lr = numpy.float64(0.001) nepochs = int(1) name = str("mnist_cnet_3l") input_dims = 1 output_dims = 10 model = Sequential() for i in range(nlayers_embeded): if i == 0: nfilters = input_dims model.add(CnetConv(nfilters, nfilters_embeded, 3, 1, 0)) model.add(CnetPool(2, 2, 0)) else: nfilters = nfilters_embeded model.add(CnetPool(2, 2, 0)) model.add(CnetLin(None, output_dims)) model.build() chain = ChainRAP() chain.add_sequence(model) chain.setup_optimizers('adam', lr) print("Model define Over ! ") #################################################Training####################################################
nfilters_embeded = int(32) nlayers_embeded = int(3) lr = numpy.float64(0.001) nepochs = int(1) name = str("mnist_ebnn") input_dims = 1 output_dims = 10 model = Sequential() for i in range(nlayers_embeded): if i == 0: nfilters = input_dims model.add(ConvPoolBNBST(nfilters, nfilters_embeded, 3, 1, 0, 2, 2, 0)) else: nfilters = nfilters_embeded model.add( BinaryConvPoolBNBST(nfilters, nfilters_embeded, 3, 1, 0, 2, 2, 0)) model.add(BinaryLinearBNSoftmax(None, output_dims)) model.build() chain = ChainRAP() chain.add_sequence(model) chain.setup_optimizers('adam', lr) print("Model define Over ! ") #################################################Training####################################################