example fix

  Examples

  --------

  import deepchem as dc

  from deepchem.models import BasicMolGANModel as MolGAN

  from deepchem.models.optimizers import ExponentialDecay

  from tensorflow import one_hot

  smiles = ['CCC', 'C1=CC=CC=C1', 'CNC' ]

  # create featurizer

  feat = dc.feat.MolGanFeaturizer()

  # featurize molecules

  features = feat.featurize(smiles)

  # Remove empty objects

  features = list(filter(lambda x: x is not None, features))

  # create model

  gan = MolGAN(learning_rate=ExponentialDecay(0.001, 0.9, 5000))

  dataset = dc.data.NumpyDataset([x.adjacency_matrix for x in features],[x.node_features for x in features])

  def iterbatches(epochs):

      for i in range(epochs):

          for batch in dataset.iterbatches(batch_size=gan.batch_size, pad_batches=True):

              adjacency_tensor = one_hot(batch[0], gan.edges)

              node_tensor = one_hot(batch[1], gan.nodes)

              yield {gan.data_inputs[0]: adjacency_tensor, gan.data_inputs[1]:node_tensor}

  gan.fit_gan(iterbatches(8), generator_steps=0.2, checkpoint_interval=5000)

  generated_data = gan.predict_gan_generator(1000)

  # convert graphs to RDKitmolecules

  nmols = feat.defeaturize(generated_data)

  print("{} molecules generated".format(len(nmols)))

  # remove invalid moles

  nmols = list(filter(lambda x: x is not None, nmols))

  # currently training is unstable so 0 is a common outcome

  print ("{} valid molecules".format(len(nmols)))

  >>>

  >> import deepchem as dc

  >> from deepchem.models import BasicMolGANModel as MolGAN

  >> from deepchem.models.optimizers import ExponentialDecay

  >> from tensorflow import one_hot

  >> smiles = ['CCC', 'C1=CC=CC=C1', 'CNC' ]

  >> # create featurizer

  >> feat = dc.feat.MolGanFeaturizer()

  >> # featurize molecules

  >> features = feat.featurize(smiles)

  >> # Remove empty objects

  >> features = list(filter(lambda x: x is not None, features))

  >> # create model

  >> gan = MolGAN(learning_rate=ExponentialDecay(0.001, 0.9, 5000))

  >> dataset = dc.data.NumpyDataset([x.adjacency_matrix for x in features],[x.node_features for x in features])

  >> def iterbatches(epochs):

  >>     for i in range(epochs):

  >>         for batch in dataset.iterbatches(batch_size=gan.batch_size, pad_batches=True):

  >>             adjacency_tensor = one_hot(batch[0], gan.edges)

  >>             node_tensor = one_hot(batch[1], gan.nodes)

  >>             yield {gan.data_inputs[0]: adjacency_tensor, gan.data_inputs[1]:node_tensor}

  >> gan.fit_gan(iterbatches(8), generator_steps=0.2, checkpoint_interval=5000)

  >> generated_data = gan.predict_gan_generator(1000)

  >> # convert graphs to RDKitmolecules

  >> nmols = feat.defeaturize(generated_data)

  >> print("{} molecules generated".format(len(nmols)))

  >> # remove invalid moles

  >> nmols = list(filter(lambda x: x is not None, nmols))

  >> # currently training is unstable so 0 is a common outcome

  >> print ("{} valid molecules".format(len(nmols)))

  References

  ----------