LSTM-VAE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation of LSTM(Long short-term memory) based VAE(variational Autoencoder)\n",
    "1. All the necessary libraries are imported\n",
    "2. Data are loaded and scaled\n",
    "3. Data is preprocessed (first the data is truncated according to time_step( here 50), and divide into possible samples), and then split into train and test data\n",
    "4. Model architechture is defined and trained on train data\n",
    "5. Model is saved\n",
    "6. For Inference, tests are made on the model, and the comparison between orginal test video and generated video are stored (in output/inference folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch.optim as optim\n",
    "from torch.nn.modules.conv import ConvTranspose1d\n",
    "from torch.nn import Module, Conv1d, Sequential, Dropout, MaxPool1d\n",
    "import matplotlib.pyplot as plt\n",
    "from visualize import visualize,animate\n",
    "from torchvision import models\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 6803, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = np.load(\"data\\mariel_beyond.npy\")\n",
    "motion_data=np.array(res)\n",
    "arr_reshaped = motion_data.reshape(-1, 1)\n",
    "# Scale the data using MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(arr_reshaped)\n",
    "scaled_data = scaled_data.reshape(motion_data.shape)\n",
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"res = np.load(\"data\\mariel_beyond.npy\")\n",
    "scaled_data=np.array(res)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6803, 165)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data=scaled_data.transpose(1, 0, 2).reshape(6803, -1)\n",
    "scaled_data.shape #55 * 3 = 165"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_steps = 50\n",
    "num_features = scaled_data.shape[1]\n",
    "\n",
    "# Reshape data into smaller sequences\n",
    "num_sequences = scaled_data.shape[0] // time_steps\n",
    "scaled_data = scaled_data[:num_sequences*time_steps]  # truncate data to a size divisible by time_steps (whole sequence time step, [x,y,z] coordinates of joints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6800, 165)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = scaled_data.reshape((num_sequences, time_steps, num_features))  #(no. of sample, time_step, [x,y,z] coordinates of joints)\n",
    "train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(108, 50, 165)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparation of training data\n",
    "\n",
    "train_data_tensor = torch.tensor(train_data, dtype=torch.float32)\n",
    "input_size=train_data_tensor.shape[-1]\n",
    "num_samples=train_data.shape[0] \n",
    "batch_size = 32\n",
    "train_loader = DataLoader(TensorDataset(train_data_tensor), batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTMVAE(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, latent_size, num_layers=1):\n",
    "        super(LSTMVAE, self).__init__()\n",
    "        \n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.latent_size = latent_size\n",
    "        self.num_layers = num_layers\n",
    "        \n",
    "        # Encoder LSTM\n",
    "        self.encoder_lstm = nn.LSTM(input_size, hidden_size, num_layers=num_layers, batch_first=True)\n",
    "        self.encoder_mean = nn.Linear(hidden_size, latent_size)\n",
    "        self.encoder_logvar = nn.Linear(hidden_size, latent_size)\n",
    "        \n",
    "        # Decoder LSTM\n",
    "        self.decoder_lstm = nn.LSTM(latent_size, hidden_size, num_layers=num_layers, batch_first=True)\n",
    "        self.decoder_output = nn.Linear(hidden_size, input_size)\n",
    "        \n",
    "        # Post-processing parameters\n",
    "        self.filter = nn.Conv1d(input_size, input_size, kernel_size=3, padding=1)  # filter for smoothing\n",
    "        \n",
    "    def encode(self, x):\n",
    "        _, (h_n, _) = self.encoder_lstm(x)\n",
    "        mean = self.encoder_mean(h_n[-1])  # Using the output of the last LSTM layer\n",
    "        logvar = self.encoder_logvar(h_n[-1])  # Using the output of the last LSTM layer\n",
    "        return mean, logvar\n",
    "    \n",
    "    def reparameterize(self, mean, logvar):\n",
    "        std = torch.exp(0.5 * logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        return mean + eps * std\n",
    "    \n",
    "    def decode(self, z, seq_len):\n",
    "        z = z.unsqueeze(1).repeat(1, seq_len, 1)\n",
    "        output, _ = self.decoder_lstm(z)\n",
    "        decoded = self.decoder_output(output)\n",
    "        return decoded\n",
    "    \n",
    "    def post_process(self, decoded):\n",
    "        decoded = decoded.permute(0, 2, 1)  # Change shape for Conv1d input\n",
    "        decoded_smooth = self.filter(decoded)\n",
    "        decoded_smooth = decoded_smooth.permute(0, 2, 1)  # Restore original shape\n",
    "        return decoded_smooth\n",
    "    \n",
    "    def forward(self, x):\n",
    "        mean, logvar = self.encode(x)\n",
    "        z = self.reparameterize(mean, logvar)\n",
    "        decoded = self.decode(z, x.size(1))\n",
    "        decoded_smooth = self.post_process(decoded)\n",
    "        return decoded_smooth, mean, logvar\n",
    "\n",
    "def loss_function(decoded, x, mean, logvar):\n",
    "    recon_loss = nn.functional.mse_loss(decoded, x, reduction='sum')\n",
    "    kl_divergence = -0.5 * torch.sum(1 + logvar - mean.pow(2) - logvar.exp())\n",
    "    return recon_loss + kl_divergence, kl_divergence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_size = 128\n",
    "latent_size = 64\n",
    "num_layers = 2\n",
    "vae = LSTMVAE(input_size, hidden_size, latent_size, num_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 2741.941948784722, kl: 0.06901600956916809\n",
      "Epoch 2, Loss: 1314.5641276041667, kl: 0.7214106917381287\n",
      "Epoch 3, Loss: 352.32573671694155, kl: 0.7872466444969177\n",
      "Epoch 4, Loss: 174.76664790400753, kl: 0.5213584303855896\n",
      "Epoch 5, Loss: 141.41765679253473, kl: 0.48641473054885864\n",
      "Epoch 6, Loss: 151.0654489022714, kl: 0.4550502896308899\n",
      "Epoch 7, Loss: 112.54782669632523, kl: 0.39759764075279236\n",
      "Epoch 8, Loss: 106.73871301721644, kl: 0.3564729392528534\n",
      "Epoch 9, Loss: 95.65581371166088, kl: 0.33666738867759705\n",
      "Epoch 10, Loss: 97.06419089988425, kl: 0.31502941250801086\n",
      "Epoch 11, Loss: 93.64933946397569, kl: 0.2925121486186981\n",
      "Epoch 12, Loss: 91.08946736653645, kl: 0.270122230052948\n",
      "Epoch 13, Loss: 90.07183837890625, kl: 0.25265058875083923\n",
      "Epoch 14, Loss: 87.98690852412471, kl: 0.23894447088241577\n",
      "Epoch 15, Loss: 86.49114481608073, kl: 0.22343333065509796\n",
      "Epoch 16, Loss: 86.76693386501736, kl: 0.2083364874124527\n",
      "Epoch 17, Loss: 86.76928484881365, kl: 0.19654817879199982\n",
      "Epoch 18, Loss: 83.95022696035879, kl: 0.1831531524658203\n",
      "Epoch 19, Loss: 87.20569412796586, kl: 0.1720394790172577\n",
      "Epoch 20, Loss: 84.6935481318721, kl: 0.1597803235054016\n",
      "Epoch 21, Loss: 84.06930372450087, kl: 0.1483272910118103\n",
      "Epoch 22, Loss: 84.06740993923611, kl: 0.13763129711151123\n",
      "Epoch 23, Loss: 85.27327473958333, kl: 0.1295650154352188\n",
      "Epoch 24, Loss: 83.30121640805845, kl: 0.12458748370409012\n",
      "Epoch 25, Loss: 81.26410590277777, kl: 0.11471381038427353\n",
      "Epoch 26, Loss: 84.03316243489583, kl: 0.10101046413183212\n",
      "Epoch 27, Loss: 83.78342466001158, kl: 0.09524485468864441\n",
      "Epoch 28, Loss: 82.78915122703269, kl: 0.09115317463874817\n",
      "Epoch 29, Loss: 81.30518821433738, kl: 0.08714297413825989\n",
      "Epoch 30, Loss: 81.49422370062933, kl: 0.07913804799318314\n",
      "Epoch 31, Loss: 81.73856155960648, kl: 0.07940948754549026\n",
      "Epoch 32, Loss: 81.26899945294416, kl: 0.07582322508096695\n",
      "Epoch 33, Loss: 81.68192319516783, kl: 0.07893499732017517\n",
      "Epoch 34, Loss: 80.59342447916667, kl: 0.08931606262922287\n",
      "Epoch 35, Loss: 76.38877360026042, kl: 0.0889507383108139\n",
      "Epoch 36, Loss: 76.99745008680556, kl: 0.09679105877876282\n",
      "Epoch 37, Loss: 72.95261241771557, kl: 0.15991602838039398\n",
      "Epoch 38, Loss: 75.53086739999277, kl: 0.18057693541049957\n",
      "Epoch 39, Loss: 75.58783128526476, kl: 0.15861919522285461\n",
      "Epoch 40, Loss: 76.52132896140769, kl: 0.14568395912647247\n",
      "Epoch 41, Loss: 69.52998747649016, kl: 0.21189698576927185\n",
      "Epoch 42, Loss: 66.80017485441985, kl: 0.17075598239898682\n",
      "Epoch 43, Loss: 64.68018086751302, kl: 0.2641577124595642\n",
      "Epoch 44, Loss: 71.32520605016637, kl: 0.263703852891922\n",
      "Epoch 45, Loss: 69.19657954463253, kl: 0.26442408561706543\n",
      "Epoch 46, Loss: 66.7868511058666, kl: 0.2487245798110962\n",
      "Epoch 47, Loss: 66.38325076633029, kl: 0.21927331387996674\n",
      "Epoch 48, Loss: 61.59357311107494, kl: 0.46081480383872986\n",
      "Epoch 49, Loss: 62.78310592086227, kl: 0.1790769398212433\n",
      "Epoch 50, Loss: 63.86319478352865, kl: 0.10677561163902283\n",
      "Epoch 51, Loss: 60.30202342845775, kl: 0.20200586318969727\n",
      "Epoch 52, Loss: 61.1768148916739, kl: 0.19239455461502075\n",
      "Epoch 53, Loss: 55.16703118218316, kl: 0.374899297952652\n",
      "Epoch 54, Loss: 54.3972088849103, kl: 0.22656992077827454\n",
      "Epoch 55, Loss: 54.47773629647714, kl: 0.2714664041996002\n",
      "Epoch 56, Loss: 63.39184626826533, kl: 0.10192707180976868\n",
      "Epoch 57, Loss: 49.815568146882235, kl: 0.11666283756494522\n",
      "Epoch 58, Loss: 50.84884926124855, kl: 0.26565733551979065\n",
      "Epoch 59, Loss: 44.644019232855904, kl: 0.10997577756643295\n",
      "Epoch 60, Loss: 52.41058462637442, kl: 0.22409746050834656\n",
      "Epoch 61, Loss: 44.70648871527778, kl: 0.16902782022953033\n",
      "Epoch 62, Loss: 46.349830344871236, kl: 0.23899319767951965\n",
      "Epoch 63, Loss: 42.579868457935476, kl: 0.39648300409317017\n",
      "Epoch 64, Loss: 41.95993268048322, kl: 0.17896215617656708\n",
      "Epoch 65, Loss: 43.618744179054545, kl: 0.40268874168395996\n",
      "Epoch 66, Loss: 41.635306746871386, kl: 0.24386875331401825\n",
      "Epoch 67, Loss: 44.149397390860095, kl: 0.31805679202079773\n",
      "Epoch 68, Loss: 42.71161227756076, kl: 0.2716033458709717\n",
      "Epoch 69, Loss: 39.42686095061126, kl: 0.271980345249176\n",
      "Epoch 70, Loss: 37.41777942798756, kl: 0.5176147818565369\n",
      "Epoch 71, Loss: 38.19439753779658, kl: 0.2117558717727661\n",
      "Epoch 72, Loss: 37.521285163031685, kl: 0.21873213350772858\n",
      "Epoch 73, Loss: 36.651153847023295, kl: 0.28558096289634705\n",
      "Epoch 74, Loss: 40.044529385036896, kl: 0.8725350499153137\n",
      "Epoch 75, Loss: 37.693566781503186, kl: 0.5060420036315918\n",
      "Epoch 76, Loss: 36.18260758011429, kl: 0.7189074158668518\n",
      "Epoch 77, Loss: 39.024491769296155, kl: 0.2974109351634979\n",
      "Epoch 78, Loss: 36.04860969826027, kl: 0.1884787380695343\n",
      "Epoch 79, Loss: 36.36402045355903, kl: 0.2740241587162018\n",
      "Epoch 80, Loss: 35.17957842791522, kl: 0.35072919726371765\n",
      "Epoch 81, Loss: 33.844520851417826, kl: 0.28704148530960083\n",
      "Epoch 82, Loss: 33.6221273916739, kl: 0.2179148942232132\n",
      "Epoch 83, Loss: 37.329993919089986, kl: 0.46790385246276855\n",
      "Epoch 84, Loss: 35.91404074209708, kl: 0.19118395447731018\n",
      "Epoch 85, Loss: 35.37633175320096, kl: 0.3161158263683319\n",
      "Epoch 86, Loss: 35.96289005985967, kl: 0.4154607653617859\n",
      "Epoch 87, Loss: 35.532705801504626, kl: 0.35927432775497437\n",
      "Epoch 88, Loss: 37.50060696072049, kl: 0.31929415464401245\n",
      "Epoch 89, Loss: 33.68705579969618, kl: 0.8883228898048401\n",
      "Epoch 90, Loss: 34.598925555193865, kl: 0.5977156162261963\n",
      "Epoch 91, Loss: 32.74017786096643, kl: 0.44560855627059937\n",
      "Epoch 92, Loss: 34.34084856951678, kl: 0.2782086133956909\n",
      "Epoch 93, Loss: 32.03793447989005, kl: 0.8451197743415833\n",
      "Epoch 94, Loss: 33.46113077799479, kl: 0.8541487455368042\n",
      "Epoch 95, Loss: 33.67693837483724, kl: 0.32978537678718567\n",
      "Epoch 96, Loss: 32.7378816957827, kl: 0.8561713695526123\n",
      "Epoch 97, Loss: 32.60531559696904, kl: 0.3927532434463501\n",
      "Epoch 98, Loss: 33.8013257627134, kl: 0.5163881778717041\n",
      "Epoch 99, Loss: 33.25695433440032, kl: 0.22351381182670593\n",
      "Epoch 100, Loss: 33.68751695421007, kl: 0.8003516793251038\n",
      "Epoch 101, Loss: 33.482236508969905, kl: 0.22017814218997955\n",
      "Epoch 102, Loss: 34.30423934371383, kl: 0.35766342282295227\n",
      "Epoch 103, Loss: 36.514108163339124, kl: 0.8937361240386963\n",
      "Epoch 104, Loss: 36.47961821379485, kl: 0.44699451327323914\n",
      "Epoch 105, Loss: 38.94140229401765, kl: 0.33648526668548584\n",
      "Epoch 106, Loss: 41.276253594292534, kl: 0.30690324306488037\n",
      "Epoch 107, Loss: 44.26505449083116, kl: 0.17141638696193695\n",
      "Epoch 108, Loss: 39.095165959110965, kl: 0.3399527370929718\n",
      "Epoch 109, Loss: 38.048193472403064, kl: 0.5070378184318542\n",
      "Epoch 110, Loss: 39.43801823368779, kl: 0.3340730369091034\n",
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      "Epoch 1860, Loss: 10.877402270281756, kl: 0.4066369831562042\n",
      "Epoch 1861, Loss: 11.083092018410012, kl: 0.42307230830192566\n",
      "Epoch 1862, Loss: 11.144542552806714, kl: 0.43963101506233215\n",
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      "Epoch 1864, Loss: 10.993018962718823, kl: 0.46077120304107666\n",
      "Epoch 1865, Loss: 11.123542644359448, kl: 0.39917704463005066\n",
      "Epoch 1866, Loss: 11.023494861744068, kl: 0.4569801986217499\n",
      "Epoch 1867, Loss: 11.067325309470847, kl: 0.40555739402770996\n",
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      "Epoch 1869, Loss: 11.308991043655961, kl: 0.41232988238334656\n",
      "Epoch 1870, Loss: 10.473063716182002, kl: 0.40949949622154236\n",
      "Epoch 1871, Loss: 11.268247251157407, kl: 0.48446857929229736\n",
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      "Epoch 1876, Loss: 11.26303552698206, kl: 0.4296700954437256\n",
      "Epoch 1877, Loss: 11.962618933783638, kl: 0.41229844093322754\n",
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      "Epoch 1880, Loss: 12.690920088026258, kl: 0.43761375546455383\n",
      "Epoch 1881, Loss: 13.70598644680447, kl: 0.46825310587882996\n",
      "Epoch 1882, Loss: 12.774269386574074, kl: 0.4481828808784485\n",
      "Epoch 1883, Loss: 11.610373179117838, kl: 0.3949016332626343\n",
      "Epoch 1884, Loss: 11.738528216326678, kl: 0.39063212275505066\n",
      "Epoch 1885, Loss: 11.430271996392143, kl: 0.46775659918785095\n",
      "Epoch 1886, Loss: 11.414991590711805, kl: 0.3966044783592224\n",
      "Epoch 1887, Loss: 11.403455946180555, kl: 0.3974904716014862\n",
      "Epoch 1888, Loss: 11.499050846806279, kl: 0.376380980014801\n",
      "Epoch 1889, Loss: 11.222073449028862, kl: 0.40811145305633545\n",
      "Epoch 1890, Loss: 11.365761650933159, kl: 0.4263407289981842\n",
      "Epoch 1891, Loss: 11.289105733235678, kl: 0.44965606927871704\n",
      "Epoch 1892, Loss: 10.476187670672381, kl: 0.4408324658870697\n",
      "Epoch 1893, Loss: 11.548846774631077, kl: 0.4756767749786377\n",
      "Epoch 1894, Loss: 11.107671949598524, kl: 0.38932010531425476\n",
      "Epoch 1895, Loss: 11.269264503761574, kl: 0.3916792571544647\n",
      "Epoch 1896, Loss: 11.733990633929217, kl: 0.47045275568962097\n",
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      "Epoch 1899, Loss: 11.537170975296586, kl: 0.45935338735580444\n",
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      "Epoch 1901, Loss: 11.755025651719835, kl: 0.4683416187763214\n",
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      "Epoch 1903, Loss: 11.46337819982458, kl: 0.46068018674850464\n",
      "Epoch 1904, Loss: 10.709249708387587, kl: 0.44573232531547546\n",
      "Epoch 1905, Loss: 10.588057200113932, kl: 0.3986617922782898\n",
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      "Epoch 1908, Loss: 10.704666702835649, kl: 0.4525263011455536\n",
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      "Epoch 1911, Loss: 10.552483170120805, kl: 0.4200098514556885\n",
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      "Epoch 1926, Loss: 19.998003359194154, kl: 0.4664786159992218\n",
      "Epoch 1927, Loss: 17.40210907547562, kl: 0.4231187403202057\n",
      "Epoch 1928, Loss: 16.701479028772425, kl: 0.38841480016708374\n",
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      "Epoch 1931, Loss: 13.931164211697048, kl: 0.40488243103027344\n",
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      "Epoch 1933, Loss: 13.228715119538483, kl: 0.3841865658760071\n",
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      "Epoch 1965, Loss: 12.349033779568142, kl: 0.32245728373527527\n",
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      "Epoch 1969, Loss: 14.01189535635489, kl: 0.4307193458080292\n",
      "Epoch 1970, Loss: 13.907814873589409, kl: 0.3434702455997467\n",
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      "Epoch 2017, Loss: 12.698502717194733, kl: 0.3894917666912079\n",
      "Epoch 2018, Loss: 12.97873306274414, kl: 0.3745749890804291\n",
      "Epoch 2019, Loss: 12.425753275553385, kl: 0.34508079290390015\n",
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      "Epoch 6576, Loss: 16.918053521050346, kl: 0.5058669447898865\n",
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      "Epoch 6751, Loss: 7.363346947564019, kl: 0.4871518611907959\n",
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      "Epoch 7700, Loss: 6.877930959065755, kl: 0.5481841564178467\n",
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      "Epoch 9831, Loss: 6.926447409170645, kl: 0.513818085193634\n",
      "Epoch 9832, Loss: 6.99976165206344, kl: 0.502850353717804\n",
      "Epoch 9833, Loss: 7.364334954155816, kl: 0.5678688287734985\n",
      "Epoch 9834, Loss: 7.14320726747866, kl: 0.54555344581604\n",
      "Epoch 9835, Loss: 7.041752073499891, kl: 0.5096345543861389\n",
      "Epoch 9836, Loss: 6.888543588143808, kl: 0.5126367211341858\n",
      "Epoch 9837, Loss: 6.977149680808738, kl: 0.5017282962799072\n",
      "Epoch 9838, Loss: 6.479663425021702, kl: 0.5108807682991028\n",
      "Epoch 9839, Loss: 6.690235137939453, kl: 0.4923667311668396\n",
      "Epoch 9840, Loss: 6.72804429796007, kl: 0.5676767826080322\n",
      "Epoch 9841, Loss: 7.024668587578668, kl: 0.5022526383399963\n",
      "Epoch 9842, Loss: 6.7446375952826605, kl: 0.50674968957901\n",
      "Epoch 9843, Loss: 7.207027435302734, kl: 0.5404269099235535\n",
      "Epoch 9844, Loss: 6.788839834707755, kl: 0.5089520215988159\n",
      "Epoch 9845, Loss: 7.033286977697302, kl: 0.5063181519508362\n",
      "Epoch 9846, Loss: 6.984956529405382, kl: 0.5063610672950745\n",
      "Epoch 9847, Loss: 7.285835831253617, kl: 0.49147331714630127\n",
      "Epoch 9848, Loss: 6.846556769476996, kl: 0.5022580623626709\n",
      "Epoch 9849, Loss: 6.593754803692853, kl: 0.5497822761535645\n",
      "Epoch 9850, Loss: 7.087759017944336, kl: 0.541051983833313\n",
      "Epoch 9851, Loss: 7.189275035151729, kl: 0.45469367504119873\n",
      "Epoch 9852, Loss: 6.950852429425275, kl: 0.5457163453102112\n",
      "Epoch 9853, Loss: 6.5961028911449295, kl: 0.49167874455451965\n",
      "Epoch 9854, Loss: 7.057828267415364, kl: 0.5203449130058289\n",
      "Epoch 9855, Loss: 6.804925000226056, kl: 0.5485023856163025\n",
      "Epoch 9856, Loss: 6.695054018938983, kl: 0.547251284122467\n",
      "Epoch 9857, Loss: 6.905409212465639, kl: 0.5207923054695129\n",
      "Epoch 9858, Loss: 7.08553914670591, kl: 0.5070636868476868\n",
      "Epoch 9859, Loss: 6.835913622820819, kl: 0.55283123254776\n",
      "Epoch 9860, Loss: 6.845930947197808, kl: 0.5099079012870789\n",
      "Epoch 9861, Loss: 6.747673317238137, kl: 0.496121346950531\n",
      "Epoch 9862, Loss: 6.824607637193468, kl: 0.5385751128196716\n",
      "Epoch 9863, Loss: 6.8431664926034435, kl: 0.5307596325874329\n",
      "Epoch 9864, Loss: 6.602440092298719, kl: 0.5093072652816772\n",
      "Epoch 9865, Loss: 7.059816925613968, kl: 0.5005601048469543\n",
      "Epoch 9866, Loss: 6.816699275264034, kl: 0.5114709734916687\n",
      "Epoch 9867, Loss: 6.746901618109809, kl: 0.5662205219268799\n",
      "Epoch 9868, Loss: 6.606894104569046, kl: 0.5160612463951111\n",
      "Epoch 9869, Loss: 6.887852280228226, kl: 0.5368392467498779\n",
      "Epoch 9870, Loss: 6.655131587275752, kl: 0.5394310355186462\n",
      "Epoch 9871, Loss: 7.040199632997866, kl: 0.5444209575653076\n",
      "Epoch 9872, Loss: 6.729366302490234, kl: 0.5321513414382935\n",
      "Epoch 9873, Loss: 6.45867955243146, kl: 0.4991435706615448\n",
      "Epoch 9874, Loss: 6.390931164776838, kl: 0.49592092633247375\n",
      "Epoch 9875, Loss: 6.845641171490705, kl: 0.5021516680717468\n",
      "Epoch 9876, Loss: 7.331149489791305, kl: 0.510626494884491\n",
      "Epoch 9877, Loss: 6.809839460584852, kl: 0.5021124482154846\n",
      "Epoch 9878, Loss: 7.000662415115921, kl: 0.558958888053894\n",
      "Epoch 9879, Loss: 7.60887697007921, kl: 0.5252910256385803\n",
      "Epoch 9880, Loss: 7.307111669469763, kl: 0.5321555137634277\n",
      "Epoch 9881, Loss: 6.836523409242983, kl: 0.5096269249916077\n",
      "Epoch 9882, Loss: 6.775433716950594, kl: 0.5309401750564575\n",
      "Epoch 9883, Loss: 6.621490902370876, kl: 0.48759105801582336\n",
      "Epoch 9884, Loss: 6.764744793927228, kl: 0.5598996877670288\n",
      "Epoch 9885, Loss: 6.885495503743489, kl: 0.5071930289268494\n",
      "Epoch 9886, Loss: 7.038695723922165, kl: 0.5268169045448303\n",
      "Epoch 9887, Loss: 7.404253783049406, kl: 0.5276846885681152\n",
      "Epoch 9888, Loss: 7.2522510245994285, kl: 0.5157111883163452\n",
      "Epoch 9889, Loss: 6.732386412443938, kl: 0.5114527344703674\n",
      "Epoch 9890, Loss: 6.6703426219798905, kl: 0.5371525883674622\n",
      "Epoch 9891, Loss: 6.565760577166522, kl: 0.5437682271003723\n",
      "Epoch 9892, Loss: 6.718330383300781, kl: 0.5473673939704895\n",
      "Epoch 9893, Loss: 6.829139144332321, kl: 0.5781055092811584\n",
      "Epoch 9894, Loss: 6.772825382373951, kl: 0.557763397693634\n",
      "Epoch 9895, Loss: 6.85123168097602, kl: 0.5514081716537476\n",
      "Epoch 9896, Loss: 6.774319048281069, kl: 0.4807046353816986\n",
      "Epoch 9897, Loss: 6.873374938964844, kl: 0.49815189838409424\n",
      "Epoch 9898, Loss: 7.3647657323766635, kl: 0.5334445834159851\n",
      "Epoch 9899, Loss: 6.979111918696651, kl: 0.5139688849449158\n",
      "Epoch 9900, Loss: 6.673871499520761, kl: 0.5286155343055725\n",
      "Epoch 9901, Loss: 6.966133824101201, kl: 0.5098778605461121\n",
      "Epoch 9902, Loss: 6.726738117359303, kl: 0.542775571346283\n",
      "Epoch 9903, Loss: 6.84153973614728, kl: 0.5273255109786987\n",
      "Epoch 9904, Loss: 7.282924369529441, kl: 0.48881134390830994\n",
      "Epoch 9905, Loss: 6.772659584327981, kl: 0.5115934014320374\n",
      "Epoch 9906, Loss: 6.586171750669126, kl: 0.49451157450675964\n",
      "Epoch 9907, Loss: 6.816427584047671, kl: 0.5477443933486938\n",
      "Epoch 9908, Loss: 6.689406571564851, kl: 0.5244700312614441\n",
      "Epoch 9909, Loss: 6.806710843686704, kl: 0.4783232510089874\n",
      "Epoch 9910, Loss: 6.8525796819616245, kl: 0.5207088589668274\n",
      "Epoch 9911, Loss: 6.983205724645544, kl: 0.4970915615558624\n",
      "Epoch 9912, Loss: 6.528377250388816, kl: 0.5211617946624756\n",
      "Epoch 9913, Loss: 6.73755787037037, kl: 0.48992085456848145\n",
      "Epoch 9914, Loss: 6.438895967271593, kl: 0.5525720119476318\n",
      "Epoch 9915, Loss: 6.506744808620876, kl: 0.4570173919200897\n",
      "Epoch 9916, Loss: 6.495298950760453, kl: 0.5111422538757324\n",
      "Epoch 9917, Loss: 6.6754819375497325, kl: 0.4993204176425934\n",
      "Epoch 9918, Loss: 6.5834692495840565, kl: 0.5024151802062988\n",
      "Epoch 9919, Loss: 6.623052950258608, kl: 0.4622426927089691\n",
      "Epoch 9920, Loss: 6.524156146579319, kl: 0.5260825753211975\n",
      "Epoch 9921, Loss: 6.737594180636936, kl: 0.4823758900165558\n",
      "Epoch 9922, Loss: 6.444771448771159, kl: 0.5273499488830566\n",
      "Epoch 9923, Loss: 6.6531632741292315, kl: 0.49891600012779236\n",
      "Epoch 9924, Loss: 6.76747661166721, kl: 0.4929436445236206\n",
      "Epoch 9925, Loss: 6.733754617196542, kl: 0.5241454839706421\n",
      "Epoch 9926, Loss: 6.72194297225387, kl: 0.5519329309463501\n",
      "Epoch 9927, Loss: 6.6941535384566695, kl: 0.53774493932724\n",
      "Epoch 9928, Loss: 6.796370188395183, kl: 0.5251317024230957\n",
      "Epoch 9929, Loss: 6.8824645148383246, kl: 0.5026072263717651\n",
      "Epoch 9930, Loss: 6.476925885235822, kl: 0.49063965678215027\n",
      "Epoch 9931, Loss: 7.174475775824653, kl: 0.46711182594299316\n",
      "Epoch 9932, Loss: 6.722141831009476, kl: 0.5411733984947205\n",
      "Epoch 9933, Loss: 7.0117725089744285, kl: 0.5076004862785339\n",
      "Epoch 9934, Loss: 7.116377936469184, kl: 0.5113372802734375\n",
      "Epoch 9935, Loss: 7.083118297435619, kl: 0.5584630370140076\n",
      "Epoch 9936, Loss: 6.938551938092267, kl: 0.5024237036705017\n",
      "Epoch 9937, Loss: 7.154738461529767, kl: 0.5437920093536377\n",
      "Epoch 9938, Loss: 6.675326100102177, kl: 0.4975278377532959\n",
      "Epoch 9939, Loss: 6.8126241895887585, kl: 0.5023143291473389\n",
      "Epoch 9940, Loss: 6.867720709906684, kl: 0.5146908164024353\n",
      "Epoch 9941, Loss: 7.029815603185583, kl: 0.5167316794395447\n",
      "Epoch 9942, Loss: 7.330314424302843, kl: 0.5257391929626465\n",
      "Epoch 9943, Loss: 8.434333306771737, kl: 0.556897759437561\n",
      "Epoch 9944, Loss: 7.746626677336516, kl: 0.5196612477302551\n",
      "Epoch 9945, Loss: 7.4291963224057795, kl: 0.5531973242759705\n",
      "Epoch 9946, Loss: 9.670122499819156, kl: 0.5477907657623291\n",
      "Epoch 9947, Loss: 8.814826894689489, kl: 0.5763465166091919\n",
      "Epoch 9948, Loss: 9.428977542453342, kl: 0.5202512741088867\n",
      "Epoch 9949, Loss: 8.083020245587385, kl: 0.5509554743766785\n",
      "Epoch 9950, Loss: 8.09608699657299, kl: 0.4970859885215759\n",
      "Epoch 9951, Loss: 8.117317623562283, kl: 0.5317550897598267\n",
      "Epoch 9952, Loss: 7.7785868468108, kl: 0.5132296681404114\n",
      "Epoch 9953, Loss: 7.732871232209383, kl: 0.5123053193092346\n",
      "Epoch 9954, Loss: 7.377688725789388, kl: 0.5032790303230286\n",
      "Epoch 9955, Loss: 7.215306105437102, kl: 0.48986610770225525\n",
      "Epoch 9956, Loss: 7.019644348709671, kl: 0.5508173108100891\n",
      "Epoch 9957, Loss: 7.136139905011213, kl: 0.5659445524215698\n",
      "Epoch 9958, Loss: 7.238237804836697, kl: 0.5522768497467041\n",
      "Epoch 9959, Loss: 9.751082455670392, kl: 0.5068022608757019\n",
      "Epoch 9960, Loss: 8.069220225016275, kl: 0.5013365149497986\n",
      "Epoch 9961, Loss: 10.646099938286675, kl: 0.5316596031188965\n",
      "Epoch 9962, Loss: 9.106668613575122, kl: 0.5274484157562256\n",
      "Epoch 9963, Loss: 9.105628472787362, kl: 0.5087332129478455\n",
      "Epoch 9964, Loss: 8.878935213442203, kl: 0.5104461908340454\n",
      "Epoch 9965, Loss: 8.308492024739584, kl: 0.5617297291755676\n",
      "Epoch 9966, Loss: 7.761808112815574, kl: 0.503158450126648\n",
      "Epoch 9967, Loss: 7.909064257586444, kl: 0.5381637811660767\n",
      "Epoch 9968, Loss: 7.410480781837746, kl: 0.5250781774520874\n",
      "Epoch 9969, Loss: 7.5392397421377675, kl: 0.4965800642967224\n",
      "Epoch 9970, Loss: 7.7174777278193725, kl: 0.548673689365387\n",
      "Epoch 9971, Loss: 7.366424136691624, kl: 0.5599680542945862\n",
      "Epoch 9972, Loss: 7.35804487157751, kl: 0.5459237098693848\n",
      "Epoch 9973, Loss: 7.081669913397895, kl: 0.5187922716140747\n",
      "Epoch 9974, Loss: 7.133527261239511, kl: 0.5314040780067444\n",
      "Epoch 9975, Loss: 6.9565777955231844, kl: 0.5353462100028992\n",
      "Epoch 9976, Loss: 7.29109834741663, kl: 0.505715548992157\n",
      "Epoch 9977, Loss: 7.120452739574291, kl: 0.5584334135055542\n",
      "Epoch 9978, Loss: 7.01218824033384, kl: 0.4930812418460846\n",
      "Epoch 9979, Loss: 7.811425880149558, kl: 0.5026541352272034\n",
      "Epoch 9980, Loss: 7.135119826705368, kl: 0.552483081817627\n",
      "Epoch 9981, Loss: 6.914843947799118, kl: 0.5386937260627747\n",
      "Epoch 9982, Loss: 7.115035445601852, kl: 0.49313539266586304\n",
      "Epoch 9983, Loss: 7.2538961481165005, kl: 0.5197592377662659\n",
      "Epoch 9984, Loss: 7.048240237765842, kl: 0.5463423728942871\n",
      "Epoch 9985, Loss: 8.24737548828125, kl: 0.5568065047264099\n",
      "Epoch 9986, Loss: 8.883817107589156, kl: 0.47284209728240967\n",
      "Epoch 9987, Loss: 7.249120005854854, kl: 0.4743824601173401\n",
      "Epoch 9988, Loss: 7.0313669840494795, kl: 0.5235311985015869\n",
      "Epoch 9989, Loss: 7.03974109225803, kl: 0.5148122906684875\n",
      "Epoch 9990, Loss: 6.960139168633355, kl: 0.5163319110870361\n",
      "Epoch 9991, Loss: 7.213165495130751, kl: 0.5153404474258423\n",
      "Epoch 9992, Loss: 6.535510734275535, kl: 0.539077877998352\n",
      "Epoch 9993, Loss: 6.59742510760272, kl: 0.556387722492218\n",
      "Epoch 9994, Loss: 6.7359997078224465, kl: 0.5043080449104309\n",
      "Epoch 9995, Loss: 6.7253846062554254, kl: 0.5338928699493408\n",
      "Epoch 9996, Loss: 6.474003050062391, kl: 0.5366862416267395\n",
      "Epoch 9997, Loss: 6.664963474980107, kl: 0.46950462460517883\n",
      "Epoch 9998, Loss: 6.826539852001049, kl: 0.48700281977653503\n",
      "Epoch 9999, Loss: 6.72411169829192, kl: 0.5329830050468445\n",
      "Epoch 10000, Loss: 6.765662581832321, kl: 0.5325378179550171\n"
     ]
    }
   ],
   "source": [
    "\n",
    "optimizer = optim.Adam(vae.parameters(), lr= 0.001)\n",
    "\n",
    "#optimizer=optim.SGD(vae.parameters(), lr=0.1, momentum=0.9)\n",
    "losses = []\n",
    "epochs = 10000\n",
    "for epoch in range(epochs):\n",
    "    vae.train()\n",
    "    total_loss = 0\n",
    "    for batch in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        x = batch[0]\n",
    "        decoded, mean, logvar = vae(x)\n",
    "        loss, kl = loss_function(decoded, x, mean, logvar)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss += loss.item()\n",
    "        avg_loss = total_loss / num_samples\n",
    "    losses.append(avg_loss)  # Store the loss for this epoch\n",
    "    print(f\"Epoch {epoch+1}, Loss: {avg_loss}, kl: {kl/num_samples}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses[5000:])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTMVAE(\n",
      "  (encoder_lstm): LSTM(165, 128, num_layers=2, batch_first=True)\n",
      "  (encoder_mean): Linear(in_features=128, out_features=64, bias=True)\n",
      "  (encoder_logvar): Linear(in_features=128, out_features=64, bias=True)\n",
      "  (decoder_lstm): LSTM(64, 128, num_layers=2, batch_first=True)\n",
      "  (decoder_output): Linear(in_features=128, out_features=165, bias=True)\n",
      "  (filter): Conv1d(165, 165, kernel_size=(3,), stride=(1,), padding=(1,))\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(vae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoder_lstm.weight_ih_l0 \t\t torch.Size([512, 165])\n",
      "encoder_lstm.weight_hh_l0 \t\t torch.Size([512, 128])\n",
      "encoder_lstm.bias_ih_l0 \t\t torch.Size([512])\n",
      "encoder_lstm.bias_hh_l0 \t\t torch.Size([512])\n",
      "encoder_lstm.weight_ih_l1 \t\t torch.Size([512, 128])\n",
      "encoder_lstm.weight_hh_l1 \t\t torch.Size([512, 128])\n",
      "encoder_lstm.bias_ih_l1 \t\t torch.Size([512])\n",
      "encoder_lstm.bias_hh_l1 \t\t torch.Size([512])\n",
      "encoder_mean.weight \t\t torch.Size([64, 128])\n",
      "encoder_mean.bias \t\t torch.Size([64])\n",
      "encoder_logvar.weight \t\t torch.Size([64, 128])\n",
      "encoder_logvar.bias \t\t torch.Size([64])\n",
      "decoder_lstm.weight_ih_l0 \t\t torch.Size([512, 64])\n",
      "decoder_lstm.weight_hh_l0 \t\t torch.Size([512, 128])\n",
      "decoder_lstm.bias_ih_l0 \t\t torch.Size([512])\n",
      "decoder_lstm.bias_hh_l0 \t\t torch.Size([512])\n",
      "decoder_lstm.weight_ih_l1 \t\t torch.Size([512, 128])\n",
      "decoder_lstm.weight_hh_l1 \t\t torch.Size([512, 128])\n",
      "decoder_lstm.bias_ih_l1 \t\t torch.Size([512])\n",
      "decoder_lstm.bias_hh_l1 \t\t torch.Size([512])\n",
      "decoder_output.weight \t\t torch.Size([165, 128])\n",
      "decoder_output.bias \t\t torch.Size([165])\n",
      "filter.weight \t\t torch.Size([165, 165, 3])\n",
      "filter.bias \t\t torch.Size([165])\n"
     ]
    }
   ],
   "source": [
    "for param_tensor in vae.state_dict():\n",
    "    print(param_tensor, \"\\t\\t\", vae.state_dict()[param_tensor].size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prettytable import PrettyTable\n",
    "def count_parameters(model):\n",
    "    table = PrettyTable([\"Modules\", \"Parameters\"])\n",
    "    total_params = 0\n",
    "    for name, parameter in model.named_parameters():\n",
    "        if not parameter.requires_grad: continue\n",
    "        params = parameter.numel()\n",
    "        table.add_row([name, params])\n",
    "        total_params+=params\n",
    "    print(table)\n",
    "    print(f\"Total Trainable Params: {total_params}\")\n",
    "    return total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------------+------------+\n",
      "|          Modules          | Parameters |\n",
      "+---------------------------+------------+\n",
      "| encoder_lstm.weight_ih_l0 |   84480    |\n",
      "| encoder_lstm.weight_hh_l0 |   65536    |\n",
      "|  encoder_lstm.bias_ih_l0  |    512     |\n",
      "|  encoder_lstm.bias_hh_l0  |    512     |\n",
      "| encoder_lstm.weight_ih_l1 |   65536    |\n",
      "| encoder_lstm.weight_hh_l1 |   65536    |\n",
      "|  encoder_lstm.bias_ih_l1  |    512     |\n",
      "|  encoder_lstm.bias_hh_l1  |    512     |\n",
      "|    encoder_mean.weight    |    8192    |\n",
      "|     encoder_mean.bias     |     64     |\n",
      "|   encoder_logvar.weight   |    8192    |\n",
      "|    encoder_logvar.bias    |     64     |\n",
      "| decoder_lstm.weight_ih_l0 |   32768    |\n",
      "| decoder_lstm.weight_hh_l0 |   65536    |\n",
      "|  decoder_lstm.bias_ih_l0  |    512     |\n",
      "|  decoder_lstm.bias_hh_l0  |    512     |\n",
      "| decoder_lstm.weight_ih_l1 |   65536    |\n",
      "| decoder_lstm.weight_hh_l1 |   65536    |\n",
      "|  decoder_lstm.bias_ih_l1  |    512     |\n",
      "|  decoder_lstm.bias_hh_l1  |    512     |\n",
      "|   decoder_output.weight   |   21120    |\n",
      "|    decoder_output.bias    |    165     |\n",
      "|       filter.weight       |   81675    |\n",
      "|        filter.bias        |    165     |\n",
      "+---------------------------+------------+\n",
      "Total Trainable Params: 634197\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "634197"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_parameters(vae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(vae, \"model\\LSTMVAE.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vae = LSTMVAE(input_size, hidden_size, latent_size, num_layers)\n",
    "vae.load_state_dict(torch.load(\"model\\LSTMVAE.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_tensor = torch.tensor(test_data, dtype=torch.float32)\n",
    "\n",
    "# Pass the test data through the trained VAE model\n",
    "vae.eval()\n",
    "with torch.no_grad():\n",
    "    decoded, _, _ = vae(test_data_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with torch.no_grad():\n",
    "    def plot_label_clusters(x):\n",
    "        _,z_mean, _ = vae(x)\n",
    "        plt.figure(figsize =(12, 10))\n",
    "        print(z_mean.shape)\n",
    "        sc = plt.scatter(z_mean[:, 0].numpy(), z_mean[:, 1].numpy())\n",
    "        plt.xlabel(\"z[0]\")\n",
    "        plt.ylabel(\"z[1]\")\n",
    "        plt.show()\n",
    "        \n",
    "    plot_label_clusters(test_data_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_test_org=test_data_tensor.numpy()\n",
    "plot_test_gen = decoded.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 50, 165)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_test_gen.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "inference_path=\"output\\inference\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#n=plot_test_org.shape[0]\n",
    "n=21\n",
    "for i in range (15, 21):\n",
    "    sample = i\n",
    "    plot_test_org_sample=plot_test_org[sample]\n",
    "    plot_test_org_reshaped=plot_test_org_sample.reshape(50, 55, 3).transpose(1, 0, 2)\n",
    "    \n",
    "    plot_test_gen_sample=plot_test_gen[sample]\n",
    "    plot_test_gen_reshaped=plot_test_gen_sample.reshape(50, 55, 3).transpose(1, 0, 2)\n",
    "    \n",
    "    os.mkdir(inference_path+str(sample))\n",
    "    \n",
    "    visualize(50, plot_test_org_reshaped, 0, False, inference_path+str(sample)+\"\\original.avi\") # visualization of original data\n",
    "    visualize(50, plot_test_gen_reshaped, 0, False, inference_path+str(sample)+\"\\generated.avi\") #visualization of test data\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SIH",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}