def focal_loss_all(prediction_tensor, target_tensor):
            r"""Compute focal loss for predictions.
                Multi-labels Focal loss formula:
                    FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p)
                         ,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = target_tensor.
            Args:
             prediction_tensor: A float tensor of shape [batch_size, num_anchors,
                num_classes] representing the predicted logits for each class
             target_tensor: A float tensor of shape [batch_size, num_anchors,
                num_classes] representing one-hot encoded classification targets
             weights: A float tensor of shape [batch_size, num_anchors]
             alpha: A scalar tensor for focal loss alpha hyper-parameter
             gamma: A scalar tensor for focal loss gamma hyper-parameter
            Returns:
                loss: A (scalar) tensor representing the value of the loss function
            """
            sigmoid_p = tf.nn.sigmoid(prediction_tensor)
            zeros = array_ops.zeros_like(sigmoid_p, dtype=sigmoid_p.dtype)

            # For poitive prediction, only need consider front part loss, back part is 0;
            # target_tensor > zeros <=> z=1, so poitive coefficient = z - p.
            pos_p_sub = array_ops.where(target_tensor > zeros, target_tensor - sigmoid_p, zeros)

            # For negative prediction, only need consider back part loss, front part is 0;
            # target_tensor > zeros <=> z=1, so negative coefficient = 0.
            neg_p_sub = array_ops.where(target_tensor > zeros, zeros, sigmoid_p)
            per_entry_cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(sigmoid_p, 1e-8, 1.0)) \
                                  - (1 - alpha) * (neg_p_sub ** gamma) * tf.log(tf.clip_by_value(1.0 - sigmoid_p, 1e-8, 1.0))
            return tf.reduce_sum(per_entry_cross_ent)
Compute focal loss for predictions.
                Multi-labels Focal loss formula:
                    FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p)
                         ,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = target_tensor.
            Args:
             prediction_tensor: A float tensor of shape [batch_size, num_anchors,
                num_classes] representing the predicted logits for each class
             target_tensor: A float tensor of shape [batch_size, num_anchors,
                num_classes] representing one-hot encoded classification targets
             weights: A float tensor of shape [batch_size, num_anchors]
             alpha: A scalar tensor for focal loss alpha hyper-parameter
             gamma: A scalar tensor for focal loss gamma hyper-parameter
            Returns:
                loss: A (scalar) tensor representing the value of the loss function
            
keras_textclassification/m01_FastText/graph.py