Change "TensorFlow" to "PyTorch" when discussing early stopping

Medbeji · May 6, 2019 · 2351fa4 · 2351fa4
1 parent ee339a6
commit 2351fa4
Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/evaluation_methods.ipynb b/evaluation_methods.ipynb
@@ -608,7 +608,7 @@
     "\n",
     "The key to addressing this uncertainty is to __regularly collect information about dev set performance as part of training__. For example, at every 100th iteration, one could make predictions on the dev set and store that vector of predictions, or just whatever assessment metric one is using. These assessments can provide direct information about how the model is doing on the actual task we care about, which will be a better indicator than the errors.\n",
     "\n",
-    "All the TensorFlow classifiers for this course accept keyword arguments `X_dev` and `dev_iter`. If these are specified, then the model is tested every `test_iter` iteration and the resulting predictions are stored in the class attribute `dev_predictions`. Here's an example:"
+    "All the PyTorch models for this course accept keyword arguments `X_dev` and `dev_iter`. If these are specified, then the model is tested every `test_iter` iteration and the resulting predictions are stored in the class attribute `dev_predictions`. Here's an example:"
    ]
   },
   {
@@ -922,7 +922,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,