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custom_metrics.py
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custom_metrics.py
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from tensorflow.keras import backend as K
def recall_m(y_true, y_pred):
'''
method that computes the Recall of a numpy vector of predicted values, with
respect to the vector of the true values of the samples predicted
'''
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision_m(y_true, y_pred):
'''
method that computes the Precision of a numpy vector of predicted values, with
respect to the vector of the true values of the samples predicted
'''
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def f1_m(y_true, y_pred):
'''
method that computes the F1-Score of a numpy vector of predicted values, with
respect to the vector of the true values of the samples predicted
'''
precision = precision_m(y_true, y_pred)
recall = recall_m(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
def focal_loss(y_true, y_pred, gamma=2.0, alpha=0.25):
'''
method that computes the Focal Loss of a numpy vector of predicted values, with
respect to the vector of the true values of the samples predicted
'''
# Define epsilon so that the backpropagation will not result in NaN
# for 0 divisor case
epsilon = K.epsilon()
# Add the epsilon to prediction value
#y_pred = y_pred + epsilon
# Clip the prediction value
y_pred = K.clip(y_pred, epsilon, 1.0-epsilon)
# Calculate cross entropy
cross_entropy = -y_true*K.log(y_pred)
# Calculate weight that consists of modulating factor and weighting factor
weight = alpha * y_true * K.pow((1-y_pred), gamma)
# Calculate focal loss
loss = weight * cross_entropy
# Sum the losses in mini_batch
loss = K.sum(loss, axis=1)
return loss
def categorical_focal_loss(gamma=2.0, alpha=0.25):
"""
Implementation of Focal Loss from the paper in multiclass classification
Formula:
loss = -alpha*((1-p)^gamma)*log(p)
Parameters:
alpha -- the same as wighting factor in balanced cross entropy
gamma -- focusing parameter for modulating factor (1-p)
Default value:
gamma -- 2.0 as mentioned in the paper
alpha -- 0.25 as mentioned in the paper
"""
return focal_loss