Tests pass

GilesStrong · Mar 11, 2021 · cec49b9 · cec49b9
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diff --git a/CHANGES.md b/CHANGES.md
@@ -2,7 +2,7 @@
 
 ## Important changes
 
-- Fixed bug in `Model.set_mom`  which resulted in momentum never being set (affects e.g. OneCycle and CyclicalMom)
+- Fixed bug in `Model.set_mom` which resulted in momentum never being set (affects e.g. OneCycle and CyclicalMom)
 - `Model.fit` now shuffles the fold indices for training folds prior to each epoch rather than once per training; removes the periodicity in training loss which was occasionally apparent.
 - Bugs found in `OneCycle`:
     - When training multiple models, the initial LR for subsequent models was the end LR of the previous model (list in partial was being mutated)
@@ -15,7 +15,7 @@
 
 - Mish activation function
 - `Model.fit_params.val_requires_grad` to control whether to compute validation epoch with gradient, default zero, built some losses might require it in the future
-- `ParameterisedPrediction` now stores copies of values for parameterised features in case they change, or need to be changed locally during prediction.
+- `ParameterisedPrediction` now stores copies of values for parametrised features in case they change, or need to be changed locally during prediction.
 - `freeze_layers` and `unfreeze_layers` methods for `Model`
 - `PivotTraining` callback implementing Learning to Pivot [Louppe, Kagan, & Kranmer, 2016](https://papers.nips.cc/paper/2017/hash/48ab2f9b45957ab574cf005eb8a76760-Abstract.html)
     - New example reimplementing paper's jets example
@@ -24,7 +24,7 @@
 - `train_models` now has arguments to:
     - Exclude specific fold indices from training and validation
     - Train models on unique folds, e.g. when training 5 models on a file with 10 folds, each model would be trained on their own unique pair of folds
-- Added discussion of core concept in LUMIN to the docs
+- Added discussion of core concepts in LUMIN to the docs
 
 ## Removals
 

diff --git a/examples/Advanced_Model_Building.ipynb b/examples/Advanced_Model_Building.ipynb
@@ -896,7 +896,7 @@
     "from lumin.nn.metrics.class_eval import AMS\n",
     "\n",
     "cb_partials = [partial(OneCycle, lengths=(5, 10), lr_range=[1e-4, 1e-2])]\n",
-    "eval_metrics = {'AMS':AMS(n_total=250000, br=10, wgt_name='gen_orig_weight')}"
+    "metric_partials = [partial(AMS,n_total=250000, br=10, wgt_name='gen_orig_weight', main_metric=False)]"
    ]
   },
   {
@@ -1013,7 +1013,7 @@
     "                                                model_builder=model_builder,\n",
     "                                                bs=bs,\n",
     "                                                cb_partials=cb_partials,\n",
-    "                                                eval_metrics=eval_metrics,\n",
+    "                                                metric_partials=metric_partials,\n",
     "                                                n_epochs=15)"
    ]
   },
@@ -1422,7 +1422,7 @@
     "                                                model_builder=model_builder,\n",
     "                                                bs=bs,\n",
     "                                                cb_partials=cb_partials,\n",
-    "                                                eval_metrics=eval_metrics,\n",
+    "                                                metric_partials=metric_partials,\n",
     "                                                n_epochs=15)"
    ]
   },

diff --git a/examples/Binary_Classification_Signal_versus_Background.ipynb b/examples/Binary_Classification_Signal_versus_Background.ipynb
diff --git a/examples/Multi_Target_Regression_Di-tau_momenta.ipynb b/examples/Multi_Target_Regression_Di-tau_momenta.ipynb
diff --git a/examples/Multiclass_Classification_Signal_versus_Backgrounds.ipynb b/examples/Multiclass_Classification_Signal_versus_Backgrounds.ipynb
diff --git a/examples/RNNs_CNNs_and_GNNs_for_matrix_data.ipynb b/examples/RNNs_CNNs_and_GNNs_for_matrix_data.ipynb