Got it, let's figure out how to replicate that TensorFlow gradient registration and override behavior in Keras. Since Keras runs on top of TensorFlow (when using the TF backend), we can totally leverage TF's gradient utilities while working within Keras workflows. Here's a step-by-step breakdown with code examples:

1. Register Your Custom Gradient First #

Just like in pure TensorFlow, we start by registering our custom gradient function. This defines the gradient logic we want to use later:

import tensorflow as tf
from tensorflow import keras

some_multiplier = 0.5

# Register the custom gradient with a unique name
@tf.RegisterGradient("AdaGradCustom")
def _ada_grad_custom(op, grad):
    # Our custom gradient logic: multiply incoming gradient by the multiplier
    return grad * some_multiplier

2. Override Gradients in Keras Workflows #

There are two common ways to apply this gradient override in Keras—let's cover both:

Option A: Override Gradients During Training Steps

If you want to override gradients for a specific part of your loss calculation (like the original example with tf.identity), you can wrap that part in gradient_override_map within your training step:

# Build a simple Keras model
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=(32,)),
    keras.layers.Dense(10, activation='softmax')
])

loss_fn = keras.losses.SparseCategoricalCrossentropy()
optimizer = keras.optimizers.Adam()

# Define a custom training step with gradient override
@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        logits = model(x, training=True)
        base_loss = loss_fn(y, logits)
        
        # Wrap the loss with an operation we want to override, inside the gradient map context
        with tf.get_default_graph().gradient_override_map({"Identity": "AdaGradCustom"}):
            # The "Ada" name here is just for identification, not strictly necessary
            loss = tf.identity(base_loss, name="Ada")
    
    # Compute gradients—this will use our custom gradient for the Identity operation
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

Option B: Wrap in a Custom Keras Layer

If you want to apply the gradient override to a specific layer's output, create a custom layer that wraps the operation with the gradient map:

class GradientOverrideLayer(keras.layers.Layer):
    def call(self, inputs):
        # Override the Identity operation's gradient here
        with tf.get_default_graph().gradient_override_map({"Identity": "AdaGradCustom"}):
            return tf.identity(inputs, name="Ada")

# Use this layer in your model
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=(32,)),
    GradientOverrideLayer(),  # Gradients through this layer will use our custom logic
    keras.layers.Dense(10, activation='softmax')
])

Key Notes #

• The core idea is that Keras doesn't replace TensorFlow's gradient system—it builds on top of it. So TF's tf.RegisterGradient and gradient_override_map work exactly as you'd expect, as long as you apply the context manager around the operation whose gradient you want to override.
• Make sure the name in gradient_override_map matches the operation type (like "Identity" for tf.identity) and maps to the name you used when registering the gradient ("AdaGradCustom" in our example).

内容的提问来源于stack exchange，提问作者Sahitya Patel