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When classifying images with Keras, I am able to achieve a validation accuracy around 90-95%, however, I am trying to improve with the use of augmentation so have switched from image_dataset_from_directory, to flow_from_directory, to make use of the ImageDataGenerator.

For some reason the validation accuracy holds at 33% and does not improve? I have modified the augmentation parameters to see if that is affecting the training, but it does not seem to be that.

Could anyone explain if I am doing something wrong, or if there is an alternative method to implementing augmentation?

original: (Found 240 files belonging to 3 classes. Using 192 files for training. Found 240 files belonging to 3 classes. Using 48 files for validation. Found 60 files belonging to 3 classes.)

train_data = tf.keras.preprocessing.image_dataset_from_directory(train_dir,
                                                                image_size=img_size,
                                                                batch_size=batch_no,
                                                                seed=seed_no,                                                               
                                                                shuffle=True,
                                                                subset="training",
                                                                validation_split=0.2,
                                                                label_mode="categorical")

validation_data = tf.keras.preprocessing.image_dataset_from_directory(train_dir,
                                                                      image_size=img_size,
                                                                      batch_size=batch_no,
                                                                      seed=seed_no,
                                                                      shuffle=False,
                                                                      validation_split=0.2,
                                                                      subset="validation",
                                                                      label_mode="categorical")
                                                                      
test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,
                                                                image_size=img_size,
                                                                batch_size=batch_no,
                                                                shuffle=False,
                                                                seed=seed_no,
                                                                label_mode="categorical")

Replaced: (Found 192 images belonging to 3 classes. Found 48 images belonging to 3 classes. Found 60 images belonging to 3 classes. )

dgen_test = ImageDataGenerator(rescale = 1./255.)
dgen_train = ImageDataGenerator(rescale = 1./255.,
                                zoom_range = 0.2,
                                horizontal_flip = True,
                                vertical_flip= True,
                                rotation_range=20,
                                shear_range=0.2,
                                validation_split=0.2
                                )

train_data = dgen_train.flow_from_directory(train_dir,
                                            subset='training',
                                            target_size=img_size,
                                            batch_size=batch_no,
                                            shuffle=True,
                                            seed=seed_no,
                                            class_mode="categorical")

validation_data = dgen_train.flow_from_directory(train_dir, 
                                                 subset='validation', 
                                                 target_size=img_size,
                                                 batch_size=batch_no, 
                                                 shuffle=False,
                                                 seed=seed_no,
                                                 class_mode='categorical')

test_data = dgen_test.flow_from_directory(test_dir, 
                                        target_size=img_size,
                                        batch_size=batch_no, 
                                        shuffle=False,
                                        seed=seed_no,
                                        class_mode='categorical')
```
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  • $\begingroup$ Avoid shear range and zoom range. Flips and rotations are ok. $\endgroup$ Nov 11 at 4:56

1 Answer 1

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Solved using tf.keras.layers and applying augmentation when building model.

https://www.tensorflow.org/tutorials/images/data_augmentation

ImageDataGenerator is depreciated.

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