reduce_mean函数reduce_mean函数 reduce_mean函数是求取均值的函数,它主要有三个参数: input:一个张量;axis:表示沿着哪个维度计算均值,如果不指定,则计算所有元素的均值;keep_dims:表示是否保留输入张量形状中与指定轴相关的维度,如果是True,则保留,否则去除。
xx = tf.cast(x,tf.float32) mean_all = tf.reduce_mean(xx, keep_dims=False) mean_0 = tf.reduce_mean(xx, axis=0, keep_dims=False) mean_1 = tf.reduce_mean(xx, axis=1, keep_dims=False) with tf.Session() as sess: m_a,m_0,m_1 = sess.run([mean_all, mean_0, mean_1])...
The result of ReduceMean function applied to src tensor. shape[i] = shapeOf[data](i) for all i that is not in the list of axes from the second input. For dimensions from axes, shape[i] == 1 if keep_dims == True, or i-th dimension is removed from the dst otherwise. ...
.square()tf.square(a)是对a里的每一个元素求平方tf.reduce_mean()函数用于计算张量tensor沿着指定的数轴(tensor的某一维度)上的的平均值,主要用作降维或者计算tensor...结合起来即采用解析几何的手法,向量也可以表示为某种坐标基向量和坐标分量的线性组合。 其实,标量,向量,矩阵它们三个也是张量,标量是零位的张量...
c=tf.reduce_mean(a) d=sess.run(c) print(a) print(d) c1=tf.reduce_mean(a1) d1=sess.run(c1) print(a1) print(d1) 总结:tf.reduce_mean(a,axis)是均值,其中a是输入矩阵,axis是从什么维度求均值。然而,代码运行发现,a为浮点数,返回为浮点数,a为整数,返回为向下取的整数。
对于一维矢量,它看起来像np.mean == tf.reduce_mean,但我不明白tf.reduce_mean(x, 1) ==> [1., 2.]中发生了什么。tf.reduce_mean(x, 0) ==> [1.5, 1.5]类型是有道理的,因为[1,2]和[1,2]的平均值是[1.5,1.5],但是tf.reduce_mean(x,1)是怎么回事?
Public sector organizations have a challenge ahead in the next 2-5 years: they must do more with less and transform along the way. But it’s a challenge, with opportunities to serve citizens with new services. One such opportunity is to reduce the incident mean time to contain ...
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MPSImageReduceColumnMean MPSImageReduceColumnMin MPSImageReduceColumnSum MPSImageReduceRowMax MPSImageReduceRowMean MPSImageReduceRowMin MPSImageReduceRowSum MPSImageReduceUnary MPSImageRegion MPSImageScale MPSImageSobel MPSImageStatisticsMean MPSImageStatisticsMeanAndVariance MPSImageStatisticsMinAndMax MPSImageSub...
np.mean(image,-1)这里的-1是什么意思?是对行求平均,还是对列求平均?我在网上只搜到了reduce_mean后面只能有0和1慕婉清4872412 2018-06-21 17:37:27 源自:3-4 逻辑回归模型训练和评价02 1986 分享 收起 1回答 Erik_Song 2018-06-21 17:55:49 这是一个三维矩阵,把最里面的rgb三个值求平均 0 ...