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Custom loss function in Keras for weighting missclassified samples
Assume that y_true and y_pred are in [-1,1]. I want a weighted mean-square-error loss function, in which the loss for samples that are positive in the y_true and negative in y_pred or vice versa are weighted by exp(alpha). Here is my code:
import keras.backend as K
alpha = 1.0
def custom_loss(y_true, y_pred):
se = K.square(y_pred-y_true)
true_label = K.less_equal(y_true,0.0)
pred_label = K.less_equal(y_pred,0.0)
return K.mean(se * K.exp(alpha*K.cast(K.not_equal(true_label,pred_label), tf.float32)))
And here is a plot of this loss function. Different curves are for different values for y_true.
I want to know:
- Whether this is a valid loss function, since it is not differentiable in 0?
- Is my code correct?
python keras classification regression loss-function
asked Nov 20 '18 at 14:32
HosseinHossein
961615
add a comment |
Assume that y_true and y_pred are in [-1,1]. I want a weighted mean-square-error loss function, in which the loss for samples that are positive in the y_true and negative in y_pred or vice versa are weighted by exp(alpha). Here is my code:
import keras.backend as K
alpha = 1.0
def custom_loss(y_true, y_pred):
se = K.square(y_pred-y_true)
true_label = K.less_equal(y_true,0.0)
pred_label = K.less_equal(y_pred,0.0)
return K.mean(se * K.exp(alpha*K.cast(K.not_equal(true_label,pred_label), tf.float32)))
And here is a plot of this loss function. Different curves are for different values for y_true.
I want to know:
- Whether this is a valid loss function, since it is not differentiable in 0?
- Is my code correct?
python keras classification regression loss-function
asked Nov 20 '18 at 14:32
HosseinHossein
961615
add a comment |
Assume that y_true and y_pred are in [-1,1]. I want a weighted mean-square-error loss function, in which the loss for samples that are positive in the y_true and negative in y_pred or vice versa are weighted by exp(alpha). Here is my code:
import keras.backend as K
alpha = 1.0
def custom_loss(y_true, y_pred):
se = K.square(y_pred-y_true)
true_label = K.less_equal(y_true,0.0)
pred_label = K.less_equal(y_pred,0.0)
return K.mean(se * K.exp(alpha*K.cast(K.not_equal(true_label,pred_label), tf.float32)))
And here is a plot of this loss function. Different curves are for different values for y_true.
I want to know:
- Whether this is a valid loss function, since it is not differentiable in 0?
- Is my code correct?
python keras classification regression loss-function
asked Nov 20 '18 at 14:32
HosseinHossein
961615
Assume that y_true and y_pred are in [-1,1]. I want a weighted mean-square-error loss function, in which the loss for samples that are positive in the y_true and negative in y_pred or vice versa are weighted by exp(alpha). Here is my code:
import keras.backend as K
alpha = 1.0
def custom_loss(y_true, y_pred):
se = K.square(y_pred-y_true)
true_label = K.less_equal(y_true,0.0)
pred_label = K.less_equal(y_pred,0.0)
return K.mean(se * K.exp(alpha*K.cast(K.not_equal(true_label,pred_label), tf.float32)))
And here is a plot of this loss function. Different curves are for different values for y_true.
I want to know:
- Whether this is a valid loss function, since it is not differentiable in 0?
- Is my code correct?
python keras classification regression loss-function
python keras classification regression loss-function
asked Nov 20 '18 at 14:32
HosseinHossein
961615
asked Nov 20 '18 at 14:32
HosseinHossein
961615
asked Nov 20 '18 at 14:32
HosseinHossein
961615
asked Nov 20 '18 at 14:32
HosseinHossein
961615
asked Nov 20 '18 at 14:32
HosseinHossein
961615
961615
add a comment |
add a comment |
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