In [4]:

    

import numpy as np
def softmax(x):
    x = np.asarray(x)
    """Compute softmax values for each sets of scores in x."""
    if len(x.shape)>1:
        
        x_t = np.transpose(x)
        out = None
        for k in range(x_t.shape[0]):
            d = np.sum([np.exp(i) for i in x_t[k]])
            res = np.asarray([np.exp(j)/d for j in x_t[k]])
            if out==None:
                out = res
            else:
                out=np.vstack((out,res))
        return np.transpose(out)
    else:
        d = np.sum([np.exp(i) for i in x])
        return np.asarray([np.exp(j)/d for j in x])


    

In [6]:

    

softmax([[1,2,3],[1,2,3]]
       )


    

    
Out[6]:





array([[ 0.5,  0.5,  0.5],
       [ 0.5,  0.5,  0.5]])

In [ ]: