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    Tensorflow矩阵运算实例(矩阵相乘,点乘,行/列累加)(2)

    栏目:代码类 时间:2020-02-05 21:11

    4. 行/列累加

    a_2d = tf.constant([1]*6, shape=[2, 3])
    d_2d_1 = tf.reduce_sum(a_2d, axis=0)
    d_2d_2 = tf.reduce_sum(a_2d, axis=1)
    a_3d = tf.constant([1]*12, shape=[2, 2, 3])
    d_3d_1 = tf.reduce_sum(a_3d, axis=1)
    d_3d_2 = tf.reduce_sum(a_3d, axis=2)
    a_4d = tf.constant([1]*24, shape=[2, 2, 2, 3])
    d_4d_1 = tf.reduce_sum(a_4d, axis=2)
    d_4d_2 = tf.reduce_sum(a_4d, axis=3)
     
    with tf.Session() as sess:
     tf.global_variables_initializer().run()
     print("# a_2d 行累加得到shape:{}\n{}".format(d_2d_1.eval().shape, d_2d_1.eval()))
     print("# a_2d 列累加得到shape:{}\n{}".format(d_2d_2.eval().shape, d_2d_2.eval()))
     print("# a_3d 行累加得到shape:{}\n{}".format(d_3d_1.eval().shape, d_3d_1.eval()))
     print("# a_3d 列累加得到shape:{}\n{}".format(d_3d_2.eval().shape, d_3d_2.eval()))
     print("# a_4d 行累加得到shape:{}\n{}".format(d_4d_1.eval().shape, d_4d_1.eval()))
     print("# a_4d 列累加得到shape:{}\n{}".format(d_4d_2.eval().shape, d_4d_2.eval()))
    

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