{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6cb560a-707c-4c1e-b50f-2b9c8037a3c7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "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 rbm.model import Model\n",
    "from rbm.entity import Entity, EntityParams, TrainingParams\n",
    "from rbm.matrix import Mat, np, rms_error_accu\n",
    "from rbm.torch import Optimizer\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4065302d-862f-4639-99d7-e396d055434c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor()\n",
    "])                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffe48fc6-0f57-48a0-b5e9-aa68cbc8edf1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n",
    "test_data = torchvision.datasets.MNIST(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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51b90664-e470-4c9a-884d-374a2ece61b2",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33501c0f-6d78-43e9-b2ce-adbe40b6d95a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[28, 28]\n"
     ]
    }
   ],
   "source": [
    "print(list(train_data[1][0].size()[1:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aedf601a-9084-4047-98e8-481f4d41a2d9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "N = 60000\n",
    "size = 784\n",
    "n_side = 10\n",
    "train_images = np.zeros(shape=(N,size))\n",
    "for i in range(N):\n",
    "    image = train_data[i][0]\n",
    "    flat_array = torch.flatten(image)\n",
    "    train_images[i, :] = np.asarray(flat_array.numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ee865c3-2af4-487f-a184-1291f99839b1",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n"
     ]
    }
   ],
   "source": [
    "print(len(train_images))\n",
    "image_indices = np.random.randint(0, N-1, n_side*n_side)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4310933-d850-4128-a2dd-27d88c004846",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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_images[image_indices[index]]\n",
    "        img = np.reshape(inp, (28, 28))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08143a53-12a1-4896-a4b2-166dedf0e021",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TestModel(Model):\n",
    "\tdef __init__(self, name: str, work_dir: str = '.'):\n",
    "\t\tsuper().__init__(name, work_dir)\n",
    "\t\tself.unit1 = Entity((28*28, 2000), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.1, momentum=0.9, num_epochs=10, mini_batch_size=1000, l2_lambda=0.000001, do_rao_blackwell=True), True)\n",
    "#\t\tself.unit2 = Entity((500, 64), EntityParams(do_gaussian_visible=False, do_gaussian_hidden=False), TrainingParams(learning_rate=0.04, momentum=0.9, num_epochs=1000, mini_batch_size=100, l2_lambda=0.01, do_rao_blackwell=True), True)\n",
    "\n",
    "\tdef forward(self, x: Mat):\n",
    "\t\tx = self.unit1.forward(x)\n",
    "#\t\tx = self.unit2.forward(x)\n",
    "\t\treturn x\n",
    "\n",
    "\tdef backward(self, x: Mat):\n",
    "#\t\tx = self.unit2.reconstruct(x)\n",
    "\t\tx = self.unit1.reconstruct(x)\n",
    "\t\treturn x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b3be9f6-38e1-442d-a7bd-1f3c164ec8d4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results/mnist_test-0-state.npz loaded successfully!\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 0%\n",
      "Entity-784x2000: err_rms              : 0.011345658688025525\n",
      "Entity-784x2000: l2_norm              : 0.8204909314316488\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 10%\n",
      "Entity-784x2000: err_rms              : 0.01116823356353982\n",
      "Entity-784x2000: l2_norm              : 0.8326780556851139\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 20%\n",
      "Entity-784x2000: err_rms              : 0.011005319056239424\n",
      "Entity-784x2000: l2_norm              : 0.8452525335038596\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 30%\n",
      "Entity-784x2000: err_rms              : 0.010847796952849377\n",
      "Entity-784x2000: l2_norm              : 0.8577021550750525\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 40%\n",
      "Entity-784x2000: err_rms              : 0.010695956999553493\n",
      "Entity-784x2000: l2_norm              : 0.8699861630331949\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 50%\n",
      "Entity-784x2000: err_rms              : 0.010549322218651411\n",
      "Entity-784x2000: l2_norm              : 0.8821295630296148\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 60%\n",
      "Entity-784x2000: err_rms              : 0.010407674711569347\n",
      "Entity-784x2000: l2_norm              : 0.8941347578912914\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 70%\n",
      "Entity-784x2000: err_rms              : 0.01027079836476334\n",
      "Entity-784x2000: l2_norm              : 0.9060038495748082\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 80%\n",
      "Entity-784x2000: err_rms              : 0.010138479205967674\n",
      "Entity-784x2000: l2_norm              : 0.917738744359132\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 90%\n",
      "Entity-784x2000: err_rms              : 0.010010506099681684\n",
      "Entity-784x2000: l2_norm              : 0.9293412422976973\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 100%\n",
      "Entity-784x2000: err_rms              : 0.009886671703490845\n",
      "Entity-784x2000: l2_norm              : 0.9408131110877473\n",
      "-------------------------------------------\n",
      "Entity-784x2000: progress              : 100%\n",
      "Entity-784x2000: err_rms_total              : 0.00989524439729295\n",
      "Entity-784x2000: l2_norm              : 0.9412544234072597\n",
      "results/mnist_test-0-state.npz saved successfully!\n"
     ]
    }
   ],
   "source": [
    "work_dir = \"results\"\n",
    "prj_name = \"mnist_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(train_images)\n",
    "\n",
    "# save state\n",
    "model.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d036b106-77a3-4993-bc2a-5fb164b8ec46",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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_images[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, (28, 28))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9adce292-cae6-455c-9409-c57ae71a36d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28, 2000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot weights\n",
    "weights = model.unit1.state.w_hv\n",
    "n, n_hid = weights.shape\n",
    "weight_indices = np.random.randint(0, N-1, n_hid)\n",
    "w = np.reshape(weights, (28, 28, 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",
    "        axes[x,y].imshow(inp)\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a44660c-6ce7-4363-b345-5745f9b6f2d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}