abs, fabs Compute the absolute value element-wise for integer, floating-point, or complex values sqrt Compute the square root of each element (equivalent to arr ** 0.5) square Compute the square of each element (equivalent to arr ** 2) exp Compute the exponent ex of each element log, lo...
hypot(x1, x2[, out]) 求直角三角形斜边 arctan2(x1, x2[, out]) Element-wise arc tangent of x1/x2 choosing the quadrant correctly. degrees(x[, out]) 弧度求角度 radians(x[, out]) 角度求弧度 unwrap(p[, discont, axis]) Unwrap by changing deltas between values to 2*pi complement....
不像许多矩阵语言,NumPy中的乘法运算符dot函数或创建矩阵对象实现(参见教程中的矩阵章节) >>> A = array( [[1,1],... [0,1]] )>>> B = array( [[2,0],... [3,4]] )>>> A*B# elementwise product array([[2,0], [0,4]])>>> dot(A,B)# matrix product array([[5,4], [3,...
一种是对应元素相乘,又称为逐元乘法(Element-Wise Product),或哈达玛积(Hadamard Product),运算符为 np.multiply(), 或 *。另一种是点积或内积元素,运算符为np.dot()。 1.3.1逐个元素操作 逐个元素操作(又称为对应元素相乘)是两个矩阵中对应元素乘积。np.multiply 函数用于数组...
σ是element-wise 激活函数,上标T表示矩阵的转置。 def activation(input_, act_func): if act_func == 'relu': return np.maximum(input_,np.zeros(input_.shape)) elif act_func == 'linear': return input_ else: raiseException('Activation function is not defined.') def forward_prop(input_vec...
>>> A = np.array([[1, 1], ... [0, 1]]) >>> B = np.array([[2, 0], ... [3, 4]]) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[...
... [3, 4]])>>> A * B#elementwise productarray([[2, 0], [0,4]])>>> A @ B#matrix productarray([[5, 4], [3, 4]])>>> A.dot(B)#another matrix productarray([[5, 4], [3, 4]]) 某些操作,例如+=and*=,会修改现有数组而不是创建新数组。
式中, ∘ 表示按元素积 (element-wise product), 又称为 Hadamard 积; 1→k 表示维的全1向量 (all-ones vector), 余者类推. 上式中 1→k 的作用是计算 X∘X 每行元素的和, 返回一个列向量; 1→nT 的作用类似于 NumPy 中的广播机制, 在这里是将一个列向量扩展为一个矩阵, 矩阵的每一列都是...
Here, numpy.maximum computed the element-wise maximum of the elements in x and y. While not common, a ufunc can return multiple arrays. modf is one example, a vectorized version of the built-in Python divmod; it returns the fractional and integral parts of a floating-point array: In [...
>>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) 一些运算如,*= 和 += 并不会新创建数组,而是在原有数组上进行修改。