{
  "schemaVersion": 1,
  "lessonId": "networks-04",
  "contentHash": "d1e8c9915efd949a1601e7b22b00a7c21ba365e4e699d9cf35d95e316d8f69ce",
  "executedAt": "2026-10-20T10:46:26.728813+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Held-out loss: 0.015700625 accuracy: 1.0 confusion:\n [[12  0]\n [ 0 12]]\nHeld-out loss: 0.015700625 accuracy: 1.0 confusion:\n [[12  0]\n [ 0 12]]\nTrue-class counts: [12 12] row-normalized confusion: [[1. 0.]\n [0. 1.]]\nAbsent class recall: undefined, not zero.\nPASS: networks-04\n",
  "notebookCells": [
    {
      "sourceHash": "fa28ec789f99924224e586840151d0adab6e75d0f3944de11f06cad0a9c3f15b",
      "outputs": []
    },
    {
      "sourceHash": "7b19a6bdb4a337163e371c6321f98f91563e8f4f978ef0eab7f34d3da3afbf45",
      "outputs": []
    },
    {
      "sourceHash": "87fb87f13409668ee21b104716225dd6299b0b38a88e0187603819d892661f26",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Held-out loss: 0.015700625 accuracy: 1.0 confusion:\n",
            " [[12  0]\n",
            " [ 0 12]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "c666fc9fc5158441b9dbec51f35b94337b4c9fc25ce58c29f63af28530d00136",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Held-out loss: 0.015700625 accuracy: 1.0 confusion:\n",
            " [[12  0]\n",
            " [ 0 12]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "f38c283e210dfef75dbcf1ae1f29371440007e7456369830c4913f4b96fd642c",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "be12030f00e3bfa788236b0be39c02ee17efa8a3d79067d0529903400c74df83",
      "outputs": []
    },
    {
      "sourceHash": "e7d6f46765f442ebf379692ecdbe42a08cce627c3b0dbdd9b05b47aef67fe6f1",
      "outputs": []
    },
    {
      "sourceHash": "c70ce67712ef83d01e49b9b3c293932969b9bfeea7ce1b4486f07957129c54ce",
      "outputs": []
    },
    {
      "sourceHash": "db2a26ea12b4a6867b8eed500a60eaaa6ba29986e62d617ed39ac0d7c08ab2b7",
      "outputs": []
    },
    {
      "sourceHash": "5d657b5e2df1ae9b1483c53280d11015a391d71d290c37b2b79d9d6cd57daa17",
      "outputs": []
    },
    {
      "sourceHash": "2e13990cc18ea816054b81f75a241fb55fa5a20d4a867be9b735a036462bfb3d",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True-class counts: [12 12] row-normalized confusion: [[1. 0.]\n",
            " [0. 1.]]\n",
            "Absent class recall: undefined, not zero.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "6ca3a8e0a4698297751a1030f29ea1fd0d7f5e5d707905d04b8fb395e89991c1",
      "outputs": []
    },
    {
      "sourceHash": "3b947d57879547e67186ce6beb8601da3d7d7425e65f42d79d568c72e3b2f9ca",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}