hybrid_learning
Content
Quickstart Guide
User Guide
API Reference
concepts
datasets
fuzzy_logic
experimentation
ca_exp_eval
hybrid_learning.experimentation.ca_exp_eval.ALEXNET_LAYERS
hybrid_learning.experimentation.ca_exp_eval.VGG16_LAYERS
hybrid_learning.experimentation.ca_exp_eval.MASK_RCNN_LAYERS
analysis_root
display_overview
gather_stats
get_all_best_emb_stats
get_all_stats
get_best_emb
get_best_emb_stats
get_common_concepts
get_concepts
get_embs
get_layers
get_stats
get_vis_best_embedding
highlight_max_blue
merge_to_overview
plot_best_iou_comparison
plot_best_ious_wt_std
plot_overview
plot_overview_for_concept
exp_eval_common
fuzzy_exp
model_registry
How to contribute
hybrid_learning
»
API Reference
»
experimentation
»
ca_exp_eval
»
hybrid_learning.experimentation.ca_exp_eval.ALEXNET_LAYERS
View page source
hybrid_learning.experimentation.ca_exp_eval.ALEXNET_LAYERS
hybrid_learning.experimentation.ca_exp_eval.
ALEXNET_LAYERS
:
Tuple
[
str
,
...
]
=
('features.1',
'features.2',
'features.4',
'features.5',
'features.7',
'features.9',
'features.11',
'features.12',
'avgpool')
Layer IDs of pytorch AlexNet model in correct order.