In [2]:

    

%matplotlib inline


    

In [1]:

    

"""Softmax"""


    

    
Out[1]:





'Softmax'

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scores = [3.0, 1.0, 0.2]


    

In [4]:

    

import numpy as np


    

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def softmax(x):
    """Compute softmax values for x."""
    y = np.exp(x)/np.sum(np.exp(x), axis=0)
    return y


    

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print(softmax(scores))


    

    




[ 0.8360188   0.11314284  0.05083836]

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# Plot softmax curves
import matplotlib.pyplot as plt
x = np.arange(-2.0, 6.0,0.1)
scores = np.vstack([x, np.ones_like(x), 0.2 * np.ones_like(x)])


    

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plt.plot(x, softmax(scores).T, linewidth=2)


    

    
Out[8]:





[<matplotlib.lines.Line2D at 0x7fcb85d39c90>,
 <matplotlib.lines.Line2D at 0x7fcb85d39e90>,
 <matplotlib.lines.Line2D at 0x7fcb85d39fd0>]






    

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plt.show()


    

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# Multiply the scores by 10.  What happens?
scores = np.array([3.0, 1.0, 0.2])
print(softmax(scores* 10))


    

    




[  9.99999998e-01   2.06115362e-09   6.91440009e-13]

In [14]:

    

# Divide the scores by 10.  What happens?
scores = np.array([3.0, 1.0, 0.2])
print(softmax(scores/ 10))


    

    




[ 0.38842275  0.31801365  0.2935636 ]

In [15]:

    

N = 1000000000
a = 0.000001
ITER_MAX = 1000000


    

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for iter in range(ITER_MAX):
    N += a


    

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print( N - 1000000000 )


    

    




0.953674316406

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from decimal import Decimal


    

In [19]:

    

N_Decimal = Decimal(N)
a_Decimal = Decimal(a)
for iter in range(ITER_MAX):
    N_Decimal += a_Decimal


    

In [20]:

    

print( N_Decimal - Decimal( N))


    

    




1.000000000000000000

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