I wrote a softmax regression function def softmax_1(x) that essentially takes in a m x n matrix, exponentiates the matrix, then sums the exponentials of each column.
x = np.arange(-2.0, 6.0, 0.1)
scores = np.vstack([x, np.ones_like(x), 0.2 * np.ones_like(x)])
#scores shape is (3, 80)
def softmax_1(x):
"""Compute softmax values for each sets of scores in x."""
return(np.exp(x)/np.sum(np.exp(x),axis=0))
Converting it into a DataFrame I have to transpose
DF_activation_1 = pd.DataFrame(softmax_1(scores).T,index=x,columns=["x","1.0","0.2"])
So I wanted to try and make a version of the softmax function that takes in the transposed version and computes the softmax function
scores_T = scores.T
#scores_T shape is (80,3)
def softmax_2(y):
return(np.exp(y/np.sum(np.exp(y),axis=1)))
DF_activation_2 = pd.DataFrame(softmax_2(scores_T),index=x,columns=["x","1.0","0.2"])
Then I get this error:
Traceback (most recent call last):
File "softmax.py", line 22, in <module>
DF_activation_2 = pd.DataFrame(softmax_2(scores_T),index=x,columns=["x","1.0","0.2"])
File "softmax.py", line 18, in softmax_2
return(np.exp(y/np.sum(np.exp(y),axis=1)))
ValueError: operands could not be broadcast together with shapes (80,3) (80,)
Why doesn't this work when I transpose and switch the axis in the np.sum method?