Created CGAN example

from deepchem.data import NumpyDataset

from deepchem.metrics import to_one_hot, from_one_hot

from deepchem.models.models import Model

from deepchem.models.tensorgraph.layers import InputFifoQueue, Label, Feature, Weights

from deepchem.models.tensorgraph.layers import InputFifoQueue, Label, Feature, Weights, Constant

from deepchem.models.tensorgraph.optimizers import Adam

from deepchem.trans import undo_transforms

from deepchem.utils.evaluate import GeneratorEvaluator

    self.outputs = list()

    self.task_weights = list()

    self.submodels = list()

    self.loss = None

    self.loss = Constant(0)

    self.built = False

    self.queue_installed = False

    self.optimizer = Adam(

      self.build()

    with self._get_tf("Graph").as_default():

      time1 = time.time()

      loss = self.loss

      if submodel is None:

        train_op = self._get_tf('train_op')

      else:

        train_op = submodel.get_train_op()

        if submodel.loss is not None:

          loss = submodel.loss

      if checkpoint_interval > 0:

        saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)

      if restore:

        n_samples += 1

        should_log = (self.tensorboard and

                      n_samples % self.tensorboard_log_frequency == 0)

        fetches = [train_op, self.loss.out_tensor]

        fetches = [train_op, loss.out_tensor]

        if should_log:

          fetches.append(self._get_tf("summary_op"))

        fetched_values = self.session.run(fetches, feed_dict=feed_dict)

        if should_log:

          self._log_tensorboard(fetches[2])

        loss = fetched_values[1]

        avg_loss += loss

        avg_loss += fetched_values[1]

        n_averaged_batches += 1

        self.global_step += 1

        if checkpoint_interval > 0 and self.global_step % checkpoint_interval == checkpoint_interval - 1: