yapf

    dim = 2

    batch_size = 10

    mean_tensor = np.random.rand(dim)

    std_tensor = np.random.rand(

        1,)

    std_tensor = np.random.rand(1,)

    with self.test_session() as sess:

      mean_tensor = tf.convert_to_tensor(mean_tensor, dtype=tf.float32)

      std_tensor = tf.convert_to_tensor(std_tensor, dtype=tf.float32)

def sluice_model(batch_size, tasks):

  model = TensorGraph(

      model_dir=model_dir, batch_size=batch_size, use_queue=False, tensorboard=True)

      model_dir=model_dir,

      batch_size=batch_size,

      use_queue=False,

      tensorboard=True)

  atom_features = Feature(shape=(None, 75))

  degree_slice = Feature(shape=(None, 2), dtype=tf.int32)

  membership = Feature(shape=(None,), dtype=tf.int32)

  batch_norm1a = BatchNorm(in_layers=[as1[0]])

  batch_norm1b = BatchNorm(in_layers=[as1[1]])

  gp1a = GraphPool(in_layers=[batch_norm1a, degree_slice, membership] + deg_adjs)

  gp1b = GraphPool(in_layers=[batch_norm1b, degree_slice, membership] + deg_adjs)

  gp1a = GraphPool(

      in_layers=[batch_norm1a, degree_slice, membership] + deg_adjs)

  gp1b = GraphPool(

      in_layers=[batch_norm1b, degree_slice, membership] + deg_adjs)

  gc2a = GraphConv(

      64,

  return model, feed_dict_generator, labels, task_weights

model_dir = "tmp/graphconv"

# Load Tox21 dataset

print(train_dataset.data_dir)

print(valid_dataset.data_dir)

# Fit models

metric = dc.metrics.Metric(

    dc.metrics.roc_auc_score, np.mean, mode="classification")

num_epochs = 10

model, generator, labels, task_weights = sluice_model(

    batch_size, tox21_tasks)

model, generator, labels, task_weights = sluice_model(batch_size, tox21_tasks)

model.fit_generator(generator(train_dataset, batch_size, epochs=num_epochs), checkpoint_interval=1000)

model.fit_generator(

    generator(train_dataset, batch_size, epochs=num_epochs),

    checkpoint_interval=1000)

print("Evaluating model")

train_scores = model.evaluate_generator(

    generator(train_dataset, batch_size),

    [metric],

    generator(train_dataset, batch_size), [metric],

    transformers,

    labels,

    weights=[task_weights],

    generator(valid_dataset, batch_size), [metric],

    transformers,

    labels,

    weights=[task_weights], per_task_metrics = True)

    weights=[task_weights],

    per_task_metrics=True)

print("Train scores")

print(train_scores)