Function model

examples/quantized_net/tutorial_quanconv_mnist.py:23–48

(inputs_shape, n_class=10)

def model(inputs_shape, n_class=10):
    net_in = Input(inputs_shape, name="input")

    net = QuanConv2dWithBN(
        n_filter=32, filter_size=(5, 5), strides=(1, 1), padding='SAME', act=tl.nn.relu, name='qconvbn1'
    )(net_in)
    net = MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool1')(net)

    net = QuanConv2dWithBN(
        n_filter=64, filter_size=(5, 5), strides=(1, 1), padding='SAME', act=tl.nn.relu, name='qconvbn2'
    )(net)
    net = MaxPool2d(filter_size=(2, 2), strides=(2, 2), padding='SAME', name='pool2')(net)

    net = Flatten(name='ft')(net)

    # net = QuanDense(256, act="relu", name='qdbn')(net)
    # net = QuanDense(n_class, name='qdbn_out')(net)

    net = QuanDenseLayerWithBN(256, act="relu", name='qdbn')(net)
    net = QuanDenseLayerWithBN(n_class, name='qdbn_out')(net)

    # net = Dense(256, act='relu', name='Dense1')(net)
    # net = Dense(n_class, name='Dense2')(net)

    net = Model(inputs=net_in, outputs=net, name='quan')
    return net


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