Backend paddle: add mfnn net

deepxde/nn/paddle/__init__.py

@@ -4,11 +4,13 @@
     "DeepONet",
     "DeepONetCartesianProd",
     "FNN",
+    "MfNN",
     "MsFFN",
     "PFNN",
     "STMsFFN",
 ]
 
 from .deeponet import DeepONet, DeepONetCartesianProd
 from .fnn import FNN, PFNN
+from .mfnn import MfNN
 from .msffn import MsFFN, STMsFFN

deepxde/nn/paddle/mfnn.py

@@ -0,0 +1,120 @@
+import paddle
+
+from .nn import NN
+from .. import activations
+from .. import initializers
+from .. import regularizers
+from ... import config
+
+
+class MfNN(NN):
+    """Multifidelity neural networks."""
+
+    def __init__(
+        self,
+        layer_sizes_low_fidelity,
+        layer_sizes_high_fidelity,
+        activation,
+        kernel_initializer,
+        regularization=None,
+        residue=False,
+        trainable_low_fidelity=True,
+        trainable_high_fidelity=True,
+    ):
+        super().__init__()
+        self.layer_size_lo = layer_sizes_low_fidelity
+        self.layer_size_hi = layer_sizes_high_fidelity
+
+        self.activation = activations.get(activation)
+        self.activation_tanh = activations.get("tanh")
+        self.initializer = initializers.get(kernel_initializer)
+        self.initializer_zero = initializers.get("zeros")
+        self.trainable_lo = trainable_low_fidelity
+        self.trainable_hi = trainable_high_fidelity
+        self.residue = residue
+        self.regularizer = regularizers.get(regularization)
+
+        # low fidelity
+        self.linears_lo = self.init_dense(self.layer_size_lo, self.trainable_lo)
+
+        # high fidelity
+        # linear part
+        self.linears_hi_l = paddle.nn.Linear(
+            in_features=self.layer_size_lo[0] + self.layer_size_lo[-1],
+            out_features=self.layer_size_hi[-1],
+            weight_attr=paddle.ParamAttr(initializer=self.initializer),
+            bias_attr=paddle.ParamAttr(initializer=self.initializer_zero),
+        )
+        if not self.trainable_hi:
+            for param in self.linears_hi_l.parameters():
+                param.stop_gradient = False
+        # nonlinear part
+        self.layer_size_hi = [
+            self.layer_size_lo[0] + self.layer_size_lo[-1]
+        ] + self.layer_size_hi
+        self.linears_hi = self.init_dense(self.layer_size_hi, self.trainable_hi)
+        # linear + nonlinear
+        if not self.residue:
+            alpha = self.init_alpha(0.0, self.trainable_hi)
+            self.add_parameter("alpha", alpha)
+        else:
+            alpha1 = self.init_alpha(0.0, self.trainable_hi)
+            alpha2 = self.init_alpha(0.0, self.trainable_hi)
+            self.add_parameter("alpha1", alpha1)
+            self.add_parameter("alpha2", alpha2)
+
+    def init_dense(self, layer_size, trainable):
+        linears = paddle.nn.LayerList()
+        for i in range(len(layer_size) - 1):
+            linear = paddle.nn.Linear(
+                in_features=layer_size[i],
+                out_features=layer_size[i + 1],
+                weight_attr=paddle.ParamAttr(initializer=self.initializer),
+                bias_attr=paddle.ParamAttr(initializer=self.initializer_zero),
+            )
+            if not trainable:
+                for param in linear.parameters():
+                    param.stop_gradient = False
+            linears.append(linear)
+        return linears
+
+    def init_alpha(self, value, trainable):
+        alpha = paddle.create_parameter(
+            shape=[1],
+            dtype=config.real(paddle),
+            default_initializer=paddle.nn.initializer.Constant(value),
+        )
+        alpha.stop_gradient = not trainable
+        return alpha
+
+    def forward(self, inputs):
+        x = inputs.astype(config.real(paddle))
+        # low fidelity
+        y = x
+        for i, linear in enumerate(self.linears_lo):
+            y = linear(y)
+            if i != len(self.linears_lo) - 1:
+                y = self.activation(y)
+        y_lo = y
+
+        # high fidelity
+        x_hi = paddle.concat([x, y_lo], axis=1)
+        # linear
+        y_hi_l = self.linears_hi_l(x_hi)
+        # nonlinear
+        y = x_hi
+        for i, linear in enumerate(self.linears_hi):
+            y = linear(y)
+            if i != len(self.linears_hi) - 1:
+                y = self.activation(y)
+        y_hi_nl = y
+        # linear + nonlinear
+        if not self.residue:
+            alpha = self.activation_tanh(self.alpha)
+            y_hi = y_hi_l + alpha * y_hi_nl
+        else:
+            alpha1 = self.activation_tanh(self.alpha1)
+            alpha2 = self.activation_tanh(self.alpha2)
+            y_hi = y_lo + 0.1 * (alpha1 * y_hi_l + alpha2 * y_hi_nl)
+
+        return y_lo, y_hi