Sparsity-constrained　optimization　problems　are　common　in　machine　learning,　such　as　sparse　coding,　low-rank　minimization　and　compressive　sensing.　However,　most　of　previous　studies　focused　on　constructing　various　hand-crafted　sparse　regularizers,　while　little　work　was　devoted　to　learning　adaptive　sparse　regularizers　from　given　input　data　for　specific　tasks.　In　this　paper,　we　propose　a　deep　sparse　regularizer　learning　model　that　learns　data-driven　sparse　regularizers　adaptively.　Via　the　proximal　gradient　algorithm,　we　find　that　the　sparse　regularizer　learning　is　equivalent　to　learning　a　parameterized　activation　function.　This　encourages　us　to　learn　sparse　regularizers　in　the　deep　learning　framework.　Therefore,　we　build　a　neural　network　composed　of　multiple　blocks,　each　being　differentiable　and　reusable.　All　blocks　contain　learnable　piecewise　linear　activation　functions　which　correspond　to　the　sparse　regularizer　to　be　learned.　Furthermore,　the　proposed　model　is　trained　with　back　propagation,　and　all　parameters　in　this　model　are　learned　end-to-end.　We　apply　our　framework　to　multi-view　clustering　and　semi-supervised　classification　tasks　to　learn　a　latent　compact　representation.　Experimental　results　demonstrate　the　superiority　of　the　proposed　framework　over　state-of-the-art　multi-view　learning　models.