = tf.train.get_checkpoint_state(model_dir) self.saver = tf.train.Saver() if ckpt and ckpt.model_checkpoint_path: self.saver.restore(self.sess, ckpt.model_checkpoint_path) print("restored %s" % ckpt.model_checkpoint_path)
def __init__(self, model_dir=None): self.sess = tf.Session() self.imgs_ph, self.bn, self.output_tensors, self.pred_labels, self.pred_locs = model.model(self.sess) total_boxes = self.pred_labels.get_shape().as_list()[1] self.positives_ph, self.negatives_ph, self.true_labels_ph, self.true_locs_ph, self.total_loss, self.class_loss, self.loc_loss = \ model.loss(self.pred_labels, self.pred_locs, total_boxes) out_shapes = [out.get_shape().as_list() for out in self.output_tensors] c.out_shapes = out_shapes c.defaults = model.default_boxes(out_shapes) # variables in model are already initialized, so only initialize those declared after with tf.variable_scope("optimizer"): self.global_step = tf.Variable(0) self.lr_ph = tf.placeholder(tf.float32) self.optimizer = tf.train.AdamOptimizer(1e-3).minimize(self.total_loss, global_step=self.global_step) new_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="optimizer") init = tf.variables_initializer(new_vars) self.sess.run(init) if model_dir is None: model_dir = FLAGS.model_dir ckpt = tf.train.get_checkpoint_state(model_dir) self.saver = tf.train.Saver() if ckpt and ckpt.model_checkpoint_path: self.saver.restore(self.sess, ckpt.model_checkpoint_path) print("restored %s" % ckpt.model_checkpoint_path)
