tf.placeholder的(None,）与（None,None）

本文注重介绍tf.placeholder的shape中None的表达
注意下面的shape

import tensorflow as tf
input = tf.placeholder(tf.float32)
input1=tf.placeholder(tf.float32,(None,))  ##注意(None,)
input3=tf.placeholder(tf.float32,(None))  ##注意(None)
input2=tf.placeholder(tf.float32,(None,None))  ##注意(None,None)
output = tf.shape(input)
output1 = tf.shape(input1)
output2 = tf.shape(input2)
output3 = tf.shape(input3)
with tf.Session() as sess:
    print(sess.run(output, feed_dict={input:[[[3.]]]})) ##注意input
    print(sess.run(output, feed_dict={input:[[3.]]})) ##注意input
    print(sess.run(output, feed_dict={input:[4.]})) ##注意input
    print(sess.run(output1, feed_dict={input1:[4.]})) ##注意input
    print(sess.run(output2, feed_dict={input2: [[4.]]})) ##注意input
    print(sess.run(output3, feed_dict={input3: [4.]})) ##注意input

下面都会报错：

import tensorflow as tf
input = tf.placeholder(tf.float32,(None,))  #注意(None,)
output = tf.shape(input)
with tf.Session() as sess:
    print(sess.run(output, feed_dict={input:[[1.]]}))  ##注意input

import tensorflow as tf
input = tf.placeholder(tf.float32,(None,None)) #注意(None,None)
output = tf.shape(input)
with tf.Session() as sess:
    print(sess.run(output, feed_dict={input:[[[1.]]]}))  ##注意input