Function model

examples/quantized_net/tutorial_quanconv_cifar10.py:58–69

(input_shape, n_classes, bitW, bitA)

def model(input_shape, n_classes, bitW, bitA):
    in_net = Input(shape=input_shape, name='input')
    net = QuanConv2dWithBN(64, (5, 5), (1, 1), act='relu', padding='SAME', bitW=bitW, bitA=bitA, name='qcnnbn1')(in_net)
    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool1')(net)
    net = QuanConv2dWithBN(64, (5, 5), (1, 1), padding='SAME', act='relu', bitW=bitW, bitA=bitA, name='qcnnbn2')(net)
    net = MaxPool2d((3, 3), (2, 2), padding='SAME', name='pool2')(net)
    net = Flatten(name='flatten')(net)
    net = QuanDense(384, act=tf.nn.relu, bitW=bitW, bitA=bitA, name='qd1relu')(net)
    net = QuanDense(192, act=tf.nn.relu, bitW=bitW, bitA=bitA, name='qd2relu')(net)
    net = Dense(n_classes, act=None, name='output')(net)
    net = Model(inputs=in_net, outputs=net, name='dorefanet')
    return net


# training settings