linear_autoencoder.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryan-Deshpande/NestedML/blob/master/linear_autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecqHz3DX2usJ",
        "outputId": "ee31db82-34be-4de0-a2f7-b7c67a22201b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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        }
      ],
      "source": [
        "#torch architecture imports\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision as tv\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision\n",
        "from torchvision.datasets import ImageFolder\n",
        "import torchvision.transforms as t\n",
        "from torchvision import transforms, datasets\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "trans = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "])\n",
        "\n",
        "image = Image.open('/content/test.png').convert('L')\n",
        "image = trans(image)\n",
        "print(image.shape, \" aft trans\")\n",
        "# open image dataset\n",
        "##load = torchvision.datasets.ImageFolder(root='C://Users//deshp//Desktop//Dp//src//pytorch//test1//test1')\n",
        "##testing = DataLoader(batch_size=64, shuffle=True, dataset=load)\n",
        "\n",
        "class Autoencoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Autoencoder, self).__init__()\n",
        "\n",
        "        self.encoder = nn.Sequential(\n",
        "            # reduce input image size\n",
        "            nn.Linear(100*96, 3200),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(3200, 512),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(512, 128),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(128, 64),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(64, 12),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(12, 3),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.Linear(3, 12),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(12, 64),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(64, 128),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            nn.Linear(128, 512),\n",
        "            nn.ReLU(),      \n",
        "\n",
        "            nn.Linear(512, 3200),\n",
        "            nn.ReLU(), \n",
        "\n",
        "            nn.Linear(3200, 100*96),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        encoded = self.encoder(x)\n",
        "\n",
        "        decoded = self.decoder(encoded)\n",
        "\n",
        "        #print(decoded)\n",
        "\n",
        "        return decoded\n",
        "\n",
        "model = Autoencoder()\n",
        "print(model.encoder[0].weight.shape)\n",
        "\n",
        "# training\n",
        "# hyperparameters\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-3)\n",
        "crit = nn.MSELoss()\n",
        "epochs = 100\n",
        "\n",
        "# viewing architecture\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "logs = 'logs/'\n",
        "writer = SummaryWriter(logs)\n",
        "\n",
        "\n",
        "output = []\n",
        "for epoch in range(100000):\n",
        "    x = image.view(image.size(0), -1)\n",
        "    recons = model(x)\n",
        "\n",
        "    #print(recons)\n",
        "\n",
        "    if epoch % 10 == 0:    \n",
        "        output.append(recons)\n",
        "\n",
        "    loss = crit(recons, x)\n",
        "\n",
        "    loss.backward()\n",
        "    writer.add_scalar('training_loss', loss.item(), epoch)\n",
        "    print(f\"{loss.item()}\")\n",
        "\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "writer.add_graph(model, input_to_model=image.view( image.size(0),-1) )\n",
        "\n",
        "print(output)\n",
        "# loss in the neural network architecture is not good.\n",
        "\n",
        "for epoch in range(10):\n",
        "    plt.figure(figsize=(9,2))\n",
        "    plt.gray()\n",
        "\n",
        "    recon = output[epoch].detach().numpy()\n",
        "\n",
        "    for i,item in enumerate(recon):\n",
        "        if i >= 9: break\n",
        "        plt.subplot(2,9,9+i+1)\n",
        "        item = item.reshape(-1,100,96)\n",
        "\n",
        "        plt.imshow(item[0])\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MEax-ri3B6uQ"
      },
      "outputs": [],
      "source": [
        "torch.save(model, \"autoencversion1.pt\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNWLiUkl9+6wFK+ZO/vai0g",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}