keras np_utils() function
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from keras.utils import np_utils
N_CLASSES = 3
label = [0,0,0,1,1,1,2,2,2]
train_label = np_utils.to_categorical(label, N_CLASSES)train_label
Out[21]:
array([[1., 0., 0.],
[1., 0., 0.],
[1., 0., 0.],
[0., 1., 0.],
[0., 1., 0.],
[0., 1., 0.],
[0., 0., 1.],
[0., 0., 1.],
[0., 0., 1.]], dtype=float32)
Similar to tf.one_hot()