在张量流张量中插入平均行

Question

我在numpy中有如下函数：

A = np.array([[1, 2, 3],
          [4, 5, 6],
          [7, 8, 9],
          [10, 11, 12]])


def insert_row_averages(A, n=2):
    slice2 = A[n::n]
    v = (A[n-1::n][:slice2.shape[0]] + slice2)/2.0
    np.insert(A.astype(float),n*np.arange(1,v.shape[0]+1),v,axis=0)

它基本上取上下行的平均值，并每隔 n 间隔将其插入两者之间。

知道如何在 Tensorflow 中完成这项工作吗？具体来说，我可以用什么来代替 np.insert，因为似乎没有等效的功能。

Answer 1

您可以使用基于初始化的方法来解决它，就像这样 -

def insert_row_averages_iniitalization_based(A, n=1):

    # Slice and compute averages   
    slice2 = A[n::n]
    v = (A[n-1::n][:slice2.shape[0]] + slice2)/2.0

    # Compute number of rows for o/p array
    nv = v.shape[0]
    nrows = A.shape[0] + nv

    # Row indices where the v values are the inserted in o/p array
    v_rows = (n+1)*np.arange(nv)+n

    # Initialize o/p array
    out = np.zeros((nrows,A.shape[1]))

    # Insert/assign v in output array
    out[v_rows] = v  # Assign v

    # Create 1D mask of length equal to no. of rows in o/p array, such that its
    # TRUE at places where A is to be assigned and FALSE at places where values
    # from v were assigned in previous step.
    mask = np.ones(nrows,dtype=bool)
    mask[v_rows] = 0

    # Use the mask to assign A.
    out[mask] = A
    return out