如何同时对 X_train 和 y_train 应用增强

Question

I am facing this problem of creating a dataset from a very few images.

Both input ( X_train ) and output ( y_train ) contains (28x28) size images such as MNIST. For example in my code:

(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_train = X_train.astype('float32')
y_train=X_train

datagen = ImageDataGenerator(zca_whitening=True)

How can I fit this datagen to both X_train and y_train simultaneously and save them in a dataset array. Don't want to pass it to training.

Thank you for the help

Answer 1

Beware that augmentation per se is not applied on the target variable y_train but only on the input variables X_train . The generator is only going to reproduce the same ground truth labels y for the newly generated X .

Hence fitting the generator is only using X_train :

datagen.fit(X_train)

If you do not want to pass the augmented data to training, you can loop over the generator after fitting to get the generated samples:

for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=32):
# Do whatever you want with the generated X_batch and y_batch.

I understand that is what you are willing to do. See examples on keras doc .