I'm trying:
target = keras.utils.to_categorical([0], num_classes)
This is giving me:
[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
What I want to do, however, is create something like:
[
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.],
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.],
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
...
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.],
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
]
where it will have 10,000 rows.
Using the function numpy.repeat should solve the problem:
target = np.array(target)
numpy.repeat(target , 10000, axis=0)
you specify the array, number of times you want to repeat each axis, and the axis.
axis parameter, That's why I had to use tile and reshapeimport keras
import numpy as np
num_classes = 10
num_rows = 10000
target = keras.utils.to_categorical(np.random.choice(num_classes,num_rows), num_classes)
Use np.tile and reshape. In the answer below, use n=10000 to get your desired answer
target = np.array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
n = 2
target_new = np.tile(target, 2).reshape(n, len(target[0]))
# array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
Go with basic
import numpy as np
np.arange(100).reshape(5,2,10)
# output:
array([[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35, 36, 37, 38, 39]],
[[40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59]],
[[60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
[70, 71, 72, 73, 74, 75, 76, 77, 78, 79]],
[[80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]])
run it.
you may add random, ones, zeros, empty etc it's on you how you wanna go
