q2_rnn.py 文件源码

def add_loss_op(self, preds):
        """Adds Ops for the loss function to the computational graph.

        TODO: Compute averaged cross entropy loss for the predictions.
        Importantly, you must ignore the loss for any masked tokens.

        Hint: You might find tf.boolean_mask useful to mask the losses on masked tokens.
        Hint: You can use tf.nn.sparse_softmax_cross_entropy_with_logits to simplify your
                    implementation. You might find tf.reduce_mean useful.
        Args:
            pred: A tensor of shape (batch_size, max_length, n_classes) containing the output of the neural
                  network before the softmax layer.
        Returns:
            loss: A 0-d tensor (scalar)
        """
        ### YOUR CODE HERE (~2-4 lines)
        loss = tf.reduce_mean(tf.boolean_mask(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.labels_placeholder,
                                                                            logits = preds), self.mask_placeholder)) 

        ### END YOUR CODE
        return loss