unignored pdfs, added network arch diagrams

thesis/third_party/PlotNeuralNet/deepsad/arch_lenet_encoder.py

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+# subter_lenet_arch.py
+# Requires running from inside the PlotNeuralNet repo, like: python3 ../subter_lenet_arch.py
+import sys, argparse
+
+sys.path.append("../")  # import pycore from repo root
+
+from pycore.tikzeng import *
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--rep_dim", type=int, default=1024, help="latent size for FC")
+args = parser.parse_args()
+REP = int(args.rep_dim)
+
+# Visual scales so the huge width doesn't dominate the figure
+H32, H16, H8 = 26, 18, 12
+D2048, D1024, D512 = 52, 36, 24
+W1, W4, W8 = 1, 2, 4
+
+
+arch = [
+    to_head(".."),
+    to_cor(),
+    to_begin(),
+    # --------------------------- ENCODER ---------------------------
+    # Input 1×32×2048  (caption carries H×W; s_filer is numeric)
+    to_Conv(
+        "input",
+        s_filer="{{2048×32}}",
+        n_filer=1,
+        offset="(0,0,0)",
+        to="(0,0,0)",
+        height=H32,
+        depth=D2048,
+        width=W1,
+        caption="input",
+    ),
+    # Conv1 (5x5, same): 1->8, 32×2048
+    to_Conv(
+        "conv1",
+        s_filer="{{1024×16}}",
+        n_filer=8,
+        offset="(2,0,0)",
+        to="(input-east)",
+        height=H32,
+        depth=D2048,
+        width=W8,
+        caption="conv1",
+    ),
+    # Pool1 2×2: 32×2048 -> 16×1024
+    # to_connection("input", "conv1"),
+    to_Pool(
+        "pool1",
+        offset="(0,0,0)",
+        to="(conv1-east)",
+        height=H16,
+        depth=D1024,
+        width=W8,
+        caption="",
+    ),
+    # Conv2 (5x5, same): 8->4, stays 16×1024
+    to_Conv(
+        "conv2",
+        s_filer="{{512×8}}",
+        n_filer=4,
+        offset="(2,0,0)",
+        to="(pool1-east)",
+        height=H16,
+        depth=D1024,
+        width=W4,
+        caption="conv2",
+    ),
+    # Pool2 2×2: 16×1024 -> 8×512
+    # to_connection("pool1", "conv2"),
+    to_Pool(
+        "pool2",
+        offset="(0,0,0)",
+        to="(conv2-east)",
+        height=H8,
+        depth=D512,
+        width=W4,
+        caption="",
+    ),
+    # FC -> rep_dim (use numeric n_filer)
+    to_fc(
+        "fc1",
+        n_filer="{{4×512×8}}",
+        offset="(2,0,0)",
+        to="(pool2-east)",
+        height=1.3,
+        depth=D512,
+        width=W1,
+        caption=f"FC",
+    ),
+    # to_connection("pool2", "fc1"),
+    # --------------------------- LATENT  ---------------------------
+    to_Conv(
+        "latent",
+        n_filer="",
+        s_filer="latent dim",
+        offset="(2,0,0)",
+        to="(fc1-east)",
+        height=H8 * 1.6,
+        depth=1.3,
+        width=W1,
+        caption=f"Latent Space",
+    ),
+    to_end(),
+]
+
+
+def main():
+    name = "subter_lenet_arch"
+    to_generate(arch, name + ".tex")
+
+
+if __name__ == "__main__":
+    main()