TFLearn Implementation of DeXpression architecture. Batch normalization is used instead of LRN. Gives a precision of 99.3 percent, recall of 99.2 percent and f1-score of 99.2 percent on CKPlus Dataset for human emotion recognition from frontal facial images.
TFLearn Implementation of DeXpression architecture.
Batch normalization is used instead of LRN.
Gives a precision of 99.3 percent, recall of 99.2 percent and f1-score of 99.2 percent on CKPlus Dataset for human emotion recognition from frontal facial images.
Please refer to this GitHub Issue #1 for info regarding the dataset creation.
I would like to extract pictures after each layer, same as shown in your papers.
Please help me on this.
Using 10-fold cross-validation on this architecture our best accuracy on the CKP database was around 40% that is on the validation set, ofcourse. I didn't use the code to generate the images because those links are dead. I just resized each image to a 224x244x1 unsigned 8-bit integer.
Is there something we have missed?
Cannot feed value of shape (350,) for Tensor 'TargetsData/Y:0', which has shape '(?, 7)'
when i am trying to feed numpy into network is give the above error? how i can solve this.
thanks
X = np.load('CKP_X.npy')
Y = np.load('CKP_Y.npy')
@arundasan91 have you completed its code? Kindly send me the link of complete code. Thank you
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