小编Jac*_*Dow的帖子

这是我的 PhpUnit 测试类：

<?php

namespace tests\AppBundle\Controller;

use Symfony\Bundle\FrameworkBundle\Test\WebTestCase;

class SendEmailControllerTest extends WebTestCase
{
    public function testMailIsSentAndContentIsCorrect()
    {
        $client = static:: createClient();

        ...
    }
}

但是当我尝试运行它时，我得到了一个错误，其跟踪是：

Unable to guess the Kernel directory.
 C:\myProject\vendor\symfony\symfony\src\Symfony\Bundle\FrameworkBundle\Test\KernelTestCase.php:62
 C:\myProject\vendor\symfony\symfony\src\Symfony\Bundle\FrameworkBundle\Test\KernelTestCase.php:138
 C:\myProject\vendor\symfony\symfony\src\Symfony\Bundle\FrameworkBundle\Test\KernelTestCase.php:184
 C:\myProject\vendor\symfony\symfony\src\Symfony\Bundle\FrameworkBundle\Test\KernelTestCase.php:165
 C:\myProject\vendor\symfony\symfony\src\Symfony\Bundle\FrameworkBundle\Test\WebTestCase.php:33
 C:\myProject\tests\AppBundle\Controller\SendEmailControllerTest.php:12

ERRORS!
Tests: 1, Assertions: 0, Errors: 1.

Remaining deprecation notices (3)

  1x: Using the KERNEL_DIR environment variable or the automatic guessing based on the phpunit.xml / phpunit.xml.dist file location is deprecated since Symfony 3.4. Set the KERNEL_CLASS environment variable to the fully-qualified class …

I've tried to save a basic MNIST model:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

saver = tf.train.Saver()

sess.run(tf.global_variables_initializer())

y = tf.matmul(x, W) + b
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

saver.save(sess, './mnist_to-save-saved')

for _ in range(1000):
    batch = mnist.train.next_batch(100)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) …