vabsalack/experiment.ipynb

## experiment.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43ccfda4",
   "metadata": {},
   "source": [
    "# Experiment 1\n",
    "### Hypothesis:\n",
    "My variety of thought on how training works:  \n",
    "Scenario **1**:  \n",
    "Train using one instance. Model adjust it's tunable params so the loss value for the instance is zero. In result, the tuned params are one of some combinations of numbers which outputs zero loss for the given instance.  \n",
    "Scenario **2**:  \n",
    "bunch of instances, the batch. the update to each of tunable params are the average of all instance's gradient in the batch. In result, we after some iterations, we would find the one of some combinations of number for params that fits the batch.  \n",
    "Scenario **3**:  \n",
    "bunch of batches, the epoch. each batch is large enough to represent entire dataset. every batch has similar loss In result, after some iterations, we would the find the one of some combinations of number for params that fits the batch.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8575c25c",
   "metadata": {},
   "source": [
    "In scnerio 3:  \n",
    "Every batch has multiple instances but the update to params happens only once for the step. For neural network pov, it tries to fits itself for, the average of gradients, the batch. In result, for each individual input of instances, it predict correctly. he learned about the every class/instance sequence. but not only them. is it also learned the average image sequence, so it outputs all classes as equal for an average of batch images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b346aa70",
   "metadata": {},
   "source": [
    "1. take the batch size used for training, here 256. \n",
    "2. shuffle the training or/and test instances. collect 256 batches of samples.\n",
    "3. average and display them.\n",
    "4. give them  as input to our model. \n",
    "4. study the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e43ea540",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netweaver import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f3f986a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Property                 | instances_train  gtruth_train     instances_test   gtruth_test      \n",
      "-----------------------------------------------------------------------------------------------\n",
      "instances count          | 60000            60000            10000            10000            \n",
      "shape of the set         | (60000, 28, 28)  (60000,)         (10000, 28, 28)  (10000,)         \n",
      "shape of an instance     | (28, 28)         (1,)             (28, 28)         (1,)             \n",
      "Data type of unit        | float32          uint8            float32          uint8            \n",
      "Total memory (gb)        | 0.17524          0.00006          0.02921          0.00001          \n",
      "\n",
      "\n",
      "train labels count       : 10\n",
      "train labels values      : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "test labels count        : 10\n",
      "test labels values       : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "path_dataset = \"/home/keerthivasan_user/Documents/test_netweaver/datasets/fashion_mnist_images\"\n",
    "instances_train, gtruth_train, instances_test, gtruth_test = load_dataset(path_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b56cd367",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0377fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_neg_pos_one(arr, maxvalue):\n",
    "    max_half = maxvalue / 2\n",
    "    return (arr - max_half) / max_half\n",
    "\n",
    "def count_labels(ar):\n",
    "    d = dict()\n",
    "    for e in ar:\n",
    "        if e in d:\n",
    "            d[e] += 1\n",
    "        else:\n",
    "            d[e] = 1\n",
    "    l = list(d.items())\n",
    "    l.sort(key=lambda e: e[0])\n",
    "    return l\n",
    "\n",
    "MAX_VALUE = 255\n",
    "BATCH_SIZE = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bc519af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before shuffle: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]\n",
      "After shuffle: [26732 12986 38199 59201 22220 22365 47919  8377 10672  7855 59267 24558\n",
      "   528 52433  6589 18474 38157 53408 43923 27110]\n"
     ]
    }
   ],
   "source": [
    "# shuffle\n",
    "indices_train = np.array(range(instances_train.shape[0]))\n",
    "indices_test = np.array(range(instances_test.shape[0]))\n",
    "\n",
    "print(f\"Before shuffle: {indices_train[:20]}\")\n",
    "\n",
    "rng = np.random.default_rng()\n",
    "\n",
    "rng.shuffle(indices_train)\n",
    "rng.shuffle(indices_test)\n",
    "\n",
    "print(f\"After shuffle: {indices_train[:20]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2efd9d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reassign after shuffle\n",
    "instances_train_shuffle = instances_train[indices_train]\n",
    "instances_test_shuffle = instances_test[indices_test]\n",
    "\n",
    "gtruth_train_shuffle = gtruth_train[indices_train]\n",
    "gtruth_test_shuffle = gtruth_test[indices_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12b4980b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 256, 28, 28) (5, 256)\n",
      "(5, 256, 28, 28) (5, 256)\n"
     ]
    }
   ],
   "source": [
    "# create seperate arrays for train and test 's instances and grtuth, has 5 batch of images, 256 each. 256 because it's the batch size used for training.\n",
    "images_train_in_batch = np.array([instances_train_shuffle[i*BATCH_SIZE : (i+1)*BATCH_SIZE] for i in range(5)])\n",
    "gtruth_train_in_batch = np.array([gtruth_train_shuffle[i*BATCH_SIZE : (i+1)*BATCH_SIZE] for i in range(5)])\n",
    "\n",
    "print(images_train_in_batch.shape, gtruth_train_in_batch.shape)\n",
    "\n",
    "images_test_in_batch = np.array([instances_test_shuffle[i*BATCH_SIZE : (i+1)*BATCH_SIZE] for i in range(5)])\n",
    "gtruth_test_in_batch = np.array([gtruth_test_shuffle[i*BATCH_SIZE : (i+1)*BATCH_SIZE] for i in range(5)])\n",
    "\n",
    "print(images_test_in_batch.shape, gtruth_test_in_batch.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "165dadb8",
   "metadata": {},
   "outputs": [
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       "version_major": 2,
       "version_minor": 0
      },
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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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width=1300.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this cell's reason is to give intuition of how average image looks for different sum of images.\n",
    "# first row is original 5 images from dataset,\n",
    "# second row, each column is average of first 5, 100, 150, 200 and 255 images. \n",
    "fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(13, 5))\n",
    "\n",
    "for i in range(5):\n",
    "    axes[0][i].imshow(images_train_in_batch[0][i])\n",
    "\n",
    "\n",
    "axes[1][0].imshow(images_train_in_batch[0][:5].mean(axis=0))\n",
    "axes[1][1].imshow(images_train_in_batch[0][:100].mean(axis=0))\n",
    "axes[1][2].imshow(images_train_in_batch[0][:150].mean(axis=0))\n",
    "axes[1][3].imshow(images_train_in_batch[0][:200].mean(axis=0))\n",
    "axes[1][4].imshow(images_train_in_batch[0][:255].mean(axis=0))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51345341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e6d4ccfaea34947b04818b29fc2aaab",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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width=1300.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the plot of first 5 batches's average, each batch has 256 images, the result is 5 average images.\n",
    "# first row is train set\n",
    "# second row is test set\n",
    "fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(13, 4))\n",
    "\n",
    "import itertools\n",
    "\n",
    "for index, (row, col) in enumerate(itertools.product(range(4), range(5))):\n",
    "    if row == 0:\n",
    "        axes[row][col].imshow(images_train_in_batch[col].mean(axis=0))\n",
    "    elif row == 1:\n",
    "        axes[row][col].imshow(images_test_in_batch[col].mean(axis=0))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d9f1b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 256, 28, 28)\n",
      "(5, 256, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# scale the images between -1 and +1\n",
    "images_train_in_batch_scaled = scale_neg_pos_one(images_train_in_batch, MAX_VALUE)\n",
    "images_test_in_batch_scaled = scale_neg_pos_one(images_test_in_batch, MAX_VALUE)\n",
    "\n",
    "print(images_train_in_batch_scaled.shape)\n",
    "print(images_test_in_batch_scaled.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28a53f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate an average list of images from scaled version. resuting. 5 images in train and test list.\n",
    "images_train_in_batch_scaled_average = np.array([batch.mean(axis=0) for batch in images_train_in_batch_scaled])\n",
    "images_test_in_batch_scaled_average = np.array([batch.mean(axis=0) for batch in images_test_in_batch_scaled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3febc44f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 784)\n",
      "(5, 784)\n"
     ]
    }
   ],
   "source": [
    "images_train_in_batch_scaled_average_flatten = images_train_in_batch_scaled_average.reshape(images_train_in_batch_scaled_average.shape[0], -1)\n",
    "images_test_in_batch_scaled_average_flatten = images_test_in_batch_scaled_average.reshape(images_test_in_batch_scaled_average.shape[0], -1)\n",
    "\n",
    "print(images_train_in_batch_scaled_average_flatten.shape)\n",
    "print(images_test_in_batch_scaled_average_flatten.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c3688a3",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8105eb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from netweaver import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2598a475",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_modelfile = (  # repalce with your model path\n",
    "    \"/home/keerthivasan_user/Documents/test_netweaver/logs/model_training/model-20250615-132411/model-20250615-132411.model\"\n",
    ")\n",
    "model_loaded: Model = Model.load_model(path=path_modelfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c816e569",
   "metadata": {},
   "outputs": [],
   "source": [
    "confidences_train = model_loaded.predict(instances_prediction=images_train_in_batch_scaled_average_flatten)\n",
    "confidences_test = model_loaded.predict(instances_prediction=images_test_in_batch_scaled_average_flatten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdcc405f",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(linewidth=500)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9eba38",
   "metadata": {},
   "source": [
    "### train set inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "222f9900",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.48852453  1.32046272 10.30139947 12.84676623 13.07689088  2.79542531 20.44492411  4.86804087 22.42200323  7.43556266]\n",
      " [ 4.29994221  1.33388379 10.31666314 13.01103694 12.89038552  2.90974779 20.57636709  5.64930761 20.4769001   8.53576581]\n",
      " [ 3.62268376  1.1624381   9.99247893 14.1496177  14.5228639   2.34689258 22.10678986  4.70170114 20.35966876  7.03486527]\n",
      " [ 4.44474469  1.31764389 10.73652461 12.35044601 13.57531864  3.00085674 22.06486051  4.97754544 19.11318523  8.41887425]\n",
      " [ 4.47102365  1.3320388  11.74994575 13.47598873 14.69774283  2.12557585 20.86487372  4.1692714  20.94352984  6.17000944]]\n"
     ]
    }
   ],
   "source": [
    "print(confidences_train*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659e6ee3",
   "metadata": {},
   "source": [
    "# Interpretation\n",
    "classes (8 [Bag], 6 [Shirt], 4[Coat], 3[Dress] and 2[Pullover]) got top five maximum scores.  \n",
    "- classees Bag and shirt scored almost equal probabitliy, ~20%. all inputs images pretty much looks similar. and they more resembles somewhere between shirt and bag I hope or they similar has similar sequence (flatten pixels) to bag and shirt.  \n",
    "- by below cell output. though we shuffled the train or test set before sample out and average them. It turns out that the bag and shirt images has more heavier pixels (think of it as brighter or more in contrast)values than other candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cccb6c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(np.uint8(0), 29), (np.uint8(1), 19), (np.uint8(2), 28), (np.uint8(3), 31), (np.uint8(4), 25), (np.uint8(5), 19), (np.uint8(6), 29), (np.uint8(7), 22), (np.uint8(8), 24), (np.uint8(9), 30)]\n",
      "[(np.uint8(0), 28), (np.uint8(1), 29), (np.uint8(2), 24), (np.uint8(3), 29), (np.uint8(4), 19), (np.uint8(5), 33), (np.uint8(6), 26), (np.uint8(7), 23), (np.uint8(8), 17), (np.uint8(9), 28)]\n",
      "[(np.uint8(0), 17), (np.uint8(1), 18), (np.uint8(2), 21), (np.uint8(3), 32), (np.uint8(4), 30), (np.uint8(5), 25), (np.uint8(6), 28), (np.uint8(7), 34), (np.uint8(8), 28), (np.uint8(9), 23)]\n",
      "[(np.uint8(0), 29), (np.uint8(1), 26), (np.uint8(2), 26), (np.uint8(3), 27), (np.uint8(4), 30), (np.uint8(5), 23), (np.uint8(6), 26), (np.uint8(7), 16), (np.uint8(8), 17), (np.uint8(9), 36)]\n",
      "[(np.uint8(0), 31), (np.uint8(1), 21), (np.uint8(2), 19), (np.uint8(3), 34), (np.uint8(4), 28), (np.uint8(5), 35), (np.uint8(6), 28), (np.uint8(7), 18), (np.uint8(8), 23), (np.uint8(9), 19)]\n"
     ]
    }
   ],
   "source": [
    "# here is the classification count of labes used to average an image from batch of imamges.\n",
    "# well, it looks like they are evenly spreaded out.\n",
    "labels_count_train = [count_labels(gtruth_train_in_batch[i]) for i in range(5)]\n",
    "print(*labels_count_train, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "580aee2e",
   "metadata": {},
   "source": [
    "### test set inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5d31de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.39577884  1.44560068  9.54022548 12.38329941 12.43097557  3.66756341 18.81947139  6.57434503 20.87012296  9.87261722]\n",
      " [ 4.26484943  1.24689053 11.76830228 13.27916323 14.53916428  2.33394291 21.43784267  4.29999784 19.91618128  6.91366556]\n",
      " [ 4.67643553  1.45006976 12.85835781 14.2226685  14.19917173  2.27449336 19.74980496  4.769168   18.89793351  6.90189684]\n",
      " [ 3.49316887  1.2524821   8.4635766  12.78050424 12.47334976  3.99821857 18.84394807  7.35746434 20.62729352 10.70999393]\n",
      " [ 4.40643844  1.34327854 10.89564413 12.14786184 13.64002447  2.83581093 19.72011903  5.4258569  21.91403529  7.67093044]]\n"
     ]
    }
   ],
   "source": [
    "print(confidences_test*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49f8d0ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(np.uint8(0), 28), (np.uint8(1), 26), (np.uint8(2), 22), (np.uint8(3), 31), (np.uint8(4), 30), (np.uint8(5), 22), (np.uint8(6), 20), (np.uint8(7), 26), (np.uint8(8), 16), (np.uint8(9), 35)]\n",
      "[(np.uint8(0), 20), (np.uint8(1), 33), (np.uint8(2), 23), (np.uint8(3), 24), (np.uint8(4), 27), (np.uint8(5), 27), (np.uint8(6), 25), (np.uint8(7), 24), (np.uint8(8), 32), (np.uint8(9), 21)]\n",
      "[(np.uint8(0), 29), (np.uint8(1), 29), (np.uint8(2), 27), (np.uint8(3), 38), (np.uint8(4), 16), (np.uint8(5), 22), (np.uint8(6), 20), (np.uint8(7), 24), (np.uint8(8), 21), (np.uint8(9), 30)]\n",
      "[(np.uint8(0), 15), (np.uint8(1), 31), (np.uint8(2), 23), (np.uint8(3), 26), (np.uint8(4), 22), (np.uint8(5), 28), (np.uint8(6), 28), (np.uint8(7), 26), (np.uint8(8), 25), (np.uint8(9), 32)]\n",
      "[(np.uint8(0), 27), (np.uint8(1), 32), (np.uint8(2), 19), (np.uint8(3), 25), (np.uint8(4), 24), (np.uint8(5), 24), (np.uint8(6), 26), (np.uint8(7), 28), (np.uint8(8), 30), (np.uint8(9), 21)]\n"
     ]
    }
   ],
   "source": [
    "labels_count_test = [count_labels(gtruth_test_in_batch[i]) for i in range(5)]\n",
    "print(*labels_count_test, sep=\"\\n\")"
   ]
  }
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