message: Fixes GitHub PR and other typos in training.py
diff: @@ -706,7 +706,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  This method can be overridden to support custom training logic.
  This method is called by `Model.make_train_function`.
  - This method should contain the mathemetical logic for one step of training.
  + This method should contain the mathematical logic for one step of training.
  This typically includes the forward pass, loss calculation, backpropagation,
  and metric updates.
  @@ -878,7 +878,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  The model will not be trained on this data. Thus, note the fact
  that the validation loss of data provided using `validation_split`
  or `validation_data` is not affected by regularization layers like
  - noise and dropuout.
  + noise and dropout.
  `validation_data` will override `validation_split`.
  `validation_data` could be:
  - tuple `(x_val, y_val)` of Numpy arrays or tensors
  @@ -1131,7 +1131,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  This method can be overridden to support custom evaluation logic.
  This method is called by `Model.make_test_function`.
  - This function should contain the mathemetical logic for one step of
  + This function should contain the mathematical logic for one step of
  evaluation.
  This typically includes the forward pass, loss calculation, and metrics
  updates.
  @@ -1383,7 +1383,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  This method can be overridden to support custom inference logic.
  This method is called by `Model.make_predict_function`.
  - This method should contain the mathemetical logic for one step of inference.
  + This method should contain the mathematical logic for one step of inference.
  This typically includes the forward pass.
  Configuration details for *how* this logic is run (e.g. `tf.function` and
  @@ -1921,7 +1921,7 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  This allows you to save the entirety of the state of a model
  in a single file.
  - Saved models can be reinstantiated via `keras.models.load_model`.
  + Saved models can be re-instantiated via `keras.models.load_model`.
  The model returned by `load_model` is a compiled model ready to be used
  (unless the saved model was never compiled in the first place).
  