added batchsize arg to fit and explained how num train obs is calculated

episodes/01-introduction.md

@@ -207,7 +207,8 @@ We can now go ahead and start training our neural network. We will probably keep
 ```python
 # fit the model
 history_intro = model_intro.fit(train_images, train_labels, epochs = 10, 
-                                validation_data = (val_images, val_labels))
+                                validation_data = (val_images, val_labels),
+                                batch_size=32)
 
 # save the model
 model_intro.save('fit_outputs/model_intro.h5')
@@ -222,13 +223,13 @@ Epoch 1/10
 
 **What does this output mean?**
 
-This output printed during the fit phase i.e. training the model against known image labels, can be broken down as follows:
+This output printed during the fit phase, i.e. training the model against known image labels, can be broken down as follows:
 
-- `Epoch` descibes the number of full passes over all *training data*. In the Output above there are **1563 training observations**. An epoch will conclude and move to the next epoch after a training pass over all 1563 observations.
+- `Epoch` describes the number of full passes over all *training data*. In the output above there are **1563 training observations**. This number is calculated as the total number of images used as input divided by the batch size (50000/32). An epoch will conclude and move to the next epoch after a training pass over all 1563 observations.
 
 - `loss` and `val_loss` can be considered as related. Where `loss` is a value the model will attempt to minimise, and is the distance between the true label of an image and the models prediction. Minimising this distance is where *learning* occurs to adjust weights and bias which reduce `loss`. On the other hand `val_loss` is a value calculated against the validation data and is a measurement of the models performance against **unseen data**. Both values are a summation of errors made for each example when fitting to the training or validation sets.
 
-- `accuracy` and `val_accuracy` can also be considered as related. Unlike `loss` and `val_loss`, these values are a percentage and are only revelent to **classification problems**. The `val_accuracy` score can be used to communicate a percentage value of model effectiveness on unseen data.
+- `accuracy` and `val_accuracy` can also be considered as related. Unlike `loss` and `val_loss`, these values are a percentage and are only revelant to **classification problems**. The `val_accuracy` score can be used to communicate a percentage value of model effectiveness on unseen data.
 
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