Activation　function　plays　an　important　role　in　neural　networks.　We　propose　to　use　hat　activation　function,　namely　the　first　order　B-spline,　as　activation　function　for　CNNs　including　MgNet　and　ResNet.　Different　from　commonly　used　activation　functions　like　ReLU,　the　hat　function　has　a　compact　support　and　no　obvious　spectral　bias.　Although　spectral　bias　is　thought　to　be　beneficial　for　generalization,　we　show　that　MgNet　and　ResNet　with　hat　function　still　exhibit　a　slightly　better　generalization　performance　than　CNNs　with　ReLU　function　by　our　experiments　of　classification　on　MNIST,　CIFAR10/100　and　ImageNet　datasets.　This　indicates　that　CNNs　without　spectral　bias　can　have　a　good　generalization　capability.　We　also　illustrate　that　although　hat　function　has　a　small　activation　area　which　is　more　likely　to　induce　vanishing　gradient　problem,　hat　CNNs　with　various　initialization　methods　still　works　well.