Perception TF v1 code savedModel graph says it has no operation #52

g = tf.Graph()
with g.as_default():
    # Graph Inputs
    features = tf.placeholder(dtype=tf.float32,
                              shape=[None, 2], name='features')
    targets = tf.placeholder(dtype=tf.float32,
                             shape=[None, 1], name='targets')

    # Model Parameters
    weights = tf.Variable(tf.zeros(shape=[2, 1],
                                   dtype=tf.float32), name='weights')
    bias = tf.Variable([[0.]], dtype=tf.float32, name='bias')

    # Forward Pass
    linear = tf.add(tf.matmul(features, weights), bias, name='linear')
    ones = tf.ones(shape=tf.shape(linear))
    zeros = tf.zeros(shape=tf.shape(linear))
    prediction = tf.where(condition=tf.less(linear, 0.),
                          x=zeros,
                          y=ones,
                          name='prediction')

    # Backward Pass
    errors = targets - prediction
    weight_update = tf.assign_add(weights,
                                  tf.reshape(errors * features, (2, 1)),
                                  name='weight_update')
    bias_update = tf.assign_add(bias, errors,
                                name='bias_update')

    train = tf.group(weight_update, bias_update, name='train')

    saver = tf.train.Saver(name='saver')

inputs = dict([(features.name, features)])
outputs = dict([(prediction.name, prediction)])
tf.saved_model.simple_save(sess, "my_path", inputs, outputs)

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['features:0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 2)
        name: features:0