I have a CNN model with a single output neuron consisting of sigmoid activation, hence its value is in between 0 and 1. I wanted to calculate a combination of loss for this particular output neuron.
I was using Mean Absolute Error and Mean Squared Error for the same, and creating a loss like this:
loss = tf.keras.losses.MeanAbsoluteError() + tf.keras.losses.MeanSquaredError()
Now, due to some issue, the tensorflow framework is not supporting loss function like this. Here is the error:
Traceback (most recent call last): File "run_kfold.py", line 189, in <module> loss = tf.keras.losses.MeanAbsoluteError() + tf.keras.losses.MeanSquaredError() TypeError: unsupported operand type(s) for +: 'MeanAbsoluteError' and 'MeanSquaredError'
Can anyone suggest how to calculate combo loss for a certain output layer. This will help to create multiple weighted losses in combination, like this:
l_1 = 0.6 l_2 = 0.4 loss = l_1 * tf.keras.losses.MeanAbsoluteError() + l_2 *tf.keras.losses.MeanSquaredError()
I can then pass this loss variable to the model.compile() function
model.compile(optimizer=opt, loss=loss, metrics = ['accuracy', sensitivity, specificity, tf.keras.metrics.RootMeanSquaredError(name='rmse')] )