In TensorFlow 2.0 with eager-execution, how to compute the gradients of a network output wrt a specific layer?

Question

I have a network made with InceptionNet, and for an input sample bx, I want to compute the gradients of the model output w.r.t. the hidden layer. I have the following code:

bx = tf.reshape(x_batch[0, :, :, :], (1, 299, 299, 3))


with tf.GradientTape() as gtape:
    #gtape.watch(x)
    preds = model(bx)
    print(preds.shape, end='  ')

    class_idx = np.argmax(preds[0])
    print(class_idx, end='   ')

    class_output = model.output[:, class_idx]
    print(class_output, end='   ')

    last_conv_layer = model.get_layer('inception_v3').get_layer('mixed10')
    #gtape.watch(last_conv_layer)
    print(last_conv_layer)


grads = gtape.gradient(class_output, last_conv_layer.output)#[0]
print(grads)

But, this will give None. I tried gtape.watch(bx) as well, but it still gives None.

Before trying GradientTape, I tried using tf.keras.backend.gradient but that gave an error as follows:

RuntimeError: tf.gradients is not supported when eager execution is enabled. Use tf.GradientTape instead.

My model is as follows:

model.summary()

Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
inception_v3 (Model)         (None, 1000)              23851784  
_________________________________________________________________
dense_5 (Dense)              (None, 2)                 2002      
=================================================================
Total params: 23,853,786
Trainable params: 23,819,354
Non-trainable params: 34,432
_________________________________________________________________

Any solution is appreciated. It doesn't have to be GradientTape, if there is any other way to compute these gradients.

Answer 1

I had the same problem as you. I'm not sure if this is the cleanest way to solve the problem, but here's my solution.

I think the problem is that you need to pass along the actual return value of last_conv_layer.call(...) as an argument to tape.watch(). Since all layers are called sequentially within the scope of the model(bx) call, you'll have to somehow inject some code into this inner scope. I did this using the following decorator:

def watch_layer(layer, tape):
    """
    Make an intermediate hidden `layer` watchable by the `tape`.
    After calling this function, you can obtain the gradient with
    respect to the output of the `layer` by calling:

        grads = tape.gradient(..., layer.result)

    """
    def decorator(func):
        def wrapper(*args, **kwargs):
            # Store the result of `layer.call` internally.
            layer.result = func(*args, **kwargs)
            # From this point onwards, watch this tensor.
            tape.watch(layer.result)
            # Return the result to continue with the forward pass.
            return layer.result
        return wrapper
    layer.call = decorator(layer.call)
    return layer

In your example, I believe the following should then work for you:

bx = tf.reshape(x_batch[0, :, :, :], (1, 299, 299, 3))
last_conv_layer = model.get_layer('inception_v3').get_layer('mixed10')
with tf.GradientTape() as gtape:
    # Make the `last_conv_layer` watchable
    watch_layer(last_conv_layer, gtape)  
    preds = model(bx)
    class_idx = np.argmax(preds[0])
    class_output = model.output[:, class_idx]
# Get the gradient w.r.t. the output of `last_conv_layer`
grads = gtape.gradient(class_output, last_conv_layer.result)  
print(grads)