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import numpy as np # 创建一个二维数组 arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 使用 np.add.reduce 计算所有元素的总和 total_sum = np.add.reduce(arr) print("Total sum:", total_sum) # 输出: Total sum: 45 # 指定轴进行累积操作 column_sum = np.add.reduce...
例子: importnumpyasnpimportmindsporeimportmindspore.opsasopsfrommindsporeimportTensor x=Tensor(np.random.randn(3,4,5,6).astype(np.float32))op=ops.ReduceSum(keep_dims=True)output=op(x,1)output.shape# case 1: Reduces a dimension by summing all elements in the dimension.x=Tensor(np.array([[...
一个不是很简单,但是很好理解的方法是:你的输入矩阵的shape是(2,2,4),那么当axis=0时,就是在第一个dimension上进行求和,最后得到的结果的shape就是去掉第一个dimension后的shape,也就是(2,4)。具体的计算方法则是,对于c[i,j,k],假设输出矩阵为s[j,k],第一个dimension求和那么就是 s[j,k]=∑i(c...
array([[1,1,1], [1,1,1]], dtype=int32)# sum all the elements# 1 + 1 + 1 + 1 + 1+ 1 = 6tf.reduce_sum(x).numpy()6# reduce along the first dimension# the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]tf.reduce_sum(x,0).numpy() ...
if applyingsess.run(tf.reduce_sum(a,0)), the product’s shape will be(4,5), the dimension length of which is 3 is reduced. appendix import tensorflow as tf import numpy as np a = np.random.rand(3,4,5) a Out[138]: array([[[ 0.67386161, 0.25891236, 0.15750355, 0.99356577, 0.6240...
array([[1, 1, 1], [1, 1, 1]], dtype=int32) 1. 2. # sum all the elements # 1 + 1 + 1 + 1 + 1+ 1 = 6 tf.reduce_sum(x).numpy() 1. 2. 3. # reduce along the first dimension # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2] ...
The output of the above program is:Python NumPy Programs »List to array conversion to use ravel() function Calculate mean across dimension in a 2D array Advertisement Advertisement Related TutorialsHow to create a numpy array of arbitrary length strings? How does python numpy.where() work?
)->"DataArray": """Reduce this array by applying `func` along some dimension(s). Parameters --- func : function Function which can be called in the form `f(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray ...