thanks for the nice presentation, when i load the embedding facenet model by tf.keras.models.load_model('facenet_keras.h5') with tensorflow 2.2, it keeps raising error (ValueError: bad marshal data (unknown type code), may i know is there any idea to mitigate it?
The idea of the FaceNet is to embed the face image to a d-dim feature vector based on any currently existing CNN networks using the proposed triplet loss. This decouples the feature extraction and classification stages. You can refer to our video at time about 7'57''.
Yes, you are right. In general, the last layer of the network for feature embedding is the fully connected layer and its output is the embedded feature.
Best explanation.. thank you so much
You are so welcome
You are so talented
Thanks a lot
@@quarter2018 How can i khow how many hidden layer used in facenet?
thanks for the nice presentation, when i load the embedding facenet model by tf.keras.models.load_model('facenet_keras.h5') with tensorflow 2.2, it keeps raising error (ValueError: bad marshal data (unknown type code), may i know is there any idea to mitigate it?
I am not familiar with keras and has no idea about how to address your problem.
i know Im kind of randomly asking but do anybody know a good place to stream new movies online ?
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@Augustus Gregory you are welcome :)
Is d in deep cnn mean the number of hidden layer that used?
$d$ is the number of neurons used in the output layer that makes your output is a $d$-dimensional feature vector.
How can i khow how many hidden layer used in facenet?
The number of hidden layers is dependent on your application.
@@quarter2018 can i talk with you on whatsapp or any way if you can.
And thank you for your answer😍🌹🌺
@@waleedaiad3411 We will open a google meeting (meet.google.com/xya-vuys-vfo) for discussion at 10:00 PM ~ 10:30 PM (UTC+8) every Wednesday.
@@quarter2018 ok i will open with you
Thanks for your answer🌺🌹
How is the embedding(feature vector) comes from?
The idea of the FaceNet is to embed the face image to a d-dim feature vector based on any currently existing CNN networks using the proposed triplet loss.
This decouples the feature extraction and classification stages.
You can refer to our video at time about 7'57''.
@@quarter2018 Thank you very much. Is it the output of the fully connected layer of CNN? I'm a machine learning beginner.
Yes, you are right. In general, the last layer of the network for feature embedding is the fully connected layer and its output is the embedded feature.
@@布丁-z8m can we use softmax loss or any loss function instead of triplet loss?