gradient_centralization.py

"""
Title: Gradient Centralization for Better Training Performance
Author: [Rishit Dagli](https://github.com/Rishit-dagli)
Date created: 06/18/21
Last modified: 05/29/25
Description: Implement Gradient Centralization to improve training performance of DNNs.
Accelerator: GPU
Converted to Keras 3 by: [Muhammad Anas Raza](https://anasrz.com)
Debugged by: [Alberto M. Esmorís](https://github.com/albertoesmp)
"""

"""
## Introduction

This example implements [Gradient Centralization](https://arxiv.org/abs/2004.01461), a
new optimization technique for Deep Neural Networks by Yong et al., and demonstrates it
on Laurence Moroney's [Horses or Humans
Dataset](https://www.tensorflow.org/datasets/catalog/horses_or_humans). Gradient
Centralization can both speedup training process and improve the final generalization
performance of DNNs. It operates directly on gradients by centralizing the gradient
vectors to have zero mean. Gradient Centralization morever improves the Lipschitzness of
the loss function and its gradient so that the training process becomes more efficient
and stable.

This example requires `tensorflow_datasets` which can be installed with this command:

```
pip install tensorflow-datasets
```
"""

"""
## Setup
"""

from time import time

import keras
from keras import layers
from keras.optimizers import RMSprop
from keras import ops

from tensorflow import data as tf_data
import tensorflow_datasets as tfds

"""
## Prepare the data

For this example, we will be using the [Horses or Humans
dataset](https://www.tensorflow.org/datasets/catalog/horses_or_humans).
"""

num_classes = 2
input_shape = (300, 300, 3)
dataset_name = "horses_or_humans"
batch_size = 128
AUTOTUNE = tf_data.AUTOTUNE

(train_ds, test_ds), metadata = tfds.load(
    name=dataset_name,
    split=[tfds.Split.TRAIN, tfds.Split.TEST],
    with_info=True,
    as_supervised=True,
)

print(f"Image shape: {metadata.features['image'].shape}")
print(f"Training images: {metadata.splits['train'].num_examples}")
print(f"Test images: {metadata.splits['test'].num_examples}")

"""
## Use Data Augmentation

We will rescale the data to `[0, 1]` and perform simple augmentations to our data.
"""

rescale = layers.Rescaling(1.0 / 255)

data_augmentation = [
    layers.RandomFlip("horizontal_and_vertical"),
    layers.RandomRotation(0.3),
    layers.RandomZoom(0.2),
]


# Helper to apply augmentation
def apply_aug(x):
    for aug in data_augmentation:
        x = aug(x)
    return x


def prepare(ds, shuffle=False, augment=False):
    # Rescale dataset
    ds = ds.map(lambda x, y: (rescale(x), y), num_parallel_calls=AUTOTUNE)

    if shuffle:
        ds = ds.shuffle(1024)

    # Batch dataset
    ds = ds.batch(batch_size)

    # Use data augmentation only on the training set
    if augment:
        ds = ds.map(
            lambda x, y: (apply_aug(x), y),
            num_parallel_calls=AUTOTUNE,
        )

    # Use buffered prefecting
    return ds.prefetch(buffer_size=AUTOTUNE)


"""
Rescale and augment the data
"""

train_ds = prepare(train_ds, shuffle=True, augment=True)
test_ds = prepare(test_ds)
"""
## Define a model

In this section we will define a Convolutional neural network.
"""


def make_model():
    return keras.Sequential(
        [
            layers.Input(shape=input_shape),
            layers.Conv2D(16, (3, 3), activation="relu"),
            layers.MaxPooling2D(2, 2),
            layers.Conv2D(32, (3, 3), activation="relu"),
            layers.Dropout(0.5),
            layers.MaxPooling2D(2, 2),
            layers.Conv2D(64, (3, 3), activation="relu"),
            layers.Dropout(0.5),
            layers.MaxPooling2D(2, 2),
            layers.Conv2D(64, (3, 3), activation="relu"),
            layers.MaxPooling2D(2, 2),
            layers.Conv2D(64, (3, 3), activation="relu"),
            layers.MaxPooling2D(2, 2),
            layers.Flatten(),
            layers.Dropout(0.5),
            layers.Dense(512, activation="relu"),
            layers.Dense(1, activation="sigmoid"),
        ]
    )


"""
## Implement Gradient Centralization

We will now
subclass the `RMSProp` optimizer class modifying the
`keras.optimizers.Optimizer.get_gradients()` method where we now implement Gradient
Centralization. On a high level the idea is that let us say we obtain our gradients
through back propagation for a Dense or Convolution layer we then compute the mean of the
column vectors of the weight matrix, and then remove the mean from each column vector.

The experiments in [this paper](https://arxiv.org/abs/2004.01461) on various
applications, including general image classification, fine-grained image classification,
detection and segmentation and Person ReID demonstrate that GC can consistently improve
the performance of DNN learning.

Also, for simplicity at the moment we are not implementing gradient cliiping functionality,
however this quite easy to implement.

At the moment we are just creating a subclass for the `RMSProp` optimizer
however you could easily reproduce this for any other optimizer or on a custom
optimizer in the same way. We will be using this class in the later section when
we train a model with Gradient Centralization.
"""


class GCRMSprop(RMSprop):
    def get_gradients(self, loss, params):
        # We here just provide a modified get_gradients() function since we are
        # trying to just compute the centralized gradients.

        grads = []
        gradients = super().get_gradients()
        for grad in gradients:
            grad_len = len(grad.shape)
            if grad_len > 1:
                axis = list(range(grad_len - 1))
                grad -= ops.mean(grad, axis=axis, keep_dims=True)
            grads.append(grad)

        return grads


optimizer = GCRMSprop(learning_rate=1e-4)

"""
## Training utilities

We will also create a callback which allows us to easily measure the total training time
and the time taken for each epoch since we are interested in comparing the effect of
Gradient Centralization on the model we built above.
"""


class TimeHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.times = []

    def on_epoch_begin(self, batch, logs={}):
        self.epoch_time_start = time()

    def on_epoch_end(self, batch, logs={}):
        self.times.append(time() - self.epoch_time_start)


"""
## Train the model without GC

We now train the model we built earlier without Gradient Centralization which we can
compare to the training performance of the model trained with Gradient Centralization.
"""

time_callback_no_gc = TimeHistory()
model = make_model()
model.compile(
    loss="binary_crossentropy",
    optimizer=RMSprop(learning_rate=1e-4),
    metrics=["accuracy"],
)

model.summary()

"""
We also save the history since we later want to compare our model trained with and not
trained with Gradient Centralization
"""

history_no_gc = model.fit(
    train_ds, epochs=10, verbose=1, callbacks=[time_callback_no_gc]
)

"""
## Train the model with GC

We will now train the same model, this time using Gradient Centralization,
notice our optimizer is the one using Gradient Centralization this time.
"""

time_callback_gc = TimeHistory()
model = make_model()
model.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"])

model.summary()

history_gc = model.fit(train_ds, epochs=10, verbose=1, callbacks=[time_callback_gc])

"""
## Comparing performance
"""

print("Not using Gradient Centralization")
print(f"Loss: {history_no_gc.history['loss'][-1]}")
print(f"Accuracy: {history_no_gc.history['accuracy'][-1]}")
print(f"Training Time: {sum(time_callback_no_gc.times)}")

print("Using Gradient Centralization")
print(f"Loss: {history_gc.history['loss'][-1]}")
print(f"Accuracy: {history_gc.history['accuracy'][-1]}")
print(f"Training Time: {sum(time_callback_gc.times)}")

"""
Readers are encouraged to try out Gradient Centralization on different datasets from
different domains and experiment with it's effect. You are strongly advised to check out
the [original paper](https://arxiv.org/abs/2004.01461) as well - the authors present
several studies on Gradient Centralization showing how it can improve general
performance, generalization, training time as well as more efficient.

Many thanks to [Ali Mustufa Shaikh](https://github.com/ialimustufa) for reviewing this
implementation.
"""