tensorflow: Shapes and Shaping 探究

import tensorflow as tf

t = tf.constant(value=[[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]], dtype=tf.int32)
with tf.Session() as sess:
    print (sess.run(tf.shape(t)))

[2 2 3]

import tensorflow as tf

t = tf.constant(value=[[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]], dtype=tf.int32)
print (t.shape)

(2, 2, 3)

import tensorflow as tf

t = tf.ones(shape=[2, 5, 10], dtype=tf.int32)

with tf.Session() as sess:
    print (sess.run(tf.size(t)))

100

import tensorflow as tf

t = tf.constant(value=[[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]], dtype=tf.int32)
with tf.Session() as sess:
    print (sess.run(tf.rank(t)))

3

import tensorflow as tf

t = tf.constant(value=[1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=tf.int32)
with tf.Session() as sess:
    print (sess.run(t))
    print
    print (sess.run(tf.reshape(t, [3, 3])))

[1 2 3 4 5 6 7 8 9]

[[1 2 3]
 [4 5 6]
 [7 8 9]]

import tensorflow as tf

t = tf.ones(shape=[1, 2, 1, 3, 1, 1], dtype=tf.int32)

with tf.Session() as sess:
    print (sess.run(tf.shape(t)))
    print
    print (sess.run(tf.shape(tf.squeeze(t))))
    print
    print (sess.run(tf.shape(tf.squeeze(t, axis=[2, 4]))))

[1 2 1 3 1 1]

[2 3]

[1 2 3 1]

import tensorflow as tf

t = tf.ones(shape=[2, 3, 5], dtype=tf.int32)

with tf.Session() as sess:
    print (sess.run(tf.shape(t)))
    print
    print (sess.run(tf.shape(tf.expand_dims(t, 0))))
    print
    print (sess.run(tf.shape(tf.expand_dims(t, 1))))
    print
    print (sess.run(tf.shape(tf.expand_dims(t, 2))))
    print
    print (sess.run(tf.shape(tf.expand_dims(t, 3))))
    print
    print (sess.run(tf.shape(tf.expand_dims(t, -1))))

[2 3 5]

[1 2 3 5]

[2 1 3 5]

[2 3 1 5]

[2 3 5 1]

[2 3 5 1]