Comments (2)
I'd like to know this as well.
from dec-keras.
to find out which cluster new data belongs to, get the embedded representations for all the images and perform a PCA on the entire set of embedded training data. Then use model.encoder.predict(X) or model.get_layer(index=0).predict(X) (depending on if you are loading a saved model) to get the embedded representation of the new image. Use the same PCA object (important!) on the embedding of the new image to get the coordinates and plot it along with the training data (different colors of course)
from dec-keras.
Related Issues (16)
- Normalized Mutual Information (NMI) and Generalizability (G) HOT 1
- Multi GPU Support
- Wrong ending condition HOT 3
- visualize HOT 2
- new dataset HOT 1
- How to insert the dataset?
- DeprecationWarning: linear_assignment_ module is deprecated
- ValueError: too many values to unpack (expected 2) HOT 2
- What is the accuracy obtained with this Keras implementation? HOT 5
- from DEC import ClusteringLayer
- The loss didn't converge to zero during fine-tunning in my own data. HOT 1
- Getting a 'initial_value must be specified' ValueError HOT 2
- Question: Unknown number of clusters? HOT 2
- DEC in TensorFlow 1.3; AttributeError: can't set attribute
- loss: nan HOT 2
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from dec-keras.