TensorFlow 2 custom loss: "No gradients provided for any variable" error

Question

I have an image segmentation problem I have to solve in TensorFlow 2.

In particular I have a training set composed by aerial images paired with their respective masks. In a mask the terrain is colored in black and the buildings are colored in white. The purpose is to predict the mask for the images in the test set.

I use a UNet with a final Conv2DTranspose with 1 filter and a sigmoid activation function. The prediction is made in the following way on the output of the final sigmoid layer: if y_pred>0.5, then it's a building, otherwise it's the background.

I want to implement a dice loss, so I wrote the following function

def dice_loss(y_true, y_pred):
    print("[dice_loss] y_pred=",y_pred,"y_true=",y_true)
    y_pred = tf.cast(y_pred > 0.5, tf.float32)
    y_true = tf.cast(y_true, tf.float32)
    numerator = 2 * tf.reduce_sum(y_true * y_pred)
    denominator = tf.reduce_sum(y_true + y_pred)

    return 1 - numerator / denominator

which I pass to TensorFlow in the following way:

loss = dice_loss
optimizer = tf.keras.optimizers.Adam(learning_rate=config.learning_rate)
metrics = [my_IoU, 'acc']
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

but at training time TensorFlow throw me the following error:

ValueError: No gradients provided for any variable:

Answer 1

The problem is in your loss function (obviously). Particularly, the following operation.

y_pred = tf.cast(y_pred > 0.5, tf.float32)

This is not a differentiable operation. Which results in Gradients being None. Change your loss function to the following and it will work.

def dice_loss(y_true, y_pred):
    print("[dice_loss] y_pred=",y_pred,"y_true=",y_true)
    y_true = tf.cast(y_true, tf.float32)
    numerator = 2 * tf.reduce_sum(y_true * y_pred)
    denominator = tf.reduce_sum(y_true + y_pred)

    return 1 - numerator / denominator