Hi,

I was experimenting with the number of neurons in the prediction layer for the week 2 ungraded lab notebook. Inline with exercise 4.

When using neurons that is less than the number of class in the fashion mnist data, the model is not able give a proper prediction, but when i pass in more neurons than the number of class in the data, the model’s is still able to accurately predict the class.

So i suppose my question why does it work even though i have more neurons, in the prediction layer, than the classes in the dataset? and would i ever get to a case where the model predicts a label that does not exist in the actual dataset?

```
import tensorflow as tf
# load the data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
# normalize the image data
normalized_x_train = x_train / 255.0
normalized_x_test = x_test / 255.0
# define the model
exercise_4_model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=512, activation=tf.keras.activations.relu),
tf.keras.layers.Dense(units=1024, activation=tf.keras.activations.relu),
tf.keras.layers.Dense(units=30, activation=tf.keras.activations.sigmoid)]) # i have more neurons than i do classes in the dataset
# compile the model
exercise_4_model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy())
#metrics=[tf.keras.metrics.Accuracy()]) i purposefully commented this out to get past the error when trying to calculate accuracy
# train the model
history = exercise_4_model.fit(normalized_x_train, y_train, epochs=5)
# evaluate the model
results = exercise_4_model.evaluate(normalized_x_test, y_test)
print(f"trained model loss on the test data: {results:.2f}")
predicted_probability = exercise_4_model.predict(normalized_x_test)
predicted_class = np.argmax(predicted_probability[60])
print(f"predicted probability distribution: {predicted_probability[60]}")
print(f"predicted_class: {predicted_class}, actual class: {y_test[60]}")
```