class LogisticModel(BaseModel):
  """Logistic model with L2 regularization."""

  def create_model(self, model_input, num_classes=10, l2_penalty=1e-8, **unused_params):



    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
                      weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
                      weights_regularizer=slim.l2_regularizer(0.0005)):
      net = slim.repeat(model_input, 1, slim.conv2d, 32, [5, 5], scope='conv1')
      net = slim.max_pool2d(net, [2, 2], scope='pool1')
      net = slim.repeat(net, 2, slim.conv2d, 64, [3, 3], scope='conv2')
      net = slim.max_pool2d(net, [2, 2], scope='pool2')
      net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv3')
      net = slim.max_pool2d(net, [2, 2], scope='pool3')



    net = slim.flatten(net)

    net = slim.fully_connected(net, 1024, scope='fc8')
    net = slim.dropout(net, 0.5, scope='dropout8')

    output = slim.fully_connected(
        net, num_classes, activation_fn=None,
        weights_regularizer=slim.l2_regularizer(l2_penalty))
    #output = slim.dropout(net, 0.5, scope='dropoutlast')
    return {"predictions": output}