Images Score Regression only regresses to the average of the target values
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I have 700 3D images, each one having a target value. The target value distribution after standardizing looks as below
After training, my validation set MSE (10% of data) does not go down and R2 score remains below 0.1 and predicted v.s. real values look like as
What I am seeing is that model is only trying to set all values as the mean value, and cannot get the values away from the mean right. I am using MSE loss and have also tried Huber loss. I have tried normalizing my data to [0,1] and also [-1,1] (enforcing the last 1-neuron layer with a sigmoid or tanh activation function to this range as well) but haven't seen any improvement.
FYI, my architecture is 3 times (conv3d, conv3d, maxpool) + 2 times(dense layer) + a one unit dense layer. Adam optimizer, leaky-relu activations, regularizations and drop outs.
FYI, I have done an extensive hyperparameter study as well, but never any improvements.
Any idea why this is happening, maybe a need of changing my data range?
regression cnn convolution image-preprocessing
asked 12 mins ago
SoyolSoyol
1112
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add a comment |
$begingroup$
I have 700 3D images, each one having a target value. The target value distribution after standardizing looks as below
After training, my validation set MSE (10% of data) does not go down and R2 score remains below 0.1 and predicted v.s. real values look like as
What I am seeing is that model is only trying to set all values as the mean value, and cannot get the values away from the mean right. I am using MSE loss and have also tried Huber loss. I have tried normalizing my data to [0,1] and also [-1,1] (enforcing the last 1-neuron layer with a sigmoid or tanh activation function to this range as well) but haven't seen any improvement.
FYI, my architecture is 3 times (conv3d, conv3d, maxpool) + 2 times(dense layer) + a one unit dense layer. Adam optimizer, leaky-relu activations, regularizations and drop outs.
FYI, I have done an extensive hyperparameter study as well, but never any improvements.
Any idea why this is happening, maybe a need of changing my data range?
regression cnn convolution image-preprocessing
asked 12 mins ago
SoyolSoyol
1112
$endgroup$
add a comment |
$begingroup$
I have 700 3D images, each one having a target value. The target value distribution after standardizing looks as below
After training, my validation set MSE (10% of data) does not go down and R2 score remains below 0.1 and predicted v.s. real values look like as
What I am seeing is that model is only trying to set all values as the mean value, and cannot get the values away from the mean right. I am using MSE loss and have also tried Huber loss. I have tried normalizing my data to [0,1] and also [-1,1] (enforcing the last 1-neuron layer with a sigmoid or tanh activation function to this range as well) but haven't seen any improvement.
FYI, my architecture is 3 times (conv3d, conv3d, maxpool) + 2 times(dense layer) + a one unit dense layer. Adam optimizer, leaky-relu activations, regularizations and drop outs.
FYI, I have done an extensive hyperparameter study as well, but never any improvements.
Any idea why this is happening, maybe a need of changing my data range?
regression cnn convolution image-preprocessing
asked 12 mins ago
SoyolSoyol
1112
$endgroup$
I have 700 3D images, each one having a target value. The target value distribution after standardizing looks as below
After training, my validation set MSE (10% of data) does not go down and R2 score remains below 0.1 and predicted v.s. real values look like as
What I am seeing is that model is only trying to set all values as the mean value, and cannot get the values away from the mean right. I am using MSE loss and have also tried Huber loss. I have tried normalizing my data to [0,1] and also [-1,1] (enforcing the last 1-neuron layer with a sigmoid or tanh activation function to this range as well) but haven't seen any improvement.
FYI, my architecture is 3 times (conv3d, conv3d, maxpool) + 2 times(dense layer) + a one unit dense layer. Adam optimizer, leaky-relu activations, regularizations and drop outs.
FYI, I have done an extensive hyperparameter study as well, but never any improvements.
Any idea why this is happening, maybe a need of changing my data range?
regression cnn convolution image-preprocessing
regression cnn convolution image-preprocessing
asked 12 mins ago
SoyolSoyol
1112
asked 12 mins ago
SoyolSoyol
1112
asked 12 mins ago
SoyolSoyol
1112
asked 12 mins ago
SoyolSoyol
1112
asked 12 mins ago
SoyolSoyol
1112
1112
add a comment |
add a comment |
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