debugging for ani and mpnn

        # Only fetch the first set of unique samples

        results.append(result[:n_valid_samples])

      return np.concatenate(results, axis=0)

  def predict_on_generator(self, generator, transformers=[]):

    return self.predict_proba_on_generator(generator, transformers)

 No newline at end of file

        feed_dict[self._training_placeholder] = 0.0

        feed_results = self.session.run(outputs, feed_dict=feed_dict)

        if len(feed_results) > 1:

          if len(transformers):

            raise ValueError("Does not support transformations "

                             "for multiple outputs.")

          result = undo_transforms(np.concatenate(feed_results, 1), transformers)

          feed_results = [result[:, i:i+1] for i in range(result.shape[1])]

        elif len(feed_results) == 1:

          result = undo_transforms(feed_results[0], transformers)

          feed_results = [result]

"""

Script that trains ANI models on qm8 dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

np.random.seed(123)

import tensorflow as tf

tf.set_random_seed(123)

import deepchem as dc

# Load Tox21 dataset

tasks, datasets, transformers = dc.molnet.load_qm8(

    featurizer='BPSymmetryFunction')

train_dataset, valid_dataset, test_dataset = datasets

# Batch size of models

max_atoms = 26

batch_size = 128

layer_structures = [128, 128, 64]

atom_number_cases = [1, 6, 7, 8, 9]

ANItransformer = dc.trans.ANITransformer(

    max_atoms=max_atoms, atom_cases=atom_number_cases)

train_dataset = ANItransformer.transform(train_dataset)

valid_dataset = ANItransformer.transform(valid_dataset)

test_dataset = ANItransformer.transform(test_dataset)

n_feat = ANItransformer.get_num_feats() - 1

# Fit models

metric = [

    dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),

    dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")

]

model = dc.models.ANIRegression(

    len(tasks),

    max_atoms,

    n_feat,

    layer_structures=layer_structures,

    atom_number_cases=atom_number_cases,

    batch_size=batch_size,

    learning_rate=0.001,

    use_queue=False,

    mode="regression")

# Fit trained model

model.fit(train_dataset, nb_epoch=300, checkpoint_interval=100)

print("Evaluating model")

train_scores = model.evaluate(train_dataset, metric, transformers)

valid_scores = model.evaluate(valid_dataset, metric, transformers)

print("Train scores")

print(train_scores)

print("Validation scores")

print(valid_scores)