keras.np_utils.to_categorical()其作用是将整形的标签转换为onehot编码。第二个参数num_class的作用是指定标签总类别，若不指定则默认。
```
import keras
ohl=keras.utils.to_categorical([1,3])
# ohl=keras.utils.to_categorical([[1],[3]])
print(ohl)
"""
[[0. 1. 0. 0.]
[0. 0. 0. 1.]]
"""
ohl=keras.utils.to_categorical([1,3],num_classes=5)
print(ohl)
"""
[[0. 1. 0. 0. 0.]
[0. 0. 0. 1. 0.]]
"""
```
这部分的源码如下
```
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.
# Arguments
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
is placed last.
"""
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
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)
return categorical
```
源码里面可以看出，若不指定num_class，则其结果就是max(y)+1。