我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用tensorflow.train()。
def add_training_op(self, loss): #optimizer = tf.train.AdamOptimizer(self.config.lr) #optimizer = tf.train.AdagradOptimizer(self.config.lr) optclass = getattr(tf.train, self.config.optimizer + 'Optimizer') assert issubclass(optclass, tf.train.Optimizer) optimizer = optclass(self.config.learning_rate) gradient_var_pairs = optimizer.compute_gradients(loss) vars = [x[1] for x in gradient_var_pairs] gradients = [x[0] for x in gradient_var_pairs] if self.config.gradient_clip > 0: clipped, _ = tf.clip_by_global_norm(gradients, self.config.gradient_clip) else: clipped = gradients self.grad_norm = tf.global_norm(clipped) train_op = optimizer.apply_gradients(zip(clipped, vars)) return train_op
def start_server_if_distributed(self): """Starts a server if the execution is distributed.""" if self.cluster: logging.info("%s: Starting trainer within cluster %s.", task_as_string(self.task), self.cluster.as_dict()) server = start_server(self.cluster, self.task) target = server.target device_fn = tf.train.replica_device_setter( ps_device="/job:ps", worker_device="/job:%s/task:%d" % (self.task.type, self.task.index), cluster=self.cluster) else: target = "" device_fn = "" return (target, device_fn)
def get_meta_filename(self, start_new_model, train_dir): if start_new_model: logging.info("%s: Flag 'start_new_model' is set. Building a new model.", task_as_string(self.task)) return None latest_checkpoint = tf.train.latest_checkpoint(train_dir) if not latest_checkpoint: logging.info("%s: No checkpoint file found. Building a new model.", task_as_string(self.task)) return None meta_filename = latest_checkpoint + ".meta" if not gfile.Exists(meta_filename): logging.info("%s: No meta graph file found. Building a new model.", task_as_string(self.task)) return None else: return meta_filename
def start_server(cluster, task): """Creates a Server. Args: cluster: A tf.train.ClusterSpec if the execution is distributed. None otherwise. task: A TaskSpec describing the job type and the task index. """ if not task.type: raise ValueError("%s: The task type must be specified." % task_as_string(task)) if task.index is None: raise ValueError("%s: The task index must be specified." % task_as_string(task)) # Create and start a server. return tf.train.Server( tf.train.ClusterSpec(cluster), protocol="grpc", job_name=task.type, task_index=task.index)
def __init__(self, cluster, task, train_dir, log_device_placement=True): """"Creates a Trainer. Args: cluster: A tf.train.ClusterSpec if the execution is distributed. None otherwise. task: A TaskSpec describing the job type and the task index. """ self.cluster = cluster self.task = task self.is_master = (task.type == "master" and task.index == 0) self.train_dir = train_dir gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu) self.config = tf.ConfigProto(log_device_placement=log_device_placement) if self.is_master and self.task.index > 0: raise StandardError("%s: Only one replica of master expected", task_as_string(self.task))
def __init__(self, cluster, task, train_dir, log_device_placement=True): """"Creates a Trainer. Args: cluster: A tf.train.ClusterSpec if the execution is distributed. None otherwise. task: A TaskSpec describing the job type and the task index. """ self.cluster = cluster self.task = task self.is_master = (task.type == "master" and task.index == 0) self.train_dir = train_dir self.config = tf.ConfigProto(log_device_placement=log_device_placement) if self.is_master and self.task.index > 0: raise StandardError("%s: Only one replica of master expected", task_as_string(self.task))
def get_input_data_tensors(reader, data_pattern, batch_size=256, num_epochs=None): logging.info("Using batch size of " + str(batch_size) + " for training.") with tf.name_scope("train_input"): files = gfile.Glob(data_pattern) if not files: raise IOError("Unable to find training files. data_pattern='" + data_pattern + "'.") logging.info("Number of training files: %s.", str(len(files))) files.sort() filename_queue = tf.train.string_input_producer( files, num_epochs=num_epochs, shuffle=False) training_data = reader.prepare_reader(filename_queue) return tf.train.batch( training_data, batch_size=batch_size, capacity=FLAGS.batch_size * 4, allow_smaller_final_batch=True, enqueue_many=True)
def build_model(self, model, reader): """Find the model and build the graph.""" label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])() optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train]) build_graph(reader=reader, model=model, optimizer_class=optimizer_class, clip_gradient_norm=FLAGS.clip_gradient_norm, train_data_pattern=FLAGS.train_data_pattern, label_loss_fn=label_loss_fn, base_learning_rate=FLAGS.base_learning_rate, learning_rate_decay=FLAGS.learning_rate_decay, learning_rate_decay_examples=FLAGS.learning_rate_decay_examples, regularization_penalty=FLAGS.regularization_penalty, num_readers=FLAGS.num_readers, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs) return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=0.25)
def build_model(self, model, reader): """Find the model and build the graph.""" label_loss_fn = find_class_by_name(FLAGS.label_loss, [losses])() optimizer_class = find_class_by_name(FLAGS.optimizer, [tf.train]) build_graph(reader=reader, model=model, optimizer_class=optimizer_class, clip_gradient_norm=FLAGS.clip_gradient_norm, train_data_pattern=FLAGS.train_data_pattern, label_loss_fn=label_loss_fn, base_learning_rate=FLAGS.base_learning_rate, learning_rate_decay=FLAGS.learning_rate_decay, learning_rate_decay_examples=FLAGS.learning_rate_decay_examples, regularization_penalty=FLAGS.regularization_penalty, num_readers=FLAGS.num_readers, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs) return tf.train.Saver(max_to_keep=0, keep_checkpoint_every_n_hours=2.0)
