Hands-On Transfer Learning with Python

Pretrained CNN model as a feature extractor with image augmentation

We will leverage the same data generators for our train and validation datasets that we used before. The code for building them is depicted as follows for ease of understanding:

train_datagen = ImageDataGenerator(rescale=1./255, zoom_range=0.3,                                    rotation_range=50,                                      width_shift_range=0.2,                                     height_shift_range=0.2,                                    shear_range=0.2,                                    horizontal_flip=True,                                     fill_mode='nearest') val_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow(train_imgs, train_labels_enc,                                      batch_size=30) val_generator = val_datagen.flow(validation_imgs,                                  validation_labels_enc,                                  batch_size=20)

Let's now build our deep learning model architecture. We won't extract the bottleneck ...