activation.py
1.68 KB
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import math
import numpy as np
def softmax(inputA):
result = []
sigmaSum = 0
normalized_arr = []
for x in inputA:
normalized_arr.append(float(x))
normalized_arr = normalize(normalized_arr)
for i in range(0, len(normalized_arr)):
powA = pow(math.e, normalized_arr[i])
sigmaSum = sigmaSum + powA
for i in range(0, len(normalized_arr)):
powB = pow(math.e, normalized_arr[i])
resultA = powB / sigmaSum
result.append(resultA)
#result = normalize(result)
return result
def softmax2(input2):
result = []
sigmaSum = 0
normalized_arr = []
'''
for x in inputA:
normalized_arr.append(float(x))
normalized_arr = normalize(normalized_arr)
for i in range(0, len(normalized_arr)):
powA = pow(math.e, normalized_arr[i])
sigmaSum = sigmaSum + powA
for i in range(0, len(normalized_arr)):
powB = pow(math.e, normalized_arr[i])
resultA = powB / sigmaSum
result.append(resultA)
'''
result = np.exp(normalized_arr) / sum(np.exp(normalized_arr))
#result = normalize(result)
return result
def normalize(arrs):
normalized_arr = []
for x in arrs:
normalized_arr.append(float(x))
if len(normalized_arr) > 0:
maximum = max(normalized_arr)
minimum = min(normalized_arr)
denom = float(maximum) - float(minimum)
if denom == 0:
denom = 1
for i in range(0,len(normalized_arr)):
normalized_arr[i] = ((normalized_arr[i] - minimum)/ denom) * 2 - 1
return normalized_arr