Commit dcec083c authored by miaecle's avatar miaecle
Browse files

old-style implementation of BP

parent e1ca2abc

deepchem/models/tensorgraph/graph_layers.py

+18 −1
Original line number Diff line number Diff line
@@ -524,9 +524,10 @@ class DTNNGather(Layer): 
    self.build()

    output = in_layers[0].out_tensor

    atom_membership = in_layers[1].out_tensor

    for i, W in enumerate(self.W_list):

    for i, W in enumerate(self.W_list[:-1]):

      output = tf.matmul(output, W) + self.b_list[i]

      output = self.activation(output)

    output = tf.matmul(output, self.W_list[-1]) + self.b_list[-1]

    output = tf.segment_sum(output, atom_membership)

    out_tensor = output

    if set_tensors:
@@ -535,6 +536,22 @@ class DTNNGather(Layer): 
    return out_tensor





class DTNNExtract(Layer):



  def __init__(self, task_id, **kwargs):

    self.task_id = task_id

    super(DTNNExtract, self).__init__(**kwargs)



  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):

    if in_layers is None:

      in_layers = self.in_layers

    in_layers = convert_to_layers(in_layers)

    output = in_layers[0].out_tensor

    out_tensor = output[:, self.task_id:self.task_id + 1]

    self.out_tensor = out_tensor

    return out_tensor





class DAGLayer(Layer):

  """ TensorGraph style implementation

    The same as deepchem.nn.DAGLayer

deepchem/models/tensorgraph/models/graph_models.py

+4 −4
Original line number Diff line number Diff line
@@ -5,7 +5,7 @@ import tensorflow as tf 
from deepchem.feat.mol_graphs import ConvMol

from deepchem.metrics import to_one_hot, from_one_hot

from deepchem.models.tensorgraph.graph_layers import WeaveLayer, WeaveGather, \

    Combine_AP, Separate_AP, DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, DAGGather

    Combine_AP, Separate_AP, DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, DAGGather, DTNNExtract

from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, SoftMaxCrossEntropy, GraphConv, BatchNorm, \

    GraphPool, GraphGather, WeightedError

from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature
@@ -273,14 +273,14 @@ class DTNNTensorGraph(TensorGraph): 
        ])

    dtnn_gather = DTNNGather(

        n_embedding=self.n_embedding,

        n_outputs=self.n_hidden,

        layer_sizes=[self.n_hidden],

        n_outputs=self.n_tasks,

        in_layers=[dtnn_layer2, self.atom_membership])



    costs = []

    self.labels_fd = []

    for task in range(self.n_tasks):

      regression = Dense(

          out_channels=1, activation_fn=None, in_layers=[dtnn_gather])

      regression = DTNNExtract(task, in_layers=[dtnn_gather])

      self.add_output(regression)



      label = Label(shape=(None, 1))

deepchem/models/tensorgraph/symmetry_functions.py

+1 −7
Original line number Diff line number Diff line
@@ -20,9 +20,6 @@ from deepchem.models.tensorgraph.layers import convert_to_layers 




class DistanceMatrix(Layer):

  """ TensorGraph style implementation

    The same as deepchem.nn.WeaveGather

    """



  def __init__(self, max_atoms, **kwargs):

    """
@@ -54,9 +51,6 @@ class DistanceMatrix(Layer): 




class DistanceCutoff(Layer):

  """ TensorGraph style implementation

    The same as deepchem.nn.WeaveGather

    """



  def __init__(self, max_atoms, cutoff=6 / 0.52917721092, **kwargs):

    """

deepchem/models/tf_new_models/graph_models.py

+37 −1
Original line number Diff line number Diff line
@@ -11,7 +11,7 @@ __license__ = "MIT" 


import tensorflow as tf

from deepchem.nn.layers import GraphGather

from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, AlternateWeaveGraphTopology

from deepchem.models.tf_new_models.graph_topology import GraphTopology, DTNNGraphTopology, DAGGraphTopology, WeaveGraphTopology, AlternateWeaveGraphTopology, BPSymmetryFunctionGraphTopology





class SequentialGraph(object):
@@ -120,6 +120,42 @@ class SequentialDTNNGraph(SequentialGraph): 
      self.layers.append(layer)





class BPSymmetryFunctionGraph(SequentialGraph):



  def __init__(self, max_atoms):

    self.graph = tf.Graph()

    with self.graph.as_default():

      self.graph_topology = BPSymmetryFunctionGraphTopology(max_atoms)

    self.layers = []



  def add(self, layer):

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

    with self.graph.as_default():

      if type(layer).__name__ in ['DistanceMatrix']:

        self.d = layer(self.graph_topology.inputs)

      elif type(layer).__name__ in ['DistanceCutoff']:

        self.d_cutoff = layer(

            [self.d, self.graph_topology.atom_flag_placeholder])

      elif type(layer).__name__ in ['RadialSymmetry']:

        self.symmetry1 = layer([self.d_cutoff, self.d])

      elif type(layer).__name__ in ['AngularSymmetry']:

        self.symmetry2 = layer(

            [self.d_cutoff, self.d, self.graph_topology.atom_coordinates])

      elif type(layer).__name__ in ['DTNNEmbedding']:

        self.atom_embedding = layer(self.graph_topology.atom_number_placeholder)

      elif type(layer).__name__ in ['BPFeatureMerge']:

        self.output = layer([

            self.atom_embedding, self.symmetry1, self.symmetry2,

            self.graph_topology.atom_flag_placeholder

        ])

      elif type(layer).__name__ in ['BPGather']:

        self.output = layer(

            [self.output, self.graph_topology.atom_flag_placeholder])

      else:

        self.output = layer(self.output)

      self.layers.append(layer)





class SequentialDAGGraph(SequentialGraph):

  """SequentialGraph for DAG models

  """

deepchem/models/tf_new_models/graph_topology.py

+39 −0
Original line number Diff line number Diff line
@@ -260,6 +260,45 @@ class DTNNGraphTopology(GraphTopology): 
    return dict_DTNN





class BPSymmetryFunctionGraphTopology(GraphTopology):



  def __init__(self, max_atoms, name='DTNN_topology'):

    self.name = name

    self.max_atoms = max_atoms



    self.atom_number_placeholder = tf.placeholder(

        dtype='int32',

        shape=(None, self.max_atoms),

        name=self.name + '_atom_number')

    self.atom_flag_placeholder = tf.placeholder(

        dtype='float32',

        shape=(None, self.max_atoms, self.max_atoms),

        name=self.name + '_atom_flag')

    self.atom_coordinates = tf.placeholder(

        dtype='float32',

        shape=(None, self.max_atoms, 3),

        name=self.name + '_coordinates')



    # Define the list of tensors to be used as topology

    self.topology = [self.atom_coordinates, self.atom_flag_placeholder]

    self.inputs = [self.atom_number_placeholder]

    self.inputs += self.topology



  def get_atom_number_placeholder(self):

    return self.atom_number_placeholder



  def batch_to_feed_dict(self, batch):



    feed_dict = dict()

    flags = np.sign(np.array(batch[:, :, 0]))

    feed_dict[self.atom_flag_placeholder] = np.stack([flags]*self.max_atoms, axis=2)*\

            np.stack([flags]*self.max_atoms, axis=1)

    feed_dict[self.atom_number_placeholder] = np.array(

        batch[:, :, 0], dtype=int)

    feed_dict[self.atom_coordinates] = np.array(batch[:, :, 1:], dtype=float)

    return feed_dict





class DAGGraphTopology(GraphTopology):

  """GraphTopology for DAG models

  """

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