Cleanup

from deepchem.models.keras_models import KerasModel

from deepchem.models.tf_keras_models.multitask_classifier import MultitaskGraphClassifier

from deepchem.models.tf_keras_models.support_classifier import SupportGraphClassifier

#from deepchem.models.tensorflow_models import TensorflowModel

from deepchem.models.multitask import SingletaskToMultitask

from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskRegressor

        for epoch in range(nb_epoch):

          avg_loss, n_batches = 0., 0

          for ind, (X_b, y_b, w_b, ids_b) in enumerate(

              ############################################################ DEBUG

              ## hardcode pad_batches=True to work around limitations in Tensorflow

              # Turns out there are valid cases where we don't want pad-batches

              # on by default.

              #dataset.iterbatches(batch_size, pad_batches=True)):

              dataset.iterbatches(self.batch_size, pad_batches=pad_batches)):

              #dataset.iterbatches(batch_size, pad_batches=pad_batches)):

              ############################################################ DEBUG

            if ind % log_every_N_batches == 0:

              log("On batch %d" % ind, self.verbosity)

            # Run training op.

out_path='.'

models = ['tf', 'tf_robust', 'logreg', 'graphconv']

#datasets = ['muv', 'nci', 'tox21', 'sider', 'toxcast']

datasets = ['tox21']

datasets = ['tox21', 'muv']

hps = {}

hps['tf'] = [{'dropouts': [0.25], 'learning_rate': 0.001,