I have so many Nan values in my output data and I padded those values with zeros. Please don't suggest me to delete Nan or impute with any other no. I want model to skip those nan positions.
example:
x = np.arange(0.5, 30)
x.shape = [10, 3]
x = [[ 0.5 1.5 2.5]
[ 3.5 4.5 5.5]
[ 6.5 7.5 8.5]
[ 9.5 10.5 11.5]
[12.5 13.5 14.5]
[15.5 16.5 17.5]
[18.5 19.5 20.5]
[21.5 22.5 23.5]
[24.5 25.5 26.5]
[27.5 28.5 29.5]]
y = np.arange(2, 10, 0.8)
y.shape = [10, 1]
y[4, 0] = 0.0
y[6, 0] = 0.0
y[7, 0] = 0.0
y = [[2. ]
[2.8]
[3.6]
[4.4]
[0. ]
[6. ]
[0. ]
[0. ]
[8.4]
[9.2]]
I expect keras deep learning model to predict zeros for 5th, 7th and 8th row as similar to the padded value in 'y'.
tensor = [0, 1, 2, 3]mask = np.array([True, False, True, False]) tf.boolean_mask(tensor, mask). Thanks!