Merge pull request #1227 from miaecle/gcfix

    return self.evaluate_generator(

        self.default_generator(dataset, predict=True),

        metrics,

        transformers=transformers,

        labels=self.my_labels,

        weights=[self.my_task_weights],

        per_task_metrics=per_task_metrics)

from deepchem.data import NumpyDataset

from deepchem.models import GraphConvModel

from deepchem.models import TensorGraph

from deepchem.molnet.load_function.delaney_datasets import load_delaney

from deepchem.molnet import load_bace_classification, load_delaney

from deepchem.models.tensorgraph.layers import ReduceSum, L2Loss

from deepchem.models import WeaveModel

from deepchem.feat import ConvMolFeaturizer

                  featurizer='GraphConv',

                  num_tasks=2):

    data_points = 10

    if mode == 'classification':

      tasks, all_dataset, transformers = load_bace_classification(featurizer)

    else:

      tasks, all_dataset, transformers = load_delaney(featurizer)

    train, valid, test = all_dataset

    for i in range(1, num_tasks):

      tasks.append("random_task")

import numpy as np

from models import GraphConvModel

from deepchem.models import GraphConvModel

np.random.seed(123)

import tensorflow as tf