Fonction Softmax Python
def softmax(x):
return np.exp(x) / np.sum(np.exp(x), axis=0)
Victorious Vole
def softmax(x):
return np.exp(x) / np.sum(np.exp(x), axis=0)
import numpy as np
def softmax(x):
"""Calculates the softmax for each row of the input x.
Your code should work for a row vector and also for matrices of shape (m,n).
Argument:
x -- A numpy matrix of shape (m,n)
Returns:
s -- A numpy matrix equal to the softmax of x, of shape (m,n)
"""
#(≈ 3 lines of code)
# Apply exp() element-wise to x. Use np.exp(...).
# x_exp = ...
# Create a vector x_sum that sums each row of x_exp. Use np.sum(..., axis = 1, keepdims = True).
# x_sum = ...
# Compute softmax(x) by dividing x_exp by x_sum. It should automatically use numpy broadcasting.
# s = ...
# YOUR CODE STARTS HERE
x_exp = np.exp(x)
x_sum = np.sum(x_exp, axis=1, keepdims=True)
s=x_exp/x_sum
# YOUR CODE ENDS HERE
return s