How to compute gradient of output wrt input in Tensorflow 2.0

Question

I have a trained Tensorflow 2.0 model (from tf.keras.Sequential()) that takes an input layer with 26 columns (X) and produces an output layer with 1 column (Y).

In TF 1.x I was able to calculate the gradient of the output with respect to the input with the following:

model = load_model('mymodel.h5')
sess = K.get_session()
grad_func = tf.gradients(model.output, model.input)
gradients = sess.run(grad_func, feed_dict={model.input: X})[0]

In TF2 when I try to run tf.gradients(), I get the error:

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

In the question In TensorFlow 2.0 with eager-execution, how to compute the gradients of a network output wrt a specific layer?, we see an answer on how to calculate gradients with respect to intermediate layers, but I don't see how to apply this to gradients with respect to the inputs. On the Tensorflow help for tf.GradientTape, there are examples with calculating gradients for simple functions, but not neural networks.

How can tf.GradientTape be used to calculate the gradient of the output with respect to the input?

Answer 1

This should work in TF2:

inp = tf.Variable(np.random.normal(size=(25, 120)), dtype=tf.float32)

with tf.GradientTape() as tape:
    preds = model(inp)

grads = tape.gradient(preds, inp)

Basically you do it the same way as TF1, but using GradientTape.