Comments (2)
After 8th epoch the heatmap loss weight increases from 0.1 to 0.9. This increases the heatmap loss weightage and overall loss value can be high. Hope this helps
from deeptemporalclustering.
if epoch > self.finetune_heatmap_at_epoch:
print('On epoch end fine tune heatmap', self.finetune_heatmap_at_epoch)
K.set_value(self.heatmap_loss_weight, self.final_heatmap_loss_weight)
from deeptemporalclustering.
Related Issues (20)
- Dimension Reduction HOT 7
- Assertion error HOT 5
- Heatmap HOT 3
- Nan: Predicted Value HOT 2
- ValueError: The name "reshape" is used 2 times in the model. All layer names should be unique. HOT 3
- CuDNNLSTM not found HOT 3
- Training and Validation Losses HOT 1
- ValueError: Input 0 is incompatible with layer AE: expected shape=(None, 5210, 6), found shape=(None, 6) HOT 3
- Problem with Autoencoder Dimensions HOT 2
- Heatmap use HOT 2
- input shape HOT 2
- how to load model.h5 HOT 2
- Heatmap issue HOT 4
- Loss interpretation
- Agglomerative Clustering without n_clusters HOT 1
- Dependency Problems with cudnn and Tensorflow HOT 1
- Practical Use
- Requirements are hard to find out HOT 1
- variable time step HOT 4
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