diff --git a/dwave/plugins/torch/nn/modules/networks.py b/dwave/plugins/torch/nn/modules/networks.py
new file mode 100755
index 0000000..f5e73e7
--- /dev/null
+++ b/dwave/plugins/torch/nn/modules/networks.py
@@ -0,0 +1,100 @@
+# Copyright 2025 D-Wave
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import torch
+from torch import nn
+
+from dwave.plugins.torch.nn.modules.conv import ConvolutionBlock, SkipConv2d
+from dwave.plugins.torch.nn.modules.linear import LinearBlock, SkipLinear
+from dwave.plugins.torch.nn.modules.vision import CACBlock
+
+
+class FullyConnectedNetwork(nn.Module):
+    def __init__(self, din, dout, depth, sn, p) -> None:
+        super().__init__(self, vars())
+        if depth == 1:
+            raise ValueError("Depth must be at least 2.")
+        self.skip = SkipLinear(din, dout)
+        big_d = max(din, dout)
+        dims = [big_d]*(depth-1) + [dout]
+        self.blocks = nn.Sequential()
+        for d_in, d_out in zip([din]+dims[:-1], dims):
+            self.blocks.append(LinearBlock(d_in, d_out, sn, p))
+            self.blocks.append(nn.Dropout(p))
+            self.blocks.append(nn.ReLU())
+        # Remove the last ReLU and Dropout
+        self.blocks.pop(-1)
+        self.blocks.pop(-1)
+
+    def forward(self, x):
+        return self.blocks(x) + self.skip(x)
+
+
+class ConvolutionNetwork(nn.Module):
+    def __init__(
+            self, channels: list[int], input_shape: tuple[int, int, int]
+    ):
+        super().__init__(self, vars())
+        channels = channels.copy()
+        input_shape = tuple(input_shape)
+        cx, hx, wx = input_shape
+        if hx != wx:
+            raise NotImplementedError("TODO")
+        self.channels = channels
+        self.cin = cx
+        self.cout = self.channels[-1]
+        self.input_shape = input_shape
+
+        channels_in = [cx] + channels[:-1]
+        self.blocks = nn.Sequential()
+        for cin, cout in zip(channels_in, channels):
+            self.blocks.append(ConvolutionBlock((cin, hx, wx), cout))
+            self.blocks.append(nn.ReLU())
+        self.blocks.pop(-1)
+        self.skip = SkipConv2d(cx, cout)
+
+    def forward(self, x):
+        x = self.blocks(x) + self.skip(x)
+        return x
+
+
+class CACNetwork(nn.Module):
+    def __init__(
+            self, channels: list[int], input_shape: tuple[int, int, int], ps: int, heads: int
+    ):
+        super().__init__(self, vars())
+        channels = channels.copy()
+        input_shape = tuple(input_shape)
+        cx, hx, wx = input_shape
+        if hx != wx:
+            raise NotImplementedError("TODO")
+        self.channels = channels
+        self.cin = cx
+        self.cout = self.channels[-1]
+        self.input_shape = input_shape
+
+        channels_in = [cx] + channels[:-1]
+        self.activations = nn.ModuleList()
+        self.blocks = nn.ModuleList()
+        self.skips = nn.ModuleList()
+        for cin, cout in zip(channels_in, channels):
+            self.blocks.append(CACBlock((cin, hx, wx), cout, ps, heads))
+            self.skips.append(SkipConv2d(cin, cout))
+            self.activations.append(nn.ReLU())
+        self.activations[-1] = torch.nn.Identity()
+
+    def forward(self, x):
+        for block, skip, act in zip(self.blocks, self.skips, self.activations):
+            x = act(block(x) + skip(x))
+        return x