Improving the clothing image classifier with data augmentation

Armed with several common augmentation methods, we now apply them to train our image classifier on a small dataset in the following steps:

1. We start by constructing a small training set:

   >>> n_small = 500
   >>> X_train = X_train[:n_small]
   >>> train_labels = train_labels[:n_small]
   >>> print(X_train.shape)
   (500, 28, 28, 1)
   

   We only use 500 samples for training.

2. We architect the CNN model using the Keras Sequential API:

   >>> model = models.Sequential()
   >>> model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
   >>> model.add(layers.MaxPooling2D((2, 2)))
   >>> model.add(layers.Conv2D(64, (3, 3), activation='relu'))
   >>> model.add(layers.Flatten())
   >>> model.add(layers.Dense(32, activation='relu'))
   >>> model.add(layers.Dense(10, activation...