Loading deepchem/models/torch_models/layers.py +7 −3 Original line number Diff line number Diff line Loading @@ -733,7 +733,7 @@ class PositionwiseFeedForward(nn.Module): d_input: int = 1024, d_hidden: int = 1024, d_output: int = 1024, activation: Any = 'leakyrelu', activation: str = 'leakyrelu', n_layers: int = 1, dropout_p: float = 0.0): """Initialize a PositionwiseFeedForward layer. Loading @@ -757,7 +757,7 @@ class PositionwiseFeedForward(nn.Module): super(PositionwiseFeedForward, self).__init__() if activation == 'relu': self.activation = nn.ReLU() self.activation: Any = nn.ReLU() elif activation == 'leakyrelu': self.activation = nn.LeakyReLU(0.1) Loading @@ -778,17 +778,21 @@ class PositionwiseFeedForward(nn.Module): elif activation == "linear": self.activation = lambda x: x <<<<<<< HEAD self.n_layers = n_layers <<<<<<< HEAD d_output = d_output if d_output is not None else d_input d_hidden = d_hidden if d_hidden is not None else d_input ======= ======= self.n_layers: int = n_layers >>>>>>> Mypy fixes d_output = d_output if d_output != 0 else d_input d_hidden = d_hidden if d_hidden != 0 else d_input >>>>>>> Rebase+Update if n_layers == 1: self.linears = [nn.Linear(d_input, d_output)] self.linears: Any = [nn.Linear(d_input, d_output)] else: self.linears = [nn.Linear(d_input, d_hidden)] + \ Loading Loading
deepchem/models/torch_models/layers.py +7 −3 Original line number Diff line number Diff line Loading @@ -733,7 +733,7 @@ class PositionwiseFeedForward(nn.Module): d_input: int = 1024, d_hidden: int = 1024, d_output: int = 1024, activation: Any = 'leakyrelu', activation: str = 'leakyrelu', n_layers: int = 1, dropout_p: float = 0.0): """Initialize a PositionwiseFeedForward layer. Loading @@ -757,7 +757,7 @@ class PositionwiseFeedForward(nn.Module): super(PositionwiseFeedForward, self).__init__() if activation == 'relu': self.activation = nn.ReLU() self.activation: Any = nn.ReLU() elif activation == 'leakyrelu': self.activation = nn.LeakyReLU(0.1) Loading @@ -778,17 +778,21 @@ class PositionwiseFeedForward(nn.Module): elif activation == "linear": self.activation = lambda x: x <<<<<<< HEAD self.n_layers = n_layers <<<<<<< HEAD d_output = d_output if d_output is not None else d_input d_hidden = d_hidden if d_hidden is not None else d_input ======= ======= self.n_layers: int = n_layers >>>>>>> Mypy fixes d_output = d_output if d_output != 0 else d_input d_hidden = d_hidden if d_hidden != 0 else d_input >>>>>>> Rebase+Update if n_layers == 1: self.linears = [nn.Linear(d_input, d_output)] self.linears: Any = [nn.Linear(d_input, d_output)] else: self.linears = [nn.Linear(d_input, d_hidden)] + \ Loading
