Deep Neural Networks (DNNs) are very powerful and successful but suffer from high computation and memory cost. As a useful attempt, binary neural networks represent weights and activations with binary values, which can significantly reduce resource consumption. How- ever, the simultaneous binarization introduces the coupling effect, aggravating the difficulty of training. In this paper, we develop a novel framework named TP-ADMM that decouples the binarization process into two iteratively optimized stages. Firstly, we propose an improved target propagation method to optimize the network with binary activations in a more stable format. Secondly, we apply the alternating direction method (ADMM) with a varying penalty to get the weights binarized, making weights binarization a discretely constrained optimization problem. Experiments on three public datasets for image classification show that the proposed method outperforms the existing methods.