expert_output_tensor
concat
gate
subtask_input=tf.squeeze(gate_softmax*expert_output_tensor)
task
expert2
combined_score_sigmoid = sigmoid(output_score)
gate_softmax = expand_dims(gate_softmax)
ExpertLayer
expert1
subtask_input
loss: ctr: cross_entropydwell: square_error error = output_score - sample_weightlosses = loss_ops[ctr] + loss_ops[dwell]loss_ops = (l2_losses + task_layer_l2_loss) * l2_loss_coefl2_losses = sum(l2_list)task_layer_l2_loss = l2_loss(w_gate) + l2_loss(w_top)
expert_output
dense_layer
output_score = subtask_input*w_top +b_top
gate_softmax = softmax(expert_output *w_gate))
expert3
