tf.reduce_sum()方法深度解析

最新推荐文章于 2022-05-15 10:46:54 发布

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最新推荐文章于 2022-05-15 10:46:54 发布 · 2.3k 阅读

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#python #机器学习 #deep learning #tensorflow #reduce_sum

这篇博客详细解析了TensorFlow中的tf.reduce_sum()函数，介绍了如何在不同维度上进行求和操作。当axis参数为None时，函数对所有元素求和并返回一个数值。当设置axis并保持keepdims为False时，函数会沿着指定轴进行求和，改变了矩阵的维度。文章通过实例解释了axis参数在高维矩阵中的作用，展示了不同设置下的运算结果。

首先看一下reduce_sum及其参数的注释 :

def tf.reduce_sum(input_tensor, axis=None, keepdims=False, name=None)

Computes the sum of elements across dimensions of a tensor.

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

If axis is None, all dimensions are reduced, and a tensor with a
single element is returned.

1 参数只有一个矩阵 input_tensor，其余参数为默认

下面我们用三个张量为例(下统一称为矩阵)，对一维向量，二维矩阵，三维矩阵分别执行其余参数为默认的reduce_sum()运算。

x_dim_1 = tf.constant([1,2,3])
z = tf.reduce_sum(x_dim_1)
print("1D sum :")
print(z)

x_dim_2 = tf.constant([[1,2,3],[4,5,6]])
z = tf.reduce_sum(x_dim_2)
print("2D sum :")