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我应该从PHP中删除表单中的临时文件发送(Should I remove temporary file send from form in PHP)

 我正在玩各种ajax上传者。 在分析他们的服务器端代码时，我看到这样的事情：  
@unlink($_FILES['file']['tmp_name'])
 
 它要么静音（如上所述），所以什么也不做（在我的情况下）或取消静音，所以抛出警告，禁止访问临时文件夹（在我的情况下）并中断脚本的执行。  
 我错过了什么？ 我总是被告知，我们不应该触摸通过PHP表单传输的临时文件 。 因为这是不必要的（并禁止某些时间，就像我的情况一样）。 当脚本结束时，PHP将执行所有清理 - 即删除所有上传的临时文件。  
 上面的代码是什么原因？ 是否适用于这种情况，当脚本中断时，PHP会因某些严重错误而停止，因此无法删除临时文件？ 还是有另一个原因？  
 编辑 ：很遗憾，即使在Plupload示例代码中我也发现了这种错误。 

I'm playing with various ajax uploaders. When analyzing their server-side code, I see something like this: 
@unlink($_FILES['file']['tmp_name'])
 
It is either muted one (like above), so does nothing (in my case) or unmuted one, so throws a warning, that access to temporary folder is prohibited (in my case) and breaks execution of a script. 
What am I missing? I was always told, that we should not touch temporary files transmited via PHP form. Because this is unnecessary (and somethimes prohibited, like in my case). PHP will do all the cleaning, when script ends -- i.e. remove all uploaded temporary files. 
What is the reason in code like above? Is it for the case, when script breaks, PHP is halted with some critical error and thus isn't able to remove temporary files? Or there is another reason? 
Edit: It is quite pity, that I found this kind of mistake even in Plupload example code.

原文：

更新时间：2022-03-18 16:03

最满意答案

 这是一种方法（我假设有一个拼写错误，你想要的是x3 * (mynet(x2) - mynet(x1)) ？）：  
import tensorflow as tf
import numpy as np

class MLP:
    def __init__(self, x1, x2, sizes, activations):
        x_sizes = [tf.shape(x1)[0], tf.shape(x2)[0]]
        last_out = tf.concat([x1, x2], axis=0)
        self.layers = []
        for l, size in enumerate(sizes[1:]):
            self.layers.append(last_out)
            last_out = tf.layers.dense(last_out, size, activation=activations[l], kernel_initializer=tf.glorot_uniform_initializer())
        self.layers.append(last_out)
        self.x1_eval, self.x2_eval = tf.split(last_out, x_sizes, axis=0)



def main():
    session = tf.Session()

    dim = 3
    nn_sizes = [dim, 15, 1]
    nn_activations = [tf.nn.tanh, tf.nn.tanh, tf.identity]

    w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='mynet')
    x1 = tf.placeholder(dtype=tf.float32, shape=[None, dim], name='x1')
    x2 = tf.placeholder(dtype=tf.float32, shape=[None, dim], name='x2')
    x3 = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x3')

    mynet = MLP(x1, x2, nn_sizes, nn_activations)

    myfun = tf.reduce_sum(tf.multiply(x3, (mynet.x2_eval - mynet.x1_eval)))

    optimizer = tf.contrib.opt.ScipyOptimizerInterface(myfun,var_list=w)

    n = 1000
    x1_samples = np.asmatrix(np.random.rand(n,dim))
    x2_samples = np.asmatrix(np.random.rand(n,dim))
    x3_samples = np.asmatrix(np.random.rand(n,1))

    session.run(tf.global_variables_initializer())
    print(session.run(myfun, {x1: x1_samples, x2: x2_samples, x3: x3_samples}))
    optimizer.minimize(session, {x1: x1_samples, x2: x2_samples, x3: x3_samples})
    print(session.run(myfun, {x1: x1_samples, x2: x2_samples, x3: x3_samples}))


if __name__ == '__main__':
    main()

Here's one approach (I assume there is a typo and what you want is x3 * (mynet(x2) - mynet(x1))?): 
import tensorflow as tf
import numpy as np

class MLP:
    def __init__(self, x1, x2, sizes, activations):
        x_sizes = [tf.shape(x1)[0], tf.shape(x2)[0]]
        last_out = tf.concat([x1, x2], axis=0)
        self.layers = []
        for l, size in enumerate(sizes[1:]):
            self.layers.append(last_out)
            last_out = tf.layers.dense(last_out, size, activation=activations[l], kernel_initializer=tf.glorot_uniform_initializer())
        self.layers.append(last_out)
        self.x1_eval, self.x2_eval = tf.split(last_out, x_sizes, axis=0)



def main():
    session = tf.Session()

    dim = 3
    nn_sizes = [dim, 15, 1]
    nn_activations = [tf.nn.tanh, tf.nn.tanh, tf.identity]

    w = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='mynet')
    x1 = tf.placeholder(dtype=tf.float32, shape=[None, dim], name='x1')
    x2 = tf.placeholder(dtype=tf.float32, shape=[None, dim], name='x2')
    x3 = tf.placeholder(dtype=tf.float32, shape=[None, 1], name='x3')

    mynet = MLP(x1, x2, nn_sizes, nn_activations)

    myfun = tf.reduce_sum(tf.multiply(x3, (mynet.x2_eval - mynet.x1_eval)))

    optimizer = tf.contrib.opt.ScipyOptimizerInterface(myfun,var_list=w)

    n = 1000
    x1_samples = np.asmatrix(np.random.rand(n,dim))
    x2_samples = np.asmatrix(np.random.rand(n,dim))
    x3_samples = np.asmatrix(np.random.rand(n,1))

    session.run(tf.global_variables_initializer())
    print(session.run(myfun, {x1: x1_samples, x2: x2_samples, x3: x3_samples}))
    optimizer.minimize(session, {x1: x1_samples, x2: x2_samples, x3: x3_samples})
    print(session.run(myfun, {x1: x1_samples, x2: x2_samples, x3: x3_samples}))


if __name__ == '__main__':
    main()

我应该从PHP中删除表单中的临时文件发送(Should I remove temporary file send from form in PHP)

最满意答案

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