Merge pull request #1298 from peastman/examples

                        dataset,

                        epochs=1,

                        predict=False,

                        deterministic=True,

                        pad_batches=True):

    for epoch in range(epochs):

      if not predict:

        print('Starting epoch %i' % epoch)

      for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(

          batch_size=self.batch_size,

          deterministic=True,

          deterministic=deterministic,

          pad_batches=pad_batches):

        feed_dict = dict()

        jac=self.grad_one,

        method="BFGS",

        tol=1e-6,

        options={

            'disp': True

        })

        options={'disp': True})

    return res.x.reshape((num_atoms, 3))

    for transformer in transformers:

      dataset = transformer.transform(dataset)

  if spit == None:

  if split == None:

    return chembl_tasks, (dataset, None, None), transformers

  splitters = {

import numpy as np

from models import GraphConvModel

from deepchem.models import GraphConvModel

np.random.seed(123)

import tensorflow as tf

import numpy as np

from models import GraphConvModel

from deepchem.models import GraphConvModel

np.random.seed(123)

import tensorflow as tf

import tensorflow as tf

tf.set_random_seed(123)

import deepchem as dc

from membrain_permeability_datasets import load_permeability

from membrane_permeability_datasets import load_permeability

# Load Tox21 dataset

permeability_tasks, permeability_datasets, transformers = load_permeability(