How can I get the gradient of the loss w.r.t. model prediction in Tensorflow?

Question

I'd like to calculate the error gradient: dJ/dredictionp (if J is the cost function). In the function train_step() you can see that the gradients are calculated wrt the model weights.

When I tried to calculate the gradients like: gradients = tape.gradient(loss, predictions) , it returned None which would mean that my loss function is not dependent on the predictions.

How can that be?

class SimpleModel(models.Model):
    def __init__(self, nb_classes, X_dim: int, batch_size: int):
        super().__init__()
        self.model_input_layer = layers.InputLayer(input_shape=(X_dim,), batch_size=batch_size)

        self.d1 = layers.Dense(64, name="d1")
        self.a1 = layers.Activation("relu", name="a1")

        self.d2 = layers.Dense(32, name="d2")
        self.a2 = layers.Activation("relu", name="a2")

        self.d3 = layers.Dense(nb_classes, name="d3")
        self.a3 = layers.Activation("softmax", name="a3")

        self.model_input = None
        self.d1_output = None
        self.a1_output = None
        self.d2_output = None
        self.a2_output = None
        self.d3_output = None
        self.a3_output = None

    def call(self, inputs, training=None, mask=None):
        self.model_input = self.model_input_layer(inputs)
        self.d1_output = self.d1(self.model_input)
        self.a1_output = self.a1(self.d1_output)
        self.d2_output = self.d2(self.a1_output)
        self.a2_output = self.a2(self.d2_output)
        self.d3_output = self.d3(self.a2_output)
        self.a3_output = self.a3(self.d3_output)
        return self.a3_output


model = SimpleModel(NB_CLASSES, X_DIM, BATCH_SIZE)
model.build((BATCH_SIZE, X_DIM))

optimizer = Adam()
loss_object = losses.CategoricalCrossentropy()

train_loss = metrics.Mean(name='train_loss')
test_loss = metrics.Mean(name='test_loss')


@tf.function
def train_step(X, y):
    with tf.GradientTape() as tape:
        predictions = model(X)
        loss = loss_object(y, predictions)
    gradients = tape.gradient(loss, model.trainable_weights)
    optimizer.apply_gradients(zip(gradients, model.trainable_weights))

    train_loss(loss)

Answer 1

The issue is that GradientTape by default only tracks trainable variables, and no other tensors. Thus you need to explicitly tell it to track the tensor of interest. Try this:

predictions = model(X)  # if you also need gradients for model variables, move this back into the tape context
with tf.GradientTape() as tape:
    tape.watch(predictions)
    loss = loss_object(y, predictions)
gradients = tape.gradient(loss, [predictions])

Note the use of the watch method to track arbitrary tensors. This should not return None anymore.