wehub-resource-sync
42 lines
1.0 KiB
Python
42 lines
1.0 KiB
Python
from __future__ import division
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import numpy as np
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from mlfromscratch.utils import accuracy_score
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from mlfromscratch.deep_learning.activation_functions import Sigmoid
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class Loss(object):
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def loss(self, y_true, y_pred):
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return NotImplementedError()
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def gradient(self, y, y_pred):
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raise NotImplementedError()
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def acc(self, y, y_pred):
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return 0
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class SquareLoss(Loss):
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def __init__(self): pass
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def loss(self, y, y_pred):
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return 0.5 * np.power((y - y_pred), 2)
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def gradient(self, y, y_pred):
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return -(y - y_pred)
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class CrossEntropy(Loss):
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def __init__(self): pass
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def loss(self, y, p):
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# Avoid division by zero
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p = np.clip(p, 1e-15, 1 - 1e-15)
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return - y * np.log(p) - (1 - y) * np.log(1 - p)
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def acc(self, y, p):
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return accuracy_score(np.argmax(y, axis=1), np.argmax(p, axis=1))
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def gradient(self, y, p):
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# Avoid division by zero
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p = np.clip(p, 1e-15, 1 - 1e-15)
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return - (y / p) + (1 - y) / (1 - p)
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