How can I create custom loss function in Keras which is different for each sample
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I have e.g. 100 samples (100 outputs). I would like to write custom loss function with a "weight" for each sample:
(target[j] - prediction[j])**2 + f(j),
Where f is a custom numeric function (e.g. j**2). How can I do this
Now I am only able to create "universal" loss function (without "weights"):
def customloss(target,prediction):
return (target - prediction)**2
The problem is I cannot get the index (j).
python keras loss
edited Nov 16 '16 at 13:10
Div
4,41092647
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
add a comment |
I have e.g. 100 samples (100 outputs). I would like to write custom loss function with a "weight" for each sample:
(target[j] - prediction[j])**2 + f(j),
Where f is a custom numeric function (e.g. j**2). How can I do this
Now I am only able to create "universal" loss function (without "weights"):
def customloss(target,prediction):
return (target - prediction)**2
The problem is I cannot get the index (j).
python keras loss
edited Nov 16 '16 at 13:10
Div
4,41092647
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
Why dont you add an extra parameterweight? A vector with the weight for each sample.
– Imanol Luengo
Nov 16 '16 at 18:55
The problem is AFAIK weights can only multiply results, so I would get sth like that: (target[j] - prediction[j])**2 * f(j) and I would like to add "weights" instead of multiply by them
– Filip Sokolowski
Nov 18 '16 at 15:56
add a comment |
I have e.g. 100 samples (100 outputs). I would like to write custom loss function with a "weight" for each sample:
(target[j] - prediction[j])**2 + f(j),
Where f is a custom numeric function (e.g. j**2). How can I do this
Now I am only able to create "universal" loss function (without "weights"):
def customloss(target,prediction):
return (target - prediction)**2
The problem is I cannot get the index (j).
python keras loss
edited Nov 16 '16 at 13:10
Div
4,41092647
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
I have e.g. 100 samples (100 outputs). I would like to write custom loss function with a "weight" for each sample:
(target[j] - prediction[j])**2 + f(j),
Where f is a custom numeric function (e.g. j**2). How can I do this
Now I am only able to create "universal" loss function (without "weights"):
def customloss(target,prediction):
return (target - prediction)**2
The problem is I cannot get the index (j).
python keras loss
python keras loss
edited Nov 16 '16 at 13:10
Div
4,41092647
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
edited Nov 16 '16 at 13:10
Div
4,41092647
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
edited Nov 16 '16 at 13:10
Div
4,41092647
edited Nov 16 '16 at 13:10
Div
4,41092647
edited Nov 16 '16 at 13:10
Div
4,41092647
4,41092647
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
asked Nov 16 '16 at 13:01
Filip SokolowskiFilip Sokolowski
61
61
Why dont you add an extra parameterweight? A vector with the weight for each sample.
– Imanol Luengo
Nov 16 '16 at 18:55
The problem is AFAIK weights can only multiply results, so I would get sth like that: (target[j] - prediction[j])**2 * f(j) and I would like to add "weights" instead of multiply by them
– Filip Sokolowski
Nov 18 '16 at 15:56
add a comment |
Why dont you add an extra parameterweight? A vector with the weight for each sample.
– Imanol Luengo
Nov 16 '16 at 18:55
The problem is AFAIK weights can only multiply results, so I would get sth like that: (target[j] - prediction[j])**2 * f(j) and I would like to add "weights" instead of multiply by them
– Filip Sokolowski
Nov 18 '16 at 15:56
Why dont you add an extra parameter
weight? A vector with the weight for each sample.– Imanol Luengo
Nov 16 '16 at 18:55
Why dont you add an extra parameter
weight? A vector with the weight for each sample.– Imanol Luengo
Nov 16 '16 at 18:55
The problem is AFAIK weights can only multiply results, so I would get sth like that: (target[j] - prediction[j])**2 * f(j) and I would like to add "weights" instead of multiply by them
– Filip Sokolowski
Nov 18 '16 at 15:56
The problem is AFAIK weights can only multiply results, so I would get sth like that: (target[j] - prediction[j])**2 * f(j) and I would like to add "weights" instead of multiply by them
– Filip Sokolowski
Nov 18 '16 at 15:56
add a comment |
1 Answer
1
active
oldest
votes
This might not be further relevant, but you can create a second network with an Input Layer. Towards that Input Layer you pass an Array that represents your weights.
Now wrap your model:
weight_layer = Input(shape=(None,dim))
m2 = Model(input=[m1.inputs,weight_layer],output=m1.outputs)
Since the output of a loss functions is also a Tensor you can add the weight_layer to your loss.
e.g.:
def customloss(y_true,y_pred):
return K.binary_crossentropy(y_true,y_pred) + weight_layer
m2.compile(optimizer='adam',loss=customloss,...)
answered Nov 24 '18 at 17:22
mssmss
65
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
This might not be further relevant, but you can create a second network with an Input Layer. Towards that Input Layer you pass an Array that represents your weights.
Now wrap your model:
weight_layer = Input(shape=(None,dim))
m2 = Model(input=[m1.inputs,weight_layer],output=m1.outputs)
Since the output of a loss functions is also a Tensor you can add the weight_layer to your loss.
e.g.:
def customloss(y_true,y_pred):
return K.binary_crossentropy(y_true,y_pred) + weight_layer
m2.compile(optimizer='adam',loss=customloss,...)
answered Nov 24 '18 at 17:22
mssmss
65
add a comment |
This might not be further relevant, but you can create a second network with an Input Layer. Towards that Input Layer you pass an Array that represents your weights.
Now wrap your model:
weight_layer = Input(shape=(None,dim))
m2 = Model(input=[m1.inputs,weight_layer],output=m1.outputs)
Since the output of a loss functions is also a Tensor you can add the weight_layer to your loss.
e.g.:
def customloss(y_true,y_pred):
return K.binary_crossentropy(y_true,y_pred) + weight_layer
m2.compile(optimizer='adam',loss=customloss,...)
answered Nov 24 '18 at 17:22
mssmss
65
add a comment |
This might not be further relevant, but you can create a second network with an Input Layer. Towards that Input Layer you pass an Array that represents your weights.
Now wrap your model:
weight_layer = Input(shape=(None,dim))
m2 = Model(input=[m1.inputs,weight_layer],output=m1.outputs)
Since the output of a loss functions is also a Tensor you can add the weight_layer to your loss.
e.g.:
def customloss(y_true,y_pred):
return K.binary_crossentropy(y_true,y_pred) + weight_layer
m2.compile(optimizer='adam',loss=customloss,...)
answered Nov 24 '18 at 17:22
mssmss
65
This might not be further relevant, but you can create a second network with an Input Layer. Towards that Input Layer you pass an Array that represents your weights.
Now wrap your model:
weight_layer = Input(shape=(None,dim))
m2 = Model(input=[m1.inputs,weight_layer],output=m1.outputs)
Since the output of a loss functions is also a Tensor you can add the weight_layer to your loss.
e.g.:
def customloss(y_true,y_pred):
return K.binary_crossentropy(y_true,y_pred) + weight_layer
m2.compile(optimizer='adam',loss=customloss,...)
answered Nov 24 '18 at 17:22
mssmss
65
edited Feb 25 at 21:25
answered Nov 24 '18 at 17:22
mssmss
65
answered Nov 24 '18 at 17:22
mssmss
65
answered Nov 24 '18 at 17:22
mssmss
65
65
add a comment |
add a comment |
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Why dont you add an extra parameter
weight? A vector with the weight for each sample.– Imanol Luengo
Nov 16 '16 at 18:55
The problem is AFAIK weights can only multiply results, so I would get sth like that: (target[j] - prediction[j])**2 * f(j) and I would like to add "weights" instead of multiply by them
– Filip Sokolowski
Nov 18 '16 at 15:56