from tensorflow.python import pywrap_tensorflow

reader = pywrap_tensorflow.NewCheckpointReader(pre_train_model_path)

# 获取当前图可训练变量
trainable_variables = tf.trainable_variables()
# 需要赋值的当前网络层变量，这里只是随便起的名字。
restore_v_target_name = "fc_target"
# 需要的预训练模型中的某层的名字
restore_v_source_name = "fc_source"
for v in trainable_variables:
  if restore_v_target_name == v.name:
   # 回复weights和biases
    sess.run(
      tf.assign(v, reader.get_tensor(restore_v_source_name + "/weights"))) if "weights" in v.name else sess.run(
      tf.assign(v, reader.get_tensor(restore_v_source_name + "/biases")))