Commit 6652dd1c authored by Bharath Ramsundar's avatar Bharath Ramsundar Committed by GitHub
Browse files

Merge pull request #546 from rbharath/layer_variables 

Layers own variables
parents f8eda94c 648f3616

deepchem/models/tensorgraph/graph_layers.py

+50 −17
Original line number Diff line number Diff line
@@ -25,13 +25,16 @@ class Combine_AP(Layer): 
  def __init__(self, **kwargs):

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



  def create_tensor(self, in_layers=None, **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)

    A = in_layers[0].out_tensor

    P = in_layers[1].out_tensor

    self.out_tensor = [A, P]

    out_tensor = [A, P]

    if set_tensors:

      self.out_tensor = out_tensor

    return out_tensor





class Separate_AP(Layer):
@@ -39,11 +42,14 @@ class Separate_AP(Layer): 
  def __init__(self, **kwargs):

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



  def create_tensor(self, in_layers=None, **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)

    self.out_tensor = in_layers[0].out_tensor[0]

    out_tensor = in_layers[0].out_tensor[0]

    if set_tensors:

      self.out_tensor = out_tensor

    return out_tensor





class WeaveLayer(Layer):
@@ -108,6 +114,9 @@ class WeaveLayer(Layer): 


  def build(self):

    """ Construct internal trainable weights.



    TODO(rbharath): Need to make this not set instance variables to

    follow style in other layers.

    """



    self.W_AA = self.init([self.n_atom_input_feat, self.n_hidden_AA])
@@ -147,7 +156,7 @@ class WeaveLayer(Layer): 
      self.trainable_weights.extend(

          [self.W_AP, self.b_AP, self.W_PP, self.b_PP, self.W_P, self.b_P])



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

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

    """ description and explanation refer to deepchem.nn.WeaveLayer

    parent layers: [atom_features, pair_features], pair_split, atom_to_pair

    """
@@ -190,7 +199,11 @@ class WeaveLayer(Layer): 
      P = self.activation(P)

    else:

      P = pair_features

    self.out_tensor = [A, P]

    out_tensor = [A, P]

    if set_tensors:

      self.variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor





class WeaveGather(Layer):
@@ -241,7 +254,7 @@ class WeaveGather(Layer): 
    else:

      self.trainable_weights = None



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

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

    """ description and explanation refer to deepchem.nn.WeaveGather

    parent layers: atom_features, atom_split

    """
@@ -261,7 +274,11 @@ class WeaveGather(Layer): 
    if self.gaussian_expand:

      output_molecules = tf.matmul(output_molecules, self.W) + self.b

      output_molecules = self.activation(output_molecules)

    self.out_tensor = output_molecules

    out_tensor = output_molecules

    if set_tensors:

      self.variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor



  def gaussian_histogram(self, x):

    gaussian_memberships = [(-1.645, 0.283), (-1.080, 0.170), (-0.739, 0.134),
@@ -312,7 +329,7 @@ class DTNNEmbedding(Layer): 
        [self.periodic_table_length, self.n_embedding])

    self.trainable_weights = [self.embedding_list]



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

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

    """description and explanation refer to deepchem.nn.DTNNEmbedding

    parent layers: atom_number

    """
@@ -375,7 +392,7 @@ class DTNNStep(Layer): 
        self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df

    ]



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

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

    """description and explanation refer to deepchem.nn.DTNNStep

    parent layers: atom_features, distance, distance_membership_i, distance_membership_j

    """
@@ -406,7 +423,11 @@ class DTNNStep(Layer): 
    # for atom i, sum the influence from all other atom j in the molecule

    outputs = tf.segment_sum(outputs,

                             distance_membership_i) - output_ii + atom_features

    self.out_tensor = outputs

    out_tensor = outputs

    if set_tensors:

      self.variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor





class DTNNGather(Layer):
@@ -461,7 +482,7 @@ class DTNNGather(Layer): 


    self.trainable_weights = self.W_list + self.b_list



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

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

    """description and explanation refer to deepchem.nn.DTNNGather

    parent layers: atom_features, atom_membership

    """
@@ -476,7 +497,11 @@ class DTNNGather(Layer): 
      output = tf.matmul(output, W) + self.b_list[i]

      output = self.activation(output)

    output = tf.segment_sum(output, atom_membership)

    self.out_tensor = output

    out_tensor = output

    if set_tensors:

      self.variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor





class DAGLayer(Layer):
@@ -548,7 +573,7 @@ class DAGLayer(Layer): 


    self.trainable_weights = self.W_list + self.b_list



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

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

    """description and explanation refer to deepchem.nn.DAGLayer

    parent layers: atom_features, parents, calculation_orders, calculation_masks, n_atoms

    """
@@ -614,7 +639,11 @@ class DAGLayer(Layer): 
      graph_features = tf.scatter_nd_update(graph_features, target_index,

                                            batch_outputs)



    self.out_tensor = batch_outputs

    out_tensor = batch_outputs

    if set_tensors:

      self.variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor



  def DAGgraph_step(self, batch_inputs, W_list, b_list):

    outputs = batch_inputs
@@ -686,7 +715,7 @@ class DAGGather(Layer): 


    self.trainable_weights = self.W_list + self.b_list



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

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

    """description and explanation refer to deepchem.nn.DAGGather

    parent layers: atom_features, membership

    """
@@ -704,7 +733,11 @@ class DAGGather(Layer): 
    graph_features = tf.segment_sum(atom_features, membership)

    # sum all graph outputs

    outputs = self.DAGgraph_step(graph_features, self.W_list, self.b_list)

    self.out_tensor = outputs

    out_tensor = outputs

    if set_tensors:

      self.variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor



  def DAGgraph_step(self, batch_inputs, W_list, b_list):

    outputs = batch_inputs

deepchem/models/tensorgraph/layers.py

+196 −109

File changed.

Preview size limit exceeded, changes collapsed.

deepchem/nn/model_ops.py

+0 −5
Original line number Diff line number Diff line
@@ -823,11 +823,6 @@ def fully_connected_layer(tensor, 
  ValueError

    If input tensor is not 2D.

  """

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

  #print("fully_connected_layer")

  #print("tensor")

  #print(tensor)

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

  if weight_init is None:

    num_features = tensor.get_shape()[-1].value

    weight_init = tf.truncated_normal([num_features, size], stddev=0.01)

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