final_WIDS_assignment.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LoPA607/WIDS-NetWeaver/blob/main/final_WIDS_assignment.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": {
        "id": "6lBJmUHoG_cG"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zYkUm1KFls7Q"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from  numpy import random\n",
        "np.random.seed(100)\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import tree\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XCOZ4sSCnnMD"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GY6qQKiooDZr",
        "outputId": "ca3392cc-6f20-49a8-a846-0421abcf3f4c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1400x1400 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data_file='MNIST_data.pkl'\n",
        "with open(data_file,'rb') as infile:\n",
        "  train_dataset=pickle.load(infile)\n",
        "X=train_dataset['X']\n",
        "y=train_dataset['y']\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_normalized = scaler.fit_transform(X)\n",
        "y_normalized = scaler.fit_transform(y)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=1000,shuffle=False)\n",
        "\n",
        "\n",
        "grid_size = 4\n",
        "fig, ax = plt.subplots(grid_size, grid_size, figsize=(14,14))\n",
        "for i in range(grid_size):\n",
        "    for j in range(grid_size):\n",
        "        ax[i,j].imshow(X[i*grid_size+j].reshape(28,28),cmap='gray')\n",
        "        ax[i,j].axis('on')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iqM4F0b3wVm1"
      },
      "outputs": [],
      "source": [
        "def relu(z):\n",
        "  return np.maximum(0,z)\n",
        "  pass\n",
        "\n",
        "\n",
        "def softmax(x, axis=1):\n",
        "    exp_x = np.exp(x - np.max(x, axis=axis,keepdims=True))\n",
        "    return exp_x / np.sum(exp_x,axis=axis,keepdims=True)\n",
        "    pass\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0LJ0cOwY6oYQ"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "def init_params():\n",
        "        input_nodes=784\n",
        "        hidden_nodes=256\n",
        "        output_nodes=10\n",
        "        w1= np.random.randn(input_nodes, hidden_nodes) * 0.01\n",
        "        bias1= np.zeros((hidden_nodes, 1))\n",
        "        w2= np.random.randn(hidden_nodes, output_nodes) * 0.01\n",
        "        bias2= np.zeros((output_nodes, 1))\n",
        "        return w1,bias1,w2,bias2,input_nodes,hidden_nodes,output_nodes\n",
        "\n",
        "\n",
        "\n",
        "pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xlTMdjBuJgJU"
      },
      "outputs": [],
      "source": [
        "def forward_propg(X,w1,w2,bias1,bias2):\n",
        "\n",
        "   z1=np.dot(X,w1)+bias1.T\n",
        "   f1=relu(z1)\n",
        "   z2=np.dot(f1,w2)+bias2.T\n",
        "   f2=softmax(z2)\n",
        "\n",
        "   return z1,z2,f1,f2\n",
        "   pass\n",
        "\n",
        "def relu_derivative(x):\n",
        "  return np.where(x > 0, 1, 0)\n",
        "  pass\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SsZv5V4wV10B"
      },
      "outputs": [],
      "source": [
        "def backward_propg(X,y,w1,w2,bias1,bias2,f1,f2,lr):\n",
        "  N=X.shape[0]\n",
        "  dz2 = f2/N\n",
        "  for i in range(N):\n",
        "    dz2[i][y[i]]-=1/N\n",
        "\n",
        "  dw2 = np.dot(f1.T,dz2)\n",
        "  db2 = np.sum(dz2, axis=0, keepdims=True).T\n",
        "  df1 = np.dot(dz2,w2.T)\n",
        "  d_relu=df1*(f1>0)\n",
        "  dw1 = np.dot(X.T,d_relu)\n",
        "  db1 = np.sum(d_relu, axis=0, keepdims=True).T\n",
        "  w2-= lr * dw2\n",
        "  bias2 -= lr * db2\n",
        "  w1 -= lr * dw1\n",
        "  bias1 -= lr * db1\n",
        "  return w1,bias1,w2,bias2\n",
        "\n",
        "  pass\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mv3PTq_Cfe42"
      },
      "outputs": [],
      "source": [
        "def cost_func(z2,f2,y):\n",
        "  loss=0\n",
        "  m=np.size(z2,0)\n",
        "  for i in range(m):\n",
        "        loss+= -np.log(f2[i][ y[i]])/(np.size(z2,0))\n",
        "\n",
        "\n",
        "  return loss\n",
        "\n",
        "\n",
        "  pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AWXo7UEZi5wE"
      },
      "outputs": [],
      "source": [
        "def train(X,y,hidden_nodes,epochs,lr):\n",
        "  w1,bias1,w2,bias2,input_nodes,hidden_nodes,output_nodes=init_params()\n",
        "  relu_derivative\n",
        "  for i in range(epochs):\n",
        "    z1,z2,f1,f2=forward_propg(X,w1,w2,bias1,bias2)\n",
        "    loss=cost_func(z2,f2,y_train)\n",
        "    w1,bias1,w2,bias2= backward_propg(X,y,w1,w2,bias1,bias2,f1,f2,lr)\n",
        "\n",
        "    if i % 25== 0:\n",
        "       print(\"Cost at 25th iteration %d:%f\" % (i,loss))\n",
        "  return w1,w2,bias1,bias2\n",
        "  pass\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XSIA8b3vjt4E"
      },
      "outputs": [],
      "source": [
        "def predict(X,w1,w2,bias1,bias2):\n",
        "    z1_test,z2_test,f1_test,f2_test=forward_propg(X,w1,w2,bias1,bias2)\n",
        "    predictions=np.argmax(f2_test,axis=1)\n",
        "    predictions = predictions.reshape([X.shape[0],1])\n",
        "    print(predictions.shape)\n",
        "    return predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rw54HVhUjvH4"
      },
      "outputs": [],
      "source": [
        "def accuracy(pred,y):\n",
        "    acc=np.mean(pred==y)*100\n",
        "    print(\"% Accuracy:\",acc,\"%\")\n",
        "    print(y.shape)\n",
        "    return acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "HrOOwjY9k1Po",
        "outputId": "8e3347b9-75bd-4de4-bf14-9e42df1add22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cost at 25th iteration 0:4.148154\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-40-e76ab1a23adf>:5: RuntimeWarning: divide by zero encountered in log\n",
            "  loss+= -np.log(f2[i][ y[i]])/(np.size(z2,0))\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cost at 25th iteration 25:2.302850\n",
            "Cost at 25th iteration 50:2.301117\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-cb72d4eccca4>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mroll_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"___23B0049______\"\u001b[0m \u001b[0;31m# enter ldap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mhidden_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m256\u001b[0m \u001b[0;31m# replace with your own hidden dimension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhidden_nodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-46-37742971aa92>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(X, y, hidden_nodes, epochs, lr)\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0mrelu_derivative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mz1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforward_propg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcost_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mbackward_propg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mf2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-30-bbc559dacc7c>\u001b[0m in \u001b[0;36mforward_propg\u001b[0;34m(X, w1, w2, bias1, bias2)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward_propg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbias2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m    \u001b[0mz1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbias1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m    \u001b[0mf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m    \u001b[0mz2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbias2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import random\n",
        "\n",
        "roll_num = \"___23B0049______\" # enter ldap\n",
        "hidden_dim = 256 # replace with your own hidden dimension\n",
        "w1,w2,bias1,bias2=train(X_train,y_train,hidden_nodes=256,epochs=1000,lr=1)\n",
        "pred=predict(X_test,w1,w2, bias1, bias2)\n",
        "accuracy(pred, y_test)\n",
        "model_dict = {\n",
        "    'z': hidden_dim, # hidden dimension of your model\n",
        "    'layer_0_wt': np.random.randn(784,hidden_dim), # layer 0 weight (784, z)\n",
        "    'layer_0_bias': np.zeros((hidden_dim,1)), # layer 0 bias (z, 1)\n",
        "    'layer_1_wt': np.random.randn(hidden_dim,10), # layer 1 weight (z, 10)\n",
        "    'layer_1_bias': np.zeros((10,1)) # layer 1 bias (10, 1)\n",
        "}\n",
        "\n",
        "assert model_dict['layer_0_wt'].shape == (784, hidden_dim)\n",
        "assert model_dict['layer_0_bias'].shape == (hidden_dim, 1)\n",
        "assert model_dict['layer_1_wt'].shape == (hidden_dim, 10)\n",
        "assert model_dict['layer_1_bias'].shape == (10, 1)\n",
        "\n",
        "with open(f'model_{roll_num}.pkl', 'wb') as f:\n",
        "    pickle.dump(model_dict, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "laiCxcvTz5IS",
        "outputId": "3d031fa4-4baf-4ff6-ab34-cc2610498a4f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "data_file='/content/model____23B0049______.pkl'\n",
        "with open(data_file,'rb') as infile:\n",
        "  train_dataset=pickle.load(infile)\n",
        "\n",
        "\n",
        "\n",
        "grid_size=4\n",
        "grid_size=4\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "fig,ax=plt.subplots(grid_size,grid_size,figsize=(10,10))\n",
        "random_indices = np.random.choice(len(X), size=16, replace=False)\n",
        "for i in range(grid_size):\n",
        "    for j in range(grid_size):\n",
        "\n",
        "        random_index = np.random.choice(len(X))\n",
        "\n",
        "\n",
        "        ax[i, j].imshow(X[random_index].reshape(28, 28), cmap=\"gray\")\n",
        "\n",
        "\n",
        "        ax[i, j].set_title(f\"Label: {y[random_index]}\")\n",
        "\n",
        "    # Display the image and true/predicted labels\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lm8Jzfn-__UE"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOFOHAwNMYyN2DP/XEu5qUm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}