在最后一个维度做softmax操作, 比如:

import tensorflow as tf
import numpy as np

N = 10
C = 2
a = np.random.randn(N, C, C*4) * 100

aa = tf.constant(a)
bb = tf.nn.softmax(aa)

with tf.Session() as ss:
	bbval =
	print(np.sum(bbval, axis=2)) # <== will get all ones' result

Restore pre-trained models

In TensorFlow, a tf.train.Saver is a module to save and restore variables to disk. If we need to specify which variables to restore, we need to pass a variable dict or list to tf.train.Saver. Using list is simple, now we just talk about how to use dict.

name_to_vars = { ... } # key: the variable name saved in the checkpoint file, value: the TensorFlow variable we need to restore.
saver_for_restore = tf.train.Saver(name_to_vars)

Note that in name_to_vars, the key is a string, i.e. the variable name saved in the checkpoint file, not the name you defined in your graph. The value is the TensorFlow variable we need to restore, it is the variable you define in your current graph. See the following code:

def save_ckpt():
    FLAGS.mode = 'train'
    net = CENet3D(FLAGS)

    name_to_vars = {}
    for var in net.all_vars:
        if 'discriminator_im' in
            name ='discriminator_im', 'discriminator')
            name_to_vars[name] = var
        if 'generator_vd' in or 'discriminator_vd' in
            name =
            name_to_vars[name] = var
    saver_only_for_restore = tf.train.Saver(name_to_vars)
    saver_for_save = tf.train.Saver(net.all_vars)
    with tf.Session(config=tfconfig) as sess:
        init_op =, tf.local_variables_initializer())
        ckpt = tf.train.get_checkpoint_state(FLAGS.log_path)
        if ckpt and ckpt.model_checkpoint_path:
            saver_only_for_restore.restore(sess, ckpt.model_checkpoint_path)
            print('model restored...')
  , os.path.join('./', 'initial_weights.ckpt'))
            raise ValueError('no model found!')