loss

lossfu.md

calc_loss

• We take the image and the model as inputs
• expand dims basically adds an extra dimension to our input along the x axis to make it work with inception
• for every activation in our layers, we calculate the loss and append it to a list
• reduce_mean() and reduce_sum() are approximately the mean and sum equivalent for tensors instead of just plain arrays
• Thus the sum is the total loss we get

def calc_loss(img, model):
    img_batch = tf.expand_dims(img, axis=0)
    layer_activations = model(img_batch)

    losses = []
    for act in layer_activations:
        loss = tf.math.reduce_mean(act)
        losses.append(loss)

    return tf.reduce_sum(losses)