Commit 84398f9f authored by peastman's avatar peastman
Browse files

Replaced uses of deprecated layers

parent 358a54eb

deepchem/models/tensorgraph/IRV.py

+4 −4
Original line number Diff line number Diff line
@@ -64,7 +64,7 @@ class IRVLayer(Layer): 
    out_tensor = tf.concat(outputs, axis=1)



    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor
@@ -72,12 +72,12 @@ class IRVLayer(Layer): 
    V, W, b, b2 = self.V, self.W, self.b, self.b2

    self.V, self.W, self.b, self.b2 = None, None, None, None



    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return V, W, b, b2, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.V, self.W, self.b, self.b2, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.V, self.W, self.b, self.b2, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class IRVRegularize(Layer):

deepchem/models/tensorgraph/graph_layers.py

+36 −36
Original line number Diff line number Diff line
@@ -166,7 +166,7 @@ class WeaveLayer(Layer): 


    self.out_tensors = [A, P]

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = A

    return self.out_tensors
@@ -177,8 +177,8 @@ class WeaveLayer(Layer): 
    W_AA, b_AA, W_PA, b_PA, W_A, b_A = self.W_AA, self.b_AA, self.W_PA, self.b_PA, self.W_A, self.b_A

    self.W_AA, self.b_AA, self.W_PA, self.b_PA, self.W_A, self.b_A = None, None, None, None, None, None



    out_tensor, out_tensors, trainable_weights, variables = self.out_tensor, self.out_tensors, self.trainable_weights, self.variables

    self.out_tensor, self.out_tensors, self.trainable_weights, self.variables = None, [], [], []

    out_tensor, out_tensors, trainable_weights, variables = self.out_tensor, self.out_tensors, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.out_tensors, self.trainable_weights, self.trainable_variables = None, [], [], []



    return W_AP, b_AP, W_PP, W_PP, W_P, b_P, \

           W_AA, b_AA, W_PA, b_PA, W_A, b_A, \
@@ -187,7 +187,7 @@ class WeaveLayer(Layer): 
  def set_tensors(self, tensor):

    self.W_AP, self.b_AP, self.W_PP, self.W_PP, self.W_P, self.b_P, \

    self.W_AA, self.b_AA, self.W_PA, self.b_PA, self.W_A, self.b_A, \

    self.out_tensor, self.out_tensors, self.trainable_weights, self.variables = tensor

    self.out_tensor, self.out_tensors, self.trainable_weights, self.trainable_variables = tensor





def WeaveLayerFactory(**kwargs):
@@ -266,7 +266,7 @@ class WeaveGather(Layer): 


    out_tensor = output_molecules

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor
@@ -290,12 +290,12 @@ class WeaveGather(Layer): 
    W, b = self.W, self.b

    self.W, self.b = None, None



    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return W, b, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.W, self.b, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.W, self.b, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class DTNNEmbedding(Layer):
@@ -341,19 +341,19 @@ class DTNNEmbedding(Layer): 
    atom_features = tf.nn.embedding_lookup(self.embedding_list, atom_number)

    out_tensor = atom_features

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = atom_features

    return out_tensor



  def none_tensors(self):

    embedding_list = self.embedding_list

    self.embedding_list = None

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return embedding_list, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.embedding_list, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.embedding_list, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class DTNNStep(Layer):
@@ -436,19 +436,19 @@ class DTNNStep(Layer): 
                             distance_membership_i) - output_ii + atom_features

    out_tensor = outputs

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor



  def none_tensors(self):

    W_cf, W_df, W_fc, b_cf, b_df = self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df

    self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df = None, None, None, None, None

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return W_cf, W_df, W_fc, b_cf, b_df, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.W_cf, self.W_df, self.W_fc, self.b_cf, self.b_df, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class DTNNGather(Layer):
@@ -524,19 +524,19 @@ class DTNNGather(Layer): 
    output = tf.segment_sum(output, atom_membership)

    out_tensor = output

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor



  def none_tensors(self):

    W_list, b_list = self.W_list, self.b_list

    self.W_list, self.b_list = [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return W_list, b_list, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.W_list, self.b_list, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.W_list, self.b_list, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class DTNNExtract(Layer):
@@ -694,7 +694,7 @@ class DAGLayer(Layer): 


    out_tensor = batch_outputs

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor
@@ -711,12 +711,12 @@ class DAGLayer(Layer): 
  def none_tensors(self):

    W_list, b_list = self.W_list, self.b_list

    self.W_list, self.b_list = [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return W_list, b_list, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.W_list, self.b_list, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.W_list, self.b_list, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class DAGGather(Layer):
@@ -803,7 +803,7 @@ class DAGGather(Layer): 
                                 **kwargs)

    out_tensor = outputs

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor
@@ -820,12 +820,12 @@ class DAGGather(Layer): 
  def none_tensors(self):

    W_list, b_list = self.W_list, self.b_list

    self.W_list, self.b_list = [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return W_list, b_list, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.W_list, self.b_list, self.out_tensor, self.trainable_weights, self.variables = tensor

    self.W_list, self.b_list, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor





class MessagePassing(Layer):
@@ -898,19 +898,19 @@ class MessagePassing(Layer): 
    out_tensor = out



    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor



  def none_tensors(self):

    message_tensors = self.message_function.none_tensors()

    update_tensors = self.update_function.none_tensors()

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return message_tensors, update_tensors, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    message_tensors, update_tensors, self.out_tensor, self.trainable_weights, self.variables = tensor

    message_tensors, update_tensors, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor

    self.message_function.set_tensors(message_tensors)

    self.update_function.set_tensors(update_tensors)
@@ -1048,7 +1048,7 @@ class SetGather(Layer): 


    out_tensor = q_star

    if set_tensors:

      self.variables = self.trainable_weights

      self.trainable_variables = self.trainable_weights

      self.out_tensor = out_tensor

    return out_tensor
@@ -1067,9 +1067,9 @@ class SetGather(Layer): 
  def none_tensors(self):

    U, b, c, h = self.U, self.b, self.c, self.h

    self.U, self.b, self.c, self.h = None, None, None, None

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.variables

    self.out_tensor, self.trainable_weights, self.variables = None, [], []

    out_tensor, trainable_weights, variables = self.out_tensor, self.trainable_weights, self.trainable_variables

    self.out_tensor, self.trainable_weights, self.trainable_variables = None, [], []

    return U, b, c, h, out_tensor, trainable_weights, variables



  def set_tensors(self, tensor):

    self.U, self.b, self.c, self.h, self.out_tensor, self.trainable_weights, self.variables = tensor
 
    self.U, self.b, self.c, self.h, self.out_tensor, self.trainable_weights, self.trainable_variables = tensor

deepchem/models/tensorgraph/layers.py

+124 −234

File changed.

Preview size limit exceeded, changes collapsed.

deepchem/models/tensorgraph/tensor_graph.py

+7 −9
Original line number Diff line number Diff line
@@ -201,7 +201,7 @@ class TensorGraph(Model): 
          if submodel.layers is not None:

            submodel_vars = set()

            for layer in submodel.layers:

              for var in layer.variables:

              for var in layer.trainable_variables:

                submodel_vars.add(var)

        val_grad_fn = tfe.implicit_value_and_gradients(

            lambda x: self._run_graph([loss], x, True)[0])
@@ -701,7 +701,8 @@ class TensorGraph(Model): 


        for layer in self.layers.values():

          if layer.variable_values is not None:

            for var, val in zip(layer.variables, layer.variable_values):

            for var, val in zip(layer.trainable_variables,

                                layer.variable_values):

              var.assign(val)

      self.session = None

      self._training_placeholder = None
@@ -975,12 +976,9 @@ class TensorGraph(Model): 
    if not self.built:

      self.build()

    with self._get_tf("Graph").as_default():

      if tf.executing_eagerly():

        return layer.variables

      if layer.variable_scope == '':

      if layer.trainable_variables is not None:

        return layer.trainable_variables

      return []

      return tf.get_collection(

          tf.GraphKeys.TRAINABLE_VARIABLES, scope=layer.variable_scope)



  def get_layer_variable_values(self, layer):

    """Get the variable values associated with a given layer """
@@ -1000,7 +998,7 @@ class TensorGraph(Model): 
    if tf.executing_eagerly():

      variables = []

      for layer in self.layers.values():

        variables += layer.variables

        variables += layer.trainable_variables

      return variables

    else:

      with self._get_tf("Graph").as_default():

deepchem/models/tensorgraph/tests/test_layers_eager.py

+20 −20
Original line number Diff line number Diff line
@@ -24,7 +24,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      result = layer(input)

      self.assertEqual(result.shape[0], batch_size)

      self.assertEqual(result.shape[2], filters)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -48,7 +48,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.Dense(out_dim)

      result = layer(input)

      assert result.shape == (batch_size, out_dim)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -71,7 +71,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.Highway()

      result = layer(input)

      assert result.shape == (batch_size, width)

      assert len(layer.variables) == 4

      assert len(layer.trainable_variables) == 4



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -160,7 +160,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.GRU(n_hidden, batch_size)

      result, state = layer(input)

      assert result.shape == (batch_size, n_steps, n_hidden)

      assert len(layer.variables) == 3

      assert len(layer.trainable_variables) == 3



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -192,7 +192,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.LSTM(n_hidden, batch_size)

      result, state = layer(input)

      assert result.shape == (batch_size, n_steps, n_hidden)

      assert len(layer.variables) == 3

      assert len(layer.trainable_variables) == 3



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -223,7 +223,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.TimeSeriesDense(out_dim)

      result = layer(input)

      assert result.shape == (batch_size, n_steps, out_dim)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -313,7 +313,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.Variable(value)

      result = layer()

      assert np.array_equal(result.numpy(), value)

      assert len(layer.variables) == 1

      assert len(layer.trainable_variables) == 1



  def test_add(self):

    """Test invoking Add in eager mode."""
@@ -445,7 +445,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.Conv2D(filters, kernel_size=kernel_size)

      result = layer(input)

      assert result.shape == (batch_size, length, width, filters)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -474,7 +474,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.Conv3D(filters, kernel_size=kernel_size)

      result = layer(input)

      assert result.shape == (batch_size, length, width, depth, filters)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -505,7 +505,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      result = layer(input)

      assert result.shape == (batch_size, length * stride, width * stride,

                              filters)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -538,7 +538,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      result = layer(input)

      assert result.shape == (batch_size, length * stride, width * stride,

                              depth * stride, filters)

      assert len(layer.variables) == 2

      assert len(layer.trainable_variables) == 2



      # Creating a second layer should produce different results, since it has

      # different random weights.
@@ -593,7 +593,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.GraphConv(out_channels)

      result = layer(*args)

      assert result.shape == (n_atoms, out_channels)

      assert len(layer.variables) == 2 * layer.num_deg

      assert len(layer.trainable_variables) == 2 * layer.num_deg



  def test_graph_pool(self):

    """Test invoking GraphPool in eager mode."""
@@ -650,7 +650,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      assert h_out.shape == (n_test, n_feat)

      assert h_copy_out.shape == (n_test, n_feat)

      assert c_out.shape == (n_test, n_feat)

      assert len(layer.variables) == 3

      assert len(layer.trainable_variables) == 3



  def test_attn_lstm_embedding(self):

    """Test invoking AttnLSTMEmbedding in eager mode."""
@@ -665,7 +665,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      test_out, support_out = layer(test, support)

      assert test_out.shape == (n_test, n_feat)

      assert support_out.shape == (n_support, n_feat)

      assert len(layer.variables) == 7

      assert len(layer.trainable_variables) == 7



  def test_iter_ref_lstm_embedding(self):

    """Test invoking AttnLSTMEmbedding in eager mode."""
@@ -680,7 +680,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      test_out, support_out = layer(test, support)

      assert test_out.shape == (n_test, n_feat)

      assert support_out.shape == (n_support, n_feat)

      assert len(layer.variables) == 12

      assert len(layer.trainable_variables) == 12



  def test_batch_norm(self):

    """Test invoking BatchNorm in eager mode."""
@@ -691,7 +691,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.BatchNorm()

      result = layer(input)

      assert result.shape == (batch_size, n_features)

      assert len(layer.variables) == 4

      assert len(layer.trainable_variables) == 2



  def test_weighted_error(self):

    """Test invoking WeightedError in eager mode."""
@@ -716,7 +716,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.VinaFreeEnergy(n_atoms, m_nbrs, ndim, nbr_cutoff, start,

                                    stop)

      result = layer(X, Z)

      assert len(layer.variables) == 6

      assert len(layer.trainable_variables) == 6

      assert result.shape == tuple()



      # Creating a second layer should produce different results, since it has
@@ -739,8 +739,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      input2 = np.random.rand(5, 10).astype(np.float32)

      layer = layers.WeightedLinearCombo()

      result = layer(input1, input2)

      assert len(layer.variables) == 2

      expected = input1 * layer.variables[0] + input2 * layer.variables[1]

      assert len(layer.trainable_variables) == 2

      expected = input1 * layer.trainable_variables[0] + input2 * layer.trainable_variables[1]

      assert np.allclose(result, expected)



  def test_neighbor_list(self):
@@ -789,7 +789,7 @@ class TestLayersEager(test_util.TensorFlowTestCase): 
      layer = layers.AtomicConvolution(radial_params=params)

      result = layer(input1, input2, input3)

      assert result.shape == (batch_size, max_atoms, len(params))

      assert len(layer.variables) == 3

      assert len(layer.trainable_variables) == 3



  def test_alpha_share_layer(self):

    """Test invoking AlphaShareLayer in eager mode."""

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