Custom Weighted Cross Entropy loss in KerasHow to log Keras loss output to a fileCustom loss function in KerasTensorFlow: Implementing a class-wise weighted cross entropy loss?Weighted mse custom loss function in kerasSoftmax Cross Entropy with Weights over Samples in TensorflowKeras custom loss function with different weights per exampleCustom Keras Loss Function with Internal PredictionCustom Loss function Keras combining Cross entropy loss and mae loss for Ordinal ClassificationCustom binary cross-entropy loss with weight-map using Keras
How do I fix the group tension caused by my character stealing and possibly killing without provocation?
Quoting Keynes in a lecture
Why is so much work done on numerical verification of the Riemann Hypothesis?
Why do ¬, ∀ and ∃ have the same precedence?
Is it allowed to activate the ability of multiple planeswalkers in a single turn?
Why can't the Brexit deadlock in the UK parliament be solved with a plurality vote?
What features enable the Su-25 Frogfoot to operate with such a wide variety of fuels?
How could a planet have erratic days?
How can ping know if my host is down
Is this part of the description of the Archfey warlock's Misty Escape feature redundant?
Is there a nicer/politer/more positive alternative for "negates"?
What is the English pronunciation of "pain au chocolat"?
What kind of floor tile is this?
"It doesn't matter" or "it won't matter"?
Review your own paper in Mathematics
What does Apple's new App Store requirement mean
Has any country ever had 2 former presidents in jail simultaneously?
Why should universal income be universal?
Are Captain Marvel's powers affected by Thanos breaking the Tesseract and claiming the stone?
Is there a RAID 0 Equivalent for RAM?
Does Doodling or Improvising on the Piano Have Any Benefits?
Doesn't the system of the Supreme Court oppose justice?
"Oh no!" in Latin
Does "he squandered his car on drink" sound natural?
Custom Weighted Cross Entropy loss in Keras
How to log Keras loss output to a fileCustom loss function in KerasTensorFlow: Implementing a class-wise weighted cross entropy loss?Weighted mse custom loss function in kerasSoftmax Cross Entropy with Weights over Samples in TensorflowKeras custom loss function with different weights per exampleCustom Keras Loss Function with Internal PredictionCustom Loss function Keras combining Cross entropy loss and mae loss for Ordinal ClassificationCustom binary cross-entropy loss with weight-map using Keras
Ok, so I have a neural network that classifies fire size into 3 groups, 0-1, 1 - 100, and over 100 acres. I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
tensorflow machine-learning keras neural-network loss-function
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
add a comment |
Ok, so I have a neural network that classifies fire size into 3 groups, 0-1, 1 - 100, and over 100 acres. I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
tensorflow machine-learning keras neural-network loss-function
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
add a comment |
Ok, so I have a neural network that classifies fire size into 3 groups, 0-1, 1 - 100, and over 100 acres. I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
tensorflow machine-learning keras neural-network loss-function
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
Ok, so I have a neural network that classifies fire size into 3 groups, 0-1, 1 - 100, and over 100 acres. I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
tensorflow machine-learning keras neural-network loss-function
tensorflow machine-learning keras neural-network loss-function
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
asked Mar 7 at 5:04
Dylan WichmanDylan Wichman
31
31
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
double of what?.
A)Is it the double the loss value when the classifier guesses correctly,
B)or double the loss value when the classifier is off by 1.
C)Can we relax this 'double' constraint, and can we assume that any suitable higher power would suffice?
Let us assume A).
Let f(x) denotes the probability that your input variable belong to a particular class. Note that, in f(x), x is the absolute value of the difference in categorical value.
Then we see that f(0)=0.5 is a solution for assumption A. This means that f(1)=0.25 and f(2)=0.25. Btw, the fact that f(1)==f(2) doesn't look natural.
Assume that your classifier calculates a function f(x), and uses it as follows.
def classifier_output(firesize):
if (firesize >=0 and firesize < 1.0):
return [f(0), f(1), f(2)]
elif (firesize >= 1.0 and firesize < 100.0):
return [f(1), f(0), f(1)]
else :
assert(firesize > 100.0)
return (f(2), f(1), f(0)]
The constraints are
C1)
f(x) >=0
C2)
the components of your output vector should always sum to 1.0
ie. sum of all three components of the return value should always be 1.
C3)
When the true class and predicted class differ by 2, the 1-hot encoding loss
will be -log(f(2)), According to assumption A, this should equal -2log(f(0)).
ie:
log(f(2))=2*log(f(0))
This translates to
f(2) = f(0)*f(0)
Let us put z=f(0). Now f(2)=z*z. We don't know f(1). Let us assume, f(1)=y.
From the constraint C2,
We have the following equations,
z+ z*z + y=1
z + 2*y=1
A solution to the above is z=0.5, y=0.25
If you assume B), you wont be able to find such a function.
answered Mar 7 at 16:11
koshy georgekoshy george
453517
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55036428%2fcustom-weighted-cross-entropy-loss-in-keras%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
double of what?.
A)Is it the double the loss value when the classifier guesses correctly,
B)or double the loss value when the classifier is off by 1.
C)Can we relax this 'double' constraint, and can we assume that any suitable higher power would suffice?
Let us assume A).
Let f(x) denotes the probability that your input variable belong to a particular class. Note that, in f(x), x is the absolute value of the difference in categorical value.
Then we see that f(0)=0.5 is a solution for assumption A. This means that f(1)=0.25 and f(2)=0.25. Btw, the fact that f(1)==f(2) doesn't look natural.
Assume that your classifier calculates a function f(x), and uses it as follows.
def classifier_output(firesize):
if (firesize >=0 and firesize < 1.0):
return [f(0), f(1), f(2)]
elif (firesize >= 1.0 and firesize < 100.0):
return [f(1), f(0), f(1)]
else :
assert(firesize > 100.0)
return (f(2), f(1), f(0)]
The constraints are
C1)
f(x) >=0
C2)
the components of your output vector should always sum to 1.0
ie. sum of all three components of the return value should always be 1.
C3)
When the true class and predicted class differ by 2, the 1-hot encoding loss
will be -log(f(2)), According to assumption A, this should equal -2log(f(0)).
ie:
log(f(2))=2*log(f(0))
This translates to
f(2) = f(0)*f(0)
Let us put z=f(0). Now f(2)=z*z. We don't know f(1). Let us assume, f(1)=y.
From the constraint C2,
We have the following equations,
z+ z*z + y=1
z + 2*y=1
A solution to the above is z=0.5, y=0.25
If you assume B), you wont be able to find such a function.
answered Mar 7 at 16:11
koshy georgekoshy george
453517
add a comment |
I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
double of what?.
A)Is it the double the loss value when the classifier guesses correctly,
B)or double the loss value when the classifier is off by 1.
C)Can we relax this 'double' constraint, and can we assume that any suitable higher power would suffice?
Let us assume A).
Let f(x) denotes the probability that your input variable belong to a particular class. Note that, in f(x), x is the absolute value of the difference in categorical value.
Then we see that f(0)=0.5 is a solution for assumption A. This means that f(1)=0.25 and f(2)=0.25. Btw, the fact that f(1)==f(2) doesn't look natural.
Assume that your classifier calculates a function f(x), and uses it as follows.
def classifier_output(firesize):
if (firesize >=0 and firesize < 1.0):
return [f(0), f(1), f(2)]
elif (firesize >= 1.0 and firesize < 100.0):
return [f(1), f(0), f(1)]
else :
assert(firesize > 100.0)
return (f(2), f(1), f(0)]
The constraints are
C1)
f(x) >=0
C2)
the components of your output vector should always sum to 1.0
ie. sum of all three components of the return value should always be 1.
C3)
When the true class and predicted class differ by 2, the 1-hot encoding loss
will be -log(f(2)), According to assumption A, this should equal -2log(f(0)).
ie:
log(f(2))=2*log(f(0))
This translates to
f(2) = f(0)*f(0)
Let us put z=f(0). Now f(2)=z*z. We don't know f(1). Let us assume, f(1)=y.
From the constraint C2,
We have the following equations,
z+ z*z + y=1
z + 2*y=1
A solution to the above is z=0.5, y=0.25
If you assume B), you wont be able to find such a function.
answered Mar 7 at 16:11
koshy georgekoshy george
453517
add a comment |
I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
double of what?.
A)Is it the double the loss value when the classifier guesses correctly,
B)or double the loss value when the classifier is off by 1.
C)Can we relax this 'double' constraint, and can we assume that any suitable higher power would suffice?
Let us assume A).
Let f(x) denotes the probability that your input variable belong to a particular class. Note that, in f(x), x is the absolute value of the difference in categorical value.
Then we see that f(0)=0.5 is a solution for assumption A. This means that f(1)=0.25 and f(2)=0.25. Btw, the fact that f(1)==f(2) doesn't look natural.
Assume that your classifier calculates a function f(x), and uses it as follows.
def classifier_output(firesize):
if (firesize >=0 and firesize < 1.0):
return [f(0), f(1), f(2)]
elif (firesize >= 1.0 and firesize < 100.0):
return [f(1), f(0), f(1)]
else :
assert(firesize > 100.0)
return (f(2), f(1), f(0)]
The constraints are
C1)
f(x) >=0
C2)
the components of your output vector should always sum to 1.0
ie. sum of all three components of the return value should always be 1.
C3)
When the true class and predicted class differ by 2, the 1-hot encoding loss
will be -log(f(2)), According to assumption A, this should equal -2log(f(0)).
ie:
log(f(2))=2*log(f(0))
This translates to
f(2) = f(0)*f(0)
Let us put z=f(0). Now f(2)=z*z. We don't know f(1). Let us assume, f(1)=y.
From the constraint C2,
We have the following equations,
z+ z*z + y=1
z + 2*y=1
A solution to the above is z=0.5, y=0.25
If you assume B), you wont be able to find such a function.
answered Mar 7 at 16:11
koshy georgekoshy george
453517
I need a loss function that weights the loss as double when the classifier guesses a class that is off by 2 (Actual = 0, predicted = 3)
double of what?.
A)Is it the double the loss value when the classifier guesses correctly,
B)or double the loss value when the classifier is off by 1.
C)Can we relax this 'double' constraint, and can we assume that any suitable higher power would suffice?
Let us assume A).
Let f(x) denotes the probability that your input variable belong to a particular class. Note that, in f(x), x is the absolute value of the difference in categorical value.
Then we see that f(0)=0.5 is a solution for assumption A. This means that f(1)=0.25 and f(2)=0.25. Btw, the fact that f(1)==f(2) doesn't look natural.
Assume that your classifier calculates a function f(x), and uses it as follows.
def classifier_output(firesize):
if (firesize >=0 and firesize < 1.0):
return [f(0), f(1), f(2)]
elif (firesize >= 1.0 and firesize < 100.0):
return [f(1), f(0), f(1)]
else :
assert(firesize > 100.0)
return (f(2), f(1), f(0)]
The constraints are
C1)
f(x) >=0
C2)
the components of your output vector should always sum to 1.0
ie. sum of all three components of the return value should always be 1.
C3)
When the true class and predicted class differ by 2, the 1-hot encoding loss
will be -log(f(2)), According to assumption A, this should equal -2log(f(0)).
ie:
log(f(2))=2*log(f(0))
This translates to
f(2) = f(0)*f(0)
Let us put z=f(0). Now f(2)=z*z. We don't know f(1). Let us assume, f(1)=y.
From the constraint C2,
We have the following equations,
z+ z*z + y=1
z + 2*y=1
A solution to the above is z=0.5, y=0.25
If you assume B), you wont be able to find such a function.
answered Mar 7 at 16:11
koshy georgekoshy george
453517
edited Mar 7 at 16:43
answered Mar 7 at 16:11
koshy georgekoshy george
453517
answered Mar 7 at 16:11
koshy georgekoshy george
453517
answered Mar 7 at 16:11
koshy georgekoshy george
453517
453517
add a comment |
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55036428%2fcustom-weighted-cross-entropy-loss-in-keras%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
