I would to reconstruct all the layers from the VGG network, with fully-connected layers.
In the *.mat file, the layer with id '37' corresponds to the first FC layer.
Weights and biases have shapes : [7,7,512,4096] and [1,4096].
The input tensor is from 'avgpool5' :
Tensor("VGG_3/avgpool_5/AvgPool:0", shape=(1, 7, 7, 512), dtype=float32)
This shape (for W) seems to correspond to an operation that combines "flatten + matmul" .. but I don't find an operation with TensorFlow which can handle those variables ..
After, for the next FC layers, weights and biases have normal shapes :
Hi,
I would to reconstruct all the layers from the VGG network, with fully-connected layers. In the *.mat file, the layer with id '37' corresponds to the first FC layer. Weights and biases have shapes : [7,7,512,4096] and [1,4096].
The input tensor is from 'avgpool5' :
Tensor("VGG_3/avgpool_5/AvgPool:0", shape=(1, 7, 7, 512), dtype=float32)
This shape (for W) seems to correspond to an operation that combines "flatten + matmul" .. but I don't find an operation with TensorFlow which can handle those variables ..
After, for the next FC layers, weights and biases have normal shapes :
Did you find a solution to reconstruct the whole network ?
I'm creating a workshop about Style Transfer, with TensorFlow. And I would like to give an example of classification with the whole VGG.
Thanks