Added sequential example topology to tests.

from deepchem.models.tf_keras_models.graph_topology import GraphTopology

class SequentialGraphModel(object):

  """An analog of Keras Sequential model for Graph data.

  Like the Sequential class from Keras, but automatically passes topology

  placeholders from GraphTopology to each graph layer (from keras_layers) added

  to the network. Non graph layers don't get the extra placeholders. 

  """

  def __init__(self, n_atoms, n_feat, batch_size):

    """

    Parameters

    ----------

    n_atoms: int

      (Max?) Number of atoms in system.

    n_feat: int

      Number of features per atom.

    batch_size: int

      Batch size for training models.

    """

    #super(SequentialGraphModel, self).__init__()

    self.batch_size = batch_size

    # Create graph topology and x

    self.graph_topology = GraphTopology(n_atoms, n_feat, self.batch_size)

    self.output = self.graph_topology.get_nodes()

    self.layers = []  # Keep track of the layers

    self.output = self.graph_topology.get_atom_features_placeholder()

    # Keep track of the layers

    self.layers = []  

  def add(self, layer):

    """Adds a new layer to model."""

    # Generate dicts

    deg_adj_dict = dict(zip(self.deg_adj_lists_placeholders, deg_adj_lists))

    atoms_dict = {self.nodes_placeholder : atoms,

    atoms_dict = {self.atom_features_placeholder : atoms,

                  self.deg_slice_placeholder : batch.deg_slice,

                  self.membership_placeholder : batch.membership}

    return merge_dicts([atoms_dict, deg_adj_dict])

import unittest

from tensorflow.python.framework import test_util

from keras.layers import Dense, BatchNormalization

from deepchem.models.tf_keras_models.containers import GraphContainer

from deepchem.models.tf_keras_models.graph_topology import GraphTopology

from deepchem.models.tf_keras_models.graph_models import SequentialGraphModel

from deepchem.models.tf_keras_models.keras_layers import GraphConv

from deepchem.models.tf_keras_models.keras_layers import GraphPool

from deepchem.models.tf_keras_models.keras_layers import GraphGather

class TestGraphModels(test_util.TensorFlowTestCase):

  """

    batch_size = 3

    graph_model = SequentialGraphModel(n_atoms, n_feat, batch_size)

    assert len(graph_model.layers) == 0

  def test_sample_sequential_architecture(self):

    """Tests that a representative architecture can be created."""

    n_atoms = 5

    n_feat = 10

    batch_size = 3

    graph_model = SequentialGraphModel(n_atoms, n_feat, batch_size)

    graph_model.add(GraphConv(64, activation='relu'))

    graph_model.add(BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(GraphPool())

    # Gather Projection

    graph_model.add(Dense(128, activation='relu'))

    graph_model.add(BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(GraphGather(batch_size, activation="tanh"))

    # There should be 8 layers in graph_model

    assert len(graph_model.layers) == 6