MSE vs Cross Entropy for training with facial landmark (pose) heatmaps
$begingroup$
I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.
Ground Truth and Loss
For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.
I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!
loss = tf.losses.mean_squared_error(
predictions=heatmaps, labels=labels_tensor
)
When I tried with cross entropy, I am getting better results. But they are not sharper
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')
gtmap means groundtruth map.
tensorflow cnn loss-function heatmap
asked 11 mins ago
azmathazmath
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.
Ground Truth and Loss
For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.
I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!
loss = tf.losses.mean_squared_error(
predictions=heatmaps, labels=labels_tensor
)
When I tried with cross entropy, I am getting better results. But they are not sharper
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')
gtmap means groundtruth map.
tensorflow cnn loss-function heatmap
asked 11 mins ago
azmathazmath
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.
Ground Truth and Loss
For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.
I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!
loss = tf.losses.mean_squared_error(
predictions=heatmaps, labels=labels_tensor
)
When I tried with cross entropy, I am getting better results. But they are not sharper
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')
gtmap means groundtruth map.
tensorflow cnn loss-function heatmap
asked 11 mins ago
azmathazmath
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to reimplement the excellent paper https://github.com/1adrianb/face-alignment-training in tensorflow. I have successfully defined the network and downloaded the LSD3D-W dataset. I am able to train the model however I am running into a serious issue.
Ground Truth and Loss
For training, I generate ground truths by converting the x,y landmark coordinates into gaussian heatmaps where x,y is the mean of the gaussian.
I first trained with MSE loss as given in the original implementation. After some iterations, the loss becomes extremely small but the output is completely white!
loss = tf.losses.mean_squared_error(
predictions=heatmaps, labels=labels_tensor
)
When I tried with cross entropy, I am getting better results. But they are not sharper
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=heatmaps, labels=labels_tensor), name= 'cross_entropy_loss')
gtmap means groundtruth map.
tensorflow cnn loss-function heatmap
tensorflow cnn loss-function heatmap
asked 11 mins ago
azmathazmath
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
azmathazmath
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
azmathazmath
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 mins ago
azmathazmath
101
asked 11 mins ago
azmathazmath
101
101
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
azmath is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
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: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
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
});
}
});
azmath is a new contributor. Be nice, and check out our Code of Conduct.
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%2fdatascience.stackexchange.com%2fquestions%2f51048%2fmse-vs-cross-entropy-for-training-with-facial-landmark-pose-heatmaps%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
azmath is a new contributor. Be nice, and check out our Code of Conduct.
azmath is a new contributor. Be nice, and check out our Code of Conduct.
azmath is a new contributor. Be nice, and check out our Code of Conduct.
azmath is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Data Science Stack Exchange!
- 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.
Use MathJax to format equations. MathJax reference.
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%2fdatascience.stackexchange.com%2fquestions%2f51048%2fmse-vs-cross-entropy-for-training-with-facial-landmark-pose-heatmaps%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
