Remove Names from Tox21 GraphConv

def graph_conv_model(batch_size, tasks):

  model = TensorGraph(

      model_dir=model_dir, batch_size=batch_size, use_queue=False)

  atom_features = Feature(shape=(None, 75), name="ATOMFEATURES")

  degree_slice = Feature(shape=(None, 2), dtype=tf.int32, name="DEGREE_SLICE")

  membership = Feature(shape=(None,), dtype=tf.int32, name="MEMBERSHIP")

  atom_features = Feature(shape=(None, 75))

  degree_slice = Feature(shape=(None, 2), dtype=tf.int32)

  membership = Feature(shape=(None,), dtype=tf.int32)

  deg_adjs = []

  for i in range(0, 10 + 1):

    deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32, name="DEGADJ%i" % i)

    deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)

    deg_adjs.append(deg_adj)

  gc1 = GraphConvLayer(

      64,

      activation_fn=tf.nn.relu,

      in_layers=[atom_features, degree_slice, membership] + deg_adjs,

      name="CONV1")

  batch_norm1 = BatchNormLayer(in_layers=[gc1], name="BATCHNORM1")

      in_layers=[atom_features, degree_slice, membership] + deg_adjs)

  batch_norm1 = BatchNormLayer(in_layers=[gc1])

  gp1 = GraphPoolLayer(

      in_layers=[batch_norm1, degree_slice, membership] + deg_adjs,

      name="POOL1")

      in_layers=[batch_norm1, degree_slice, membership] + deg_adjs)

  gc2 = GraphConvLayer(

      64,

      activation_fn=tf.nn.relu,

      in_layers=[gp1, degree_slice, membership] + deg_adjs,

      name="CONV2")

  batch_norm2 = BatchNormLayer(in_layers=[gc2], name="BATCHNORM2")

      in_layers=[gp1, degree_slice, membership] + deg_adjs)

  batch_norm2 = BatchNormLayer(in_layers=[gc2])

  gp2 = GraphPoolLayer(

      in_layers=[batch_norm2, degree_slice, membership] + deg_adjs,

      name="POOL2")

      in_layers=[batch_norm2, degree_slice, membership] + deg_adjs)

  dense = Dense(

      out_channels=128, activation_fn=None, in_layers=[gp2], name="DENSE1")

  batch_norm3 = BatchNormLayer(in_layers=[dense], name="BATCHNORM3")

      out_channels=128, activation_fn=None, in_layers=[gp2])

  batch_norm3 = BatchNormLayer(in_layers=[dense])

  gg1 = GraphGather(

      batch_size=batch_size,

      activation_fn=tf.nn.tanh,

      in_layers=[batch_norm3, degree_slice, membership] + deg_adjs,

      name="GATHER")

      in_layers=[batch_norm3, degree_slice, membership] + deg_adjs)

  costs = []

  labels = []

  for task in tasks:

    classification = Dense(

        out_channels=2,

        name="GUESS_%s" % task,

        activation_fn=None,

        in_layers=[gg1])

    softmax = SoftMax(name="SOFTMAX_%s" % task, in_layers=[classification])

    softmax = SoftMax(in_layers=[classification])

    model.add_output(softmax)

    label = Label(shape=(None, 2), name="LABEL_%s" % task)

    label = Label(shape=(None, 2))

    labels.append(label)

    cost = SoftMaxCrossEntropy(

        name="COST_%s" % task, in_layers=[label, classification])

    cost = SoftMaxCrossEntropy(in_layers=[label, classification])

    costs.append(cost)

  entropy = Concat(name="ENT", in_layers=costs)

  task_weights = Weights(shape=(None, len(tasks)), name="W")

  loss = WeightedError(name="ERROR", in_layers=[entropy, task_weights])

  entropy = Concat(in_layers=costs)

  task_weights = Weights(shape=(None, len(tasks)))

  loss = WeightedError(in_layers=[entropy, task_weights])

  model.set_loss(loss)

  def feed_dict_generator(dataset, batch_size, epochs=1):