I am working on epilepsy seizure prediction. I have imbalanced dataset I want to make it balanced by using focal loss. I have 2 classes one-hot encoding vector. I found the below focal loss code but I don't know how I can get y_pred
to be used in focal loss code before model.fit_generator
.
y_pred
is the output of the model. So how I can use it in the focal loss code before fitting my model??
focal loss code:
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
"""
def focal_loss(y_true, y_pred):
# 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
return focal_loss
My code:
history=model.fit_generator(generate_arrays_for_training(indexPat, train_data, start=0,end=100)
validation_data=generate_arrays_for_training(indexPat, test_data, start=0,end=100)
steps_per_epoch=int((len(train_data)/2)),
validation_steps=int((len(test_data)/2)),
verbose=2,epochs=65, max_queue_size=2, shuffle=True)
preictPrediction=model.predict_generator(generate_arrays_for_predict(indexPat, filesPath_data), max_queue_size=4, steps=len(filesPath_data))
y_pred1=np.argmax(preictPrediction,axis=1)
y_pred=list(y_pred1)
From the comment section for the benefit of the community.
This is not specific to
focal loss
, all keras loss functions takey_true
andy_pred
, you do not need to worry where those parameters are coming from, they are fed bykeras
automatically.