Custom gradients in TensorFlow

Custom gradients in TensorFlow allow you to define your gradient functions for operations, providing flexibility in how gradients are computed for complex or non-standard operations. This can be useful for tasks such as implementing custom loss functions, incorporating domain-specific knowledge into the gradient computation, or handling operations that TensorFlow does not natively support.

Why are custom gradients important?

Custom gradients are useful in TensorFlow for several reasons:

1. Implementing Custom Operations: Custom gradients allow you to define the gradient computation for operations that are not natively supported by TensorFlow, such as custom activation functions or custom layers.
2. Efficient Gradient Computation: In some cases, you might have a more efficient or numerically stable way to compute the gradient of a particular operation than the default TensorFlow implementation.
3. Incorporating Domain Knowledge: Custom gradients enable you to incorporate domain-specific knowledge into the gradient computation, which can lead to improved performance or better convergence properties for your models.
4. Regularization and Control Flow: Custom gradients can be used to implement regularization techniques or to control the flow of gradients through your computational graph, allowing you to customize the behaviour of your models.
5. Debugging and Experimentation: Custom gradients can also be useful for debugging and experimentation, as they allow you to inspect and modify the gradient computation process at a fine-grained level.

When to use custom gradients?

Custom gradients in TensorFlow are used when you want to define a custom gradient for a TensorFlow operation. This can be useful in several scenarios:

1. Numerical Stability: Sometimes, the default gradient computation can lead to numerical instability. In such cases, you can define a custom gradient that provides a more stable computation.
2. Efficiency: Custom gradients can be used to provide a more efficient computation compared to the default gradients. This can be useful when the default computation is inefficient or when you have a more efficient way to compute the gradient.
3. Non-Differentiable Operations: If you have operations in your model that are not differentiable, you can use custom gradients to define a gradient for these operations.
4. Improved Performance: In some cases, using custom gradients can lead to improved performance of your model, either in terms of training speed or final performance metrics.
5. Research and Experimentation: Custom gradients can be used in research or experimentation to explore novel ideas or improve existing models.

Implementing Custom Gradients

1. Define a Custom Operation: is a simple operation that squares the input x.
2. Define the Gradient Function: computes the gradient of custom_op with respect to its input x. In this case, since custom_op(x) = x^2, the gradient is 2 * x.
3. Use tf.custom_gradient to Define Custom Operation with Gradient : tf.custom_gradient is a decorator that allows you to define a custom operation along with its gradient function. Inside custom_op_with_grad, we compute y using custom_op(x) and define the gradient function grad(dy), which computes the gradient of the output with respect to x.
4. Example Usage and Gradient Computation: compute the gradient of custom_op both using TensorFlow’s automatic differentiation (grad_auto) and the custom gradient function (grad_custom) we defined earlier.
5. Print the Results.

Example compares the performance of a simple neural network for classifying handwritten digits (MNIST dataset) using custom and default gradients.

1. Libraries and Dataset

Python3

import tensorflow as tf
from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

2. Custom Gradient Function:

Custom gradient for the rectified linear unit (ReLU) activation function. ReLU is already supported in TensorFlow, but here’s a simplified custom version.

• custom_relu(x): This function computes the ReLU activation function, which returns x if x is greater than or equal to zero, and zero otherwise. It uses TensorFlow’s tf.maximum function to achieve this.

• custom_relu_grad(x): This function computes the gradient of the ReLU function. It returns a tensor with the same shape as x, where each element is 1.0 if the corresponding element in x is greater than zero, and 0.0 otherwise. It uses TensorFlow’s tf.where function for this purpose.
• @tf.custom_gradient: This is a decorator that allows you to define a custom gradient for a TensorFlow operation. It wraps the custom_relu_op function, which will be the custom operation with a defined gradient.
• custom_relu_op(x): This function computes the ReLU activation function using custom_relu(x). It also defines a gradient function grad(dy) that computes the gradient of the output with respect to the input (dy is the gradient of the output of custom_relu_op with respect to some external value).
• grad(dy): This function computes the gradient of the output of custom_relu_op with respect to its input (x). It uses custom_relu_grad(x) to compute the gradient and multiplies it by dy to propagate the gradient backward through the operation.

Python3

def custom_relu(x):
  return tf.maximum(x, 0.0)

def custom_relu_grad(x):
  return tf.where(x > 0, tf.ones_like(x), tf.zeros_like(x))

@tf.custom_gradient
def custom_relu_op(x):
  y = custom_relu(x)
  def grad(dy):
    return custom_relu_grad(x) * dy
  return y, grad

3. Model Definition:

• Model A uses the default ReLU activation function provided by TensorFlow.
• Model B uses the custom ReLU activation function (custom_relu_op) that we defined earlier.

Python3

# Model A (Default ReLU)
model_a = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])

# Model B (Custom ReLU)
model_b = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation=custom_relu_op),
  tf.keras.layers.Dense(10, activation='softmax')
])

4. Training:

Python3

model_a.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model_b.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model_a.fit(x_train, y_train, epochs=5)
model_b.fit(x_train, y_train, epochs=5)

test_loss_a, test_acc_a = model_a.evaluate(x_test, y_test)
test_loss_b, test_acc_b = model_b.evaluate(x_test, y_test)

Output:

Epoch 1/5
1875/1875 [==============================] - 10s 4ms/step - loss: 0.2645 - accuracy: 0.9246
Epoch 2/5
1875/1875 [==============================] - 7s 4ms/step - loss: 0.1155 - accuracy: 0.9656
Epoch 3/5
1875/1875 [==============================] - 7s 4ms/step - loss: 0.0797 - accuracy: 0.9751
Epoch 4/5
1875/1875 [==============================] - 8s 5ms/step - loss: 0.0596 - accuracy: 0.9817
Epoch 5/5
1875/1875 [==============================] - 6s 3ms/step - loss: 0.0461 - accuracy: 0.9859
Epoch 1/5
1875/1875 [==============================] - 7s 2ms/step - loss: 0.2581 - accuracy: 0.9256

5. Evaluation:

Python3

print("Model A (Default ReLU): Test Accuracy:", test_acc_a)
print("Model B (Custom ReLU): Test Accuracy:", test_acc_b)

Output:

Model A (Default ReLU): Test Accuracy: 0.9751999974250793
Model B (Custom ReLU): Test Accuracy: 0.9776999950408936

Both models appear to perform rather well on the test dataset; in terms of test accuracy, Model B (Custom ReLU) marginally outperforms Model A (Default ReLU). The behavior of the custom ReLU function and the unique features of the dataset may be the cause of this discrepancy.
It’s important to note that there may not be much of a practical difference in accuracy between the two models due to their modest differences. It does show, though, that utilizing a custom activation function, such as custom_relu_op, might occasionally result in better model performance.