{
 "cells": [
  {
   "cell_type": "code",
   "id": "d6cb560a-707c-4c1e-b50f-2b9c8037a3c7",
   "metadata": {
    "editable": true,
    "execution": {
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     "start_time": "2026-06-02T19:04:41.514717608Z"
    }
   },
   "source": [
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "from model.model import Model\n",
    "from rbm.entity import Entity, EntityParams, TrainingParams\n",
    "from rbm.matrix import Mat, np, rms_error_accu, sample_gaussian\n",
    "from compat.torch import Optimizer\n",
    "from image.sub_image import SubImageExtract, SubImageCombine, normalize\n",
    "import math\n",
    "import random"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "4065302d-862f-4639-99d7-e396d055434c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:11.032871Z",
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     "end_time": "2026-06-02T19:04:44.881601477Z",
     "start_time": "2026-06-02T19:04:44.832350671Z"
    }
   },
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))                            \n",
    "])                              "
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "ffe48fc6-0f57-48a0-b5e9-aa68cbc8edf1",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:11.037686Z",
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     "start_time": "2026-06-02T19:04:44.883213288Z"
    }
   },
   "source": [
    "train_data = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform, download=True)\n",
    "test_data = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform, download=True)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=2)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=True, num_workers=2)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "33501c0f-6d78-43e9-b2ce-adbe40b6d95a",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:12.468794Z",
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     "start_time": "2026-06-02T19:04:46.395811857Z"
    }
   },
   "source": [
    "print(train_data)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset CIFAR10\n",
      "    Number of datapoints: 50000\n",
      "    Root location: ./data\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               ToTensor()\n",
      "               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
      "           )\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "aedf601a-9084-4047-98e8-481f4d41a2d9",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:12.474580Z",
     "iopub.status.busy": "2026-06-01T18:05:12.474238Z",
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    },
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    "ExecuteTime": {
     "end_time": "2026-06-02T19:04:46.691165557Z",
     "start_time": "2026-06-02T19:04:46.454000722Z"
    }
   },
   "source": [
    "N = 500\n",
    "n_side = 10\n",
    "PATCH  = 8\n",
    "STRIDE = 4\n",
    "N_VIS  = 3 * PATCH * PATCH\n",
    "N_HID  = 128\n",
    "\n",
    "image, label = train_data[0]\n",
    "size = len(torch.flatten(image))\n",
    "train_images = np.zeros(shape=(N, size))\n",
    "train_images_tensor = torch.Tensor(N, size)\n",
    "for i in range(N):\n",
    "    image, label = train_data[i]\n",
    "    flat_array = torch.flatten(image)\n",
    "    train_images[i, :] = np.asarray(flat_array.numpy())\n",
    "    train_images_tensor[i, :] = flat_array\n",
    "print(image.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32])\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "5ee865c3-2af4-487f-a184-1291f99839b1",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:12.750649Z",
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     "end_time": "2026-06-02T19:04:49.284834012Z",
     "start_time": "2026-06-02T19:04:46.693394471Z"
    }
   },
   "source": [
    "image_indices = np.array(random.sample(range(min(N, n_side*n_side)), n_side*n_side))\n",
    "print(f\"train_images.shape:{train_images.shape}\")\n",
    "mean_training_batch = np.reshape(np.repeat(np.mean(train_images, axis=1), 3072, axis=0), train_images.shape)\n",
    "var_training_batch  = np.reshape(np.repeat(np.std(train_images,  axis=1), 3072, axis=0), train_images.shape)\n",
    "train_images_norm = (train_images - mean_training_batch) / var_training_batch\n",
    "sub_generator = SubImageExtract(PATCH, PATCH, STRIDE, STRIDE)\n",
    "train_subimages = sub_generator(np.reshape(train_images, (N, 3, 32, 32)), 3)\n",
    "print(train_subimages.shape)\n",
    "train_subimages = np.reshape(train_subimages, (train_subimages.shape[0], N_VIS))\n",
    "print(train_subimages.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_images.shape:(500, 3072)\n",
      "(24500, 3, 8, 8)\n",
      "(24500, 192)\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "08143a53-12a1-4896-a4b2-166dedf0e021",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:15.452497Z",
     "iopub.status.busy": "2026-06-01T18:05:15.452325Z",
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    "slideshow": {
     "slide_type": ""
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2026-06-02T19:04:49.336334651Z",
     "start_time": "2026-06-02T19:04:49.286632802Z"
    }
   },
   "source": [
    "class TestModel(Model):\n",
    "    def __init__(self, name: str, work_dir: str = '.'):\n",
    "        super().__init__(name, work_dir)\n",
    "        self.unit1 = Entity((N_VIS, N_HID), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), TrainingParams(learning_rate=0.005, momentum=0.9, num_epochs=1000, mini_batch_size=1000, weight_decay=0.0, l1_lambda=0.02))\n",
    "    \n",
    "    def forward(self, x: Mat):\n",
    "        x = self.unit1.forward(x)\n",
    "        return x\n",
    "    \n",
    "    def backward(self, x: Mat):\n",
    "        x = self.unit1.reconstruct(x)\n",
    "        return x"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "6b3be9f6-38e1-442d-a7bd-1f3c164ec8d4",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:15.457849Z",
     "iopub.status.busy": "2026-06-01T18:05:15.457672Z",
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     "end_time": "2026-06-02T19:05:40.992357301Z",
     "start_time": "2026-06-02T19:04:49.337748956Z"
    }
   },
   "source": [
    "work_dir = \"results\"\n",
    "prj_name = \"cifar_test\"\n",
    "prj_root = \"/home/jens/work/repos/Rbm\"\n",
    "\n",
    "# Create model\n",
    "model = TestModel(prj_name, \"results\")\n",
    "\n",
    "# Init state\n",
    "model.init(0.01)\n",
    "\n",
    "# load state\n",
    "model.load()\n",
    "\n",
    "# Train\n",
    "#model.train(normalize(train_subimages))\n",
    "model.train(train_subimages)\n",
    "\n",
    "# save state\n",
    "model.save()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 0%\n",
      "Entity-192x128: err_rms              : 0.02949249531832098\n",
      "Entity-192x128: l2_norm              : 0.5378584384984684\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 10%\n",
      "Entity-192x128: err_rms              : 0.026913473496053525\n",
      "Entity-192x128: l2_norm              : 0.5267429401932563\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 20%\n",
      "Entity-192x128: err_rms              : 0.02760073556665024\n",
      "Entity-192x128: l2_norm              : 0.5292164648393289\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 30%\n",
      "Entity-192x128: err_rms              : 0.027094088980733295\n",
      "Entity-192x128: l2_norm              : 0.5318666114176326\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 40%\n",
      "Entity-192x128: err_rms              : 0.02665192232442506\n",
      "Entity-192x128: l2_norm              : 0.537367367178328\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 50%\n",
      "Entity-192x128: err_rms              : 0.02704404682947381\n",
      "Entity-192x128: l2_norm              : 0.5423956531457014\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 60%\n",
      "Entity-192x128: err_rms              : 0.026483442118845256\n",
      "Entity-192x128: l2_norm              : 0.5488288704660952\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 70%\n",
      "Entity-192x128: err_rms              : 0.026737082232912428\n",
      "Entity-192x128: l2_norm              : 0.5567606142917088\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 80%\n",
      "Entity-192x128: err_rms              : 0.026648459305041356\n",
      "Entity-192x128: l2_norm              : 0.5632874430985447\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 90%\n",
      "Entity-192x128: err_rms              : 0.025739320428519667\n",
      "Entity-192x128: l2_norm              : 0.5699361173939471\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 100%\n",
      "Entity-192x128: err_rms              : 0.025820652970933992\n",
      "Entity-192x128: l2_norm              : 0.5755288345581242\n",
      "-------------------------------------------\n",
      "Entity-192x128: progress              : 100%\n",
      "Entity-192x128: err_rms_total              : 0.02626888348467747\n",
      "Entity-192x128: l2_norm              : 0.5764016136491302\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "41430b5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:42.169881Z",
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     "iopub.status.idle": "2026-06-01T18:05:42.691947Z",
     "shell.execute_reply": "2026-06-01T18:05:42.691290Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:42.009088062Z",
     "start_time": "2026-06-02T19:05:40.994766727Z"
    }
   },
   "source": [
    "combiner = SubImageCombine(PATCH, PATCH, STRIDE, STRIDE, 32, 32, n_ch=3)\n",
    "n_images = n_side\n",
    "nx_steps = (32 - PATCH) // STRIDE + 1\n",
    "ny_steps = (32 - PATCH) // STRIDE + 1\n",
    "\n",
    "fig, axes = plt.subplots(n_images, 2, figsize=(6, n_images * 3))\n",
    "\n",
    "for img_idx in range(n_images):\n",
    "    orig = np.reshape(train_images[img_idx], (3, 32, 32))\n",
    "    orig_display = np.asnumpy((orig - np.min(orig)) / (np.max(orig) - np.min(orig)))\n",
    "\n",
    "    start = img_idx * nx_steps * ny_steps\n",
    "    patches = train_subimages[start : start + nx_steps * ny_steps]\n",
    "    recon_patches = np.array([model.backward(model.forward(p)) for p in patches])\n",
    "    recon_full = combiner(recon_patches)\n",
    "\n",
    "    axes[img_idx, 0].imshow(orig_display.transpose(1, 2, 0))\n",
    "    axes[img_idx, 0].axis('off')\n",
    "    if img_idx == 0:\n",
    "        axes[img_idx, 0].set_title('Original')\n",
    "\n",
    "    axes[img_idx, 1].imshow(recon_full)\n",
    "    axes[img_idx, 1].axis('off')\n",
    "    if img_idx == 0:\n",
    "        axes[img_idx, 1].set_title('Reconstructed')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x3000 with 20 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "9adce292-cae6-455c-9409-c57ae71a36d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:42.694095Z",
     "iopub.status.busy": "2026-06-01T18:05:42.693875Z",
     "iopub.status.idle": "2026-06-01T18:05:44.224681Z",
     "shell.execute_reply": "2026-06-01T18:05:44.223717Z"
    },
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:43.837895532Z",
     "start_time": "2026-06-02T19:05:42.011191987Z"
    }
   },
   "source": [
    "# Plot weights\n",
    "weights = model.unit1.state.w_hv\n",
    "n, n_hid = weights.shape\n",
    "weight_indices = np.array(random.sample(range(n_hid), n_side*n_side))\n",
    "w = np.reshape(weights, (3, PATCH, PATCH, n_hid))\n",
    "print(w.shape)\n",
    "fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
    "\n",
    "index = 0\n",
    "for x in range(n_side):\n",
    "    for y in range(n_side):\n",
    "        inp = np.asnumpy(w[:,:,:, weight_indices[index]])\n",
    "        inp -= np.min(inp)\n",
    "        inp = inp / np.max(inp)\n",
    "        img = inp.transpose((1,2,0))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 8, 8, 128)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:14.004890Z",
     "iopub.status.busy": "2026-06-01T18:05:14.004617Z",
     "iopub.status.idle": "2026-06-01T18:05:15.450743Z",
     "shell.execute_reply": "2026-06-01T18:05:15.449902Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:45.598607433Z",
     "start_time": "2026-06-02T19:05:43.839405914Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
    "index = 0\n",
    "for x in range(n_side):\n",
    "    for y in range(n_side):\n",
    "        inp = train_subimages[image_indices[index]]\n",
    "        img = (1+np.reshape(inp, (3, PATCH, PATCH)))/2\n",
    "        img = img.transpose((1,2,0))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ],
   "id": "deddee55057a193c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "c976fed5-162c-4a70-8219-fc6c68c1fb66",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:45.647664165Z",
     "start_time": "2026-06-02T19:05:45.600373824Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "661dfc9d-e58a-4994-a5fa-c377d7f743e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:45.695323998Z",
     "start_time": "2026-06-02T19:05:45.649071049Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:40.789519Z",
     "iopub.status.busy": "2026-06-01T18:05:40.789280Z",
     "iopub.status.idle": "2026-06-01T18:05:42.168103Z",
     "shell.execute_reply": "2026-06-01T18:05:42.167459Z"
    },
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:47.491217507Z",
     "start_time": "2026-06-02T19:05:45.696719393Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Plot reconstructions\n",
    "fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
    "index = 0\n",
    "for x in range(n_side):\n",
    "    for y in range(n_side):\n",
    "        inp = train_subimages[image_indices[index]]\n",
    "        recon = model.backward(model.forward(inp))\n",
    "        recon -= np.min(recon)\n",
    "        recon = recon / np.max(recon)\n",
    "        img = np.reshape(recon, (3, PATCH, PATCH))\n",
    "        img = img.transpose((1,2,0))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ],
   "id": "bb342d25fa0ab4a4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "c1a67046-17e4-4924-8067-da23481eb6d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-02T19:05:47.538115047Z",
     "start_time": "2026-06-02T19:05:47.492369371Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 12
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}