PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6582e6ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5644e9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7f2d1d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b71ea9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.reshape(60000,784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ab76121",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "960cd3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7acbddc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=PCA(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "673210fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=500)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a25f184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ed05d69b50>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(range(500),model.explained_variance_ratio_,color='k')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d261cd4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ed05e7c460>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=PCA(50)\n",
    "model.fit(x_train)\n",
    "plt.plot(range(50),model.explained_variance_ratio_,color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "be089bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 50)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain_red=model.transform(x_train)\n",
    "xtrain_red.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2d58abb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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