小编Red*_*yed的帖子

受tf.keras.Model子类化的启发,我创建了自定义模型.
我可以训练它并获得成功的结果,但我无法保存它.
我使用python3.6和tensorflow v1.10(或v1.9)

这里的最小完整代码示例:

import tensorflow as tf
from tensorflow.keras.datasets import mnist


class Classifier(tf.keras.Model):
    def __init__(self):
        super().__init__(name="custom_model")

        self.batch_norm1 = tf.layers.BatchNormalization()
        self.conv1 = tf.layers.Conv2D(32, (7, 7))
        self.pool1 = tf.layers.MaxPooling2D((2, 2), (2, 2))

        self.batch_norm2 = tf.layers.BatchNormalization()
        self.conv2 = tf.layers.Conv2D(64, (5, 5))
        self.pool2 = tf.layers.MaxPooling2D((2, 2), (2, 2))

    def call(self, inputs, training=None, mask=None):
        x = self.batch_norm1(inputs)
        x = self.conv1(x)
        x = tf.nn.relu(x)
        x = self.pool1(x)

        x = self.batch_norm2(x)
        x = self.conv2(x)
        x = tf.nn.relu(x)
        x = self.pool2(x)

        return …

如何获得后台进程的返回值？如果我这样做,我会得到0

#!/bin/bash
SomeCommand&
echo $?

输出: #~0

但是,如果我尝试

SomeCommand
echo $?

输出: #~255

我读了那个等命令,但是如果那样做的话

SomeCommand$
wait $!
echo $?

如果上一个命令没有完成,我无法运行下一个命令.