ba3455b9f8d29ac7bf7a256aa754c983a6a1d276,deepchem/models/layers.py,DAGLayer,build,#DAGLayer#Any#,2933

Before Change

    self.W_list = []
    self.b_list = []
    self.dropouts = []
    init = initializers.get(self.init)
    prev_layer_size = self.n_inputs
    for layer_size in self.layer_sizes:
      self.W_list.append(init([prev_layer_size, layer_size]))
      self.b_list.append(backend.zeros(shape=[

After Change

    prev_layer_size = self.n_inputs
    for layer_size in self.layer_sizes:
      self.W_list.append(
          self.add_weight(name="kernel",
                          shape=(prev_layer_size, layer_size),
                          initializer=self.init,
                          trainable=True))
      self.b_list.append(
          self.add_weight(name="bias",
                          shape=(layer_size,),
                          initializer="zeros",
                          trainable=True))
      if self.dropout is not None and self.dropout > 0.0:
        self.dropouts.append(Dropout(rate=self.dropout))
      else:
        self.dropouts.append(None)
      prev_layer_size = layer_size
    self.W_list.append(
        self.add_weight(name="kernel",
                        shape=(prev_layer_size, self.n_outputs),
                        initializer=self.init,
                        trainable=True))
    self.b_list.append(
        self.add_weight(name="bias",
                        shape=(self.n_outputs,),
                        initializer="zeros",
                        trainable=True))
    if self.dropout is not None and self.dropout > 0.0:
      self.dropouts.append(Dropout(rate=self.dropout))
    else:

Instances