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Let utils.to_categorical support different dtypes. #10846

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Aug 7, 2018
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Let utils.to_categorical support different dtypes. #10846

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6 changes: 4 additions & 2 deletions keras/utils/np_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import numpy as np


def to_categorical(y, num_classes=None):
def to_categorical(y, num_classes=None, dtype='float32'):
"""Converts a class vector (integers) to binary class matrix.

E.g. for use with categorical_crossentropy.
Expand All @@ -15,6 +15,8 @@ def to_categorical(y, num_classes=None):
y: class vector to be converted into a matrix
(integers from 0 to num_classes).
num_classes: total number of classes.
dtype: The data type expected by the input, as a string
(`float32`, `float64`, `int32`...)

# Returns
A binary matrix representation of the input. The classes axis
Expand All @@ -28,7 +30,7 @@ def to_categorical(y, num_classes=None):
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=np.float32)
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
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