# What is the cost function of a transformer?

The paper Attention Is All You Need describes the transformer architecture that has an encoder and a decoder.

However, I wasn't clear on what the cost function to minimize is for such an architecture.

Consider a translation task, for example, where give an English sentence $$x_{english} = [x_0, x_1, x_2, \dots, x_m]$$, the transformer decodes the sentence into a French sentence $$x_{french}' = [x_0', x_1', \dots, x_n']$$. Let's say the true label is $$y_{french} = [y_0, y_1, \dots, y_p]$$.

What is the object function of the transformer? Is it the MSE between $$x_{french}'$$ and $$y_{french}$$? And does it have any weight regularization terms?

I took a look at the Tensor2Tensor's source code implementation, and it seems like the loss function is the cross-entropy between the predicted probability matrix $$\|\text{sentence length}\| \times \|\text{vocab}\|$$ (right before taking the argmax to find the token to output), and the $$\|\text{sentence length}\|$$-length vector of token IDs as the true label.
• Note that they reshape the out_logits variable there, in the line that you're linking us to, i.e. tf.reshape(out_logits, [-1, VOCAB_SIZE]), so the cross-entropy seems to be computed between two vectors and not a matrix and a vector (but I didn't really executed that code and tried to output the shape of those tensors/variables). It may be a good idea to do it, just to confirm the exact shape.