Tensorflow 解惑[一]:关于tf.identity 和tf.control_dependencies
2018-04-02 19:23
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Tensorflow 解惑[一]:关于tf.identity 和tf.control_dependencies
最近在stackoverflow上看到一个问题,链接是In TensorFlow, what is tf.identity used for?。最高票的答案贴了两段代码,说明了使用tf.identity后,才会得到累加的效果,但并未解释其中原因,这篇文章做一些解释,有不对的地方还请大神指点。首先,从tensor的定义说起,官网的解释为:A tensor is a generalization of vectors and matrices to potentially higher dimensions. tensor可以是Variable,Constant,Placeholder等等。但是,官网上还有一句话值得注意:Unlike tf.Tensor objects, a tf.Variable exists outside the context of a single session.run call.这个说明了variable的特殊性,这也就是 y = x 失效,而 y = tf.identity(x)有效的原因。同时,对于tf.control_dependencies,官网上有这么句话:control_inputs: A list of Operation or Tensor objects which must be executed or computed before running the operations defined in the context. 继续看下面的代码:
import tensorflow as tf x = tf.Variable(0.0) print x x_plus_1 = tf.assign_add(x, 1) with tf.control_dependencies([x_plus_1]): y = x print y #z=tf.identity(x,name='x') init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(5): print(sess.run(y))运行的结果为:
<tf.Variable 'Variable:0' shape=() dtype=float32_ref> <tf.Variable 'Variable:0' shape=() dtype=float32_ref> 2018-01-06 17:44:49.511158: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA 0.0 0.0 0.0 0.0 0.0此时y的类型为tf.Variable,不受tf.control_dependencies控制。我们把y=x换成y=x+0.0,代码变为:
import tensorflow as tf x = tf.Variable(0.0) print x x_plus_1 = tf.assign_add(x, 1) with tf.control_dependencies([x_plus_1]): y = x + 0.0 print y #z=tf.identity(x,name='x') init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(5): print(sess.run(y))再次运行代码,输出为:
<tf.Variable 'Variable:0' shape=() dtype=float32_ref> Tensor("add:0", shape=(), dtype=float32) 2018-01-06 18:01:28.295217: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA 1.0 2.0 3.0 4.0 5.0此时,y的类型变成了tensor类型,受tf.control_dependencies的约束,生成y之前,进行了5次自加操作。最后,换成y = tf.identity(x,name='x')。代码如下:
import tensorflow as tf x = tf.Variable(0.0) print x x_plus_1 = tf.assign_a c41a dd(x, 1) with tf.control_dependencies([x_plus_1]): y = tf.identity(x,name='x') print y #z=tf.identity(x,name='x') init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for i in range(5): print(sess.run(y))运行代码,输出为:
/Users/xuyao/anaconda3/envs/python/bin/python /Users/xuyao/PycharmProjects/Test/test_tf_identity.py <tf.Variable 'Variable:0' shape=() dtype=float32_ref> 2018-01-06 18:51:58.121838: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA Tensor("x:0", shape=(), dtype=float32) 1.0 2.0 3.0 4.0 5.0tf.identity属于tensorflow中的一个ops,跟x = x + 0.0的性质一样,返回一个tensor,受到tf.control_dependencies的约束,所以生效。
至此,文章开头提到的困惑就可以解答了。
作者:phusFuNs
链接:https://www.jianshu.com/p/6304b7c7896b
來源:简书
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