MSE vs Cross Entropy for training with facial landmark (pose) heatmaps
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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 7 mins ago
azmathazmath
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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 7 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 7 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 7 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 7 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 7 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 7 mins ago
azmathazmath
101
asked 7 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 |
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