def conv_nets():
    net = gluon.nn.HybridSequential()
    with net.name_scope():
        net.add(gluon.nn.Conv2D(channels=20, kernel_size=kernel_size, activation=activation))
        net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
        net.add(gluon.nn.Conv2D(channels=50, kernel_size=kernel_size, activation=activation))
        net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
        net.add(gluon.nn.Flatten())
        net.add(gluon.nn.Dense(hidden_dim, activation=activation))
        net.add(gluon.nn.Dense(output_dim))
    return net
def conv_nets():
    net = gluon.nn.HybridSequential()
    with net.name_scope():
        net.add(gluon.nn.Conv2D(channels=20, kernel_size=kernel_size, activation=activation))
        net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
        net.add(gluon.nn.Conv2D(channels=50, kernel_size=kernel_size, activation=activation))
        net.add(gluon.nn.MaxPool2D(pool_size=pool_size, strides=strides))
        net.add(gluon.nn.Flatten())
        net.add(gluon.nn.Dense(hidden_dim, activation=activation))
        net.add(gluon.nn.Dense(output_dim))
    return net