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 4 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 4 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 4 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 4 mins ago
SoyolSoyol
1112
asked 4 mins ago
SoyolSoyol
1112
asked 4 mins ago
SoyolSoyol
1112
asked 4 mins ago
SoyolSoyol
1112
asked 4 mins ago
SoyolSoyol
1112
1112
add a comment |
add a comment |
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