Function model

examples/quantized_net/tutorial_binarynet_mnist_cnn.py:20–42

(inputs_shape, n_class=10)

def model(inputs_shape, n_class=10):
    # In BNN, all the layers inputs are binary, with the exception of the first layer.
    # ref: https://github.com/itayhubara/BinaryNet.tf/blob/master/models/BNN_cifar10.py
    net_in = Input(inputs_shape, name='input')
    net = BinaryConv2d(32, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn1')(net_in)
    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool1')(net)
    net = BatchNorm(act=tl.act.htanh, name='bn1')(net)

    net = Sign("sign1")(net)
    net = BinaryConv2d(64, (5, 5), (1, 1), padding='SAME', b_init=None, name='bcnn2')(net)
    net = MaxPool2d((2, 2), (2, 2), padding='SAME', name='pool2')(net)
    net = BatchNorm(act=tl.act.htanh, name='bn2')(net)

    net = Flatten('ft')(net)
    net = Sign("sign2")(net)
    net = BinaryDense(256, b_init=None, name='dense')(net)
    net = BatchNorm(act=tl.act.htanh, name='bn3')(net)

    net = Sign("sign3")(net)
    net = BinaryDense(10, b_init=None, name='bout')(net)
    net = BatchNorm(name='bno')(net)
    net = Model(inputs=net_in, outputs=net, name='binarynet')
    return net


def _train_step(network, X_batch, y_batch, cost, train_op=tf.optimizers.Adam(learning_rate=0.0001), acc=None):