config.py 文件源码

def init_training_mode():
    """  init_training_mode.

    Creates `is_training` variable and its ops if they haven't be created
    yet. This op is required if you are using layers such as dropout or
    batch normalization independently of TFLearn models (DNN or Trainer class).

    """
    # 'is_training' collection stores the training mode variable
    coll = tf.get_collection('is_training')
    if len(coll) == 0:
        tr_var = variable(
            "is_training", dtype=tf.bool, shape=[],
            initializer=tf.constant_initializer(False),
            trainable=False)
        tf.add_to_collection('is_training', tr_var)
        # 'is_training_ops' stores the ops to update training mode variable
        a = tf.assign(tr_var, True)
        b = tf.assign(tr_var, False)
        tf.add_to_collection('is_training_ops', a)
        tf.add_to_collection('is_training_ops', b)