NN from scratch - mnist digits2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ca60b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "from NN import Layer_Dense, Train_Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "09d1e2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.get_logger().setLevel('INFO')\n",
    "mnist = tf.keras.datasets.mnist\n",
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4bfd4881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cf56e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16932760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6c2630f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1e0b5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "006d7d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 60000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = []\n",
    "for image in x_train:\n",
    "    X_train.append(image.flatten())\n",
    "X_train = np.array(X_train).T\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98e89156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 10000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = []\n",
    "for image in x_test:\n",
    "    X_test.append(image.flatten())\n",
    "X_test = np.array(X_test).T\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04ada7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 60000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train / 255.\n",
    "X_test = X_test / 255.\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62a7ed9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67c368cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:  0\n",
      "[9 7 7 ... 7 5 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.0991\n",
      "loss 0.09000786599459483\n",
      "----------------------------------------------\n",
      "epoch:  10\n",
      "[9 0 7 ... 7 5 0] [5 0 4 ... 5 6 8]\n",
      "acc 0.14028333333333334\n",
      "loss 0.08948527979700999\n",
      "----------------------------------------------\n",
      "epoch:  20\n",
      "[0 0 0 ... 7 2 0] [5 0 4 ... 5 6 8]\n",
      "acc 0.20471666666666666\n",
      "loss 0.08876582142072932\n",
      "----------------------------------------------\n",
      "epoch:  30\n",
      "[0 0 0 ... 7 2 0] [5 0 4 ... 5 6 8]\n",
      "acc 0.25245\n",
      "loss 0.08726180055929969\n",
      "----------------------------------------------\n",
      "epoch:  40\n",
      "[0 0 0 ... 7 2 0] [5 0 4 ... 5 6 8]\n",
      "acc 0.32666666666666666\n",
      "loss 0.08359435766599899\n",
      "----------------------------------------------\n",
      "epoch:  50\n",
      "[3 0 0 ... 9 2 0] [5 0 4 ... 5 6 8]\n",
      "acc 0.47226666666666667\n",
      "loss 0.07584522640876518\n",
      "----------------------------------------------\n",
      "epoch:  60\n",
      "[3 0 0 ... 9 2 0] [5 0 4 ... 5 6 8]\n",
      "acc 0.5843166666666667\n",
      "loss 0.0638340710125004\n",
      "----------------------------------------------\n",
      "epoch:  70\n",
      "[3 0 4 ... 9 2 8] [5 0 4 ... 5 6 8]\n",
      "acc 0.6641666666666667\n",
      "loss 0.05320677575703992\n",
      "----------------------------------------------\n",
      "epoch:  80\n",
      "[3 0 4 ... 9 6 8] [5 0 4 ... 5 6 8]\n",
      "acc 0.7172333333333333\n",
      "loss 0.0455044397440052\n",
      "----------------------------------------------\n",
      "epoch:  90\n",
      "[3 0 4 ... 5 6 8] [5 0 4 ... 5 6 8]\n",
      "acc 0.751\n",
      "loss 0.039745042002104235\n",
      "----------------------------------------------\n",
      "epoch:  100\n",
      "[3 0 4 ... 5 6 8] [5 0 4 ... 5 6 8]\n",
      "acc 0.7762\n",
      "loss 0.03542545670215364\n",
      "----------------------------------------------\n",
      "epoch:  110\n",
      "[3 0 4 ... 5 6 8] [5 0 4 ... 5 6 8]\n",
      "acc 0.79545\n",
      "loss 0.0321273218236144\n",
      "----------------------------------------------\n",
      "epoch:  120\n",
      "[3 0 4 ... 5 6 8] [5 0 4 ... 5 6 8]\n",
      "acc 0.81075\n",
      "loss 0.02954590502677916\n",
      "----------------------------------------------\n",
      "epoch:  130\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8233166666666667\n",
      "loss 0.027500350715153266\n",
      "----------------------------------------------\n",
      "epoch:  140\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8335333333333333\n",
      "loss 0.025848194247337135\n",
      "----------------------------------------------\n",
      "epoch:  150\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8381333333333333\n",
      "loss 0.024931226278168527\n",
      "----------------------------------------------\n",
      "epoch:  160\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8095166666666667\n",
      "loss 0.027598645086671106\n",
      "----------------------------------------------\n",
      "epoch:  170\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8434166666666667\n",
      "loss 0.023630617417250883\n",
      "----------------------------------------------\n",
      "epoch:  180\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8577666666666667\n",
      "loss 0.021793873469513544\n",
      "----------------------------------------------\n",
      "epoch:  190\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8645\n",
      "loss 0.02084276099825305\n",
      "----------------------------------------------\n",
      "epoch:  200\n",
      "[3 0 4 ... 5 6 5] [5 0 4 ... 5 6 8]\n",
      "acc 0.8684\n",
      "loss 0.020138141606824745\n",
      "----------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "model = [Layer_Dense(10,784,'ReLU'), Layer_Dense(128,10,'ReLU'), Layer_Dense(10, 128,'Softmax')]\n",
    "\n",
    "train = Train_Model(model,X_train, y_train, epochs=201, learning_rate=0.1)\n",
    "train.Train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b554e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0:,1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d84efd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [[5.09745746e-05]\n",
      " [9.11781169e-01]\n",
      " [4.41456491e-02]\n",
      " [5.87316373e-03]\n",
      " [1.17808546e-03]\n",
      " [6.27804195e-03]\n",
      " [8.92332002e-03]\n",
      " [6.09217320e-03]\n",
      " [1.28665694e-02]\n",
      " [2.81085345e-03]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example = X_test[0:,2].reshape(784,1)\n",
    "train.predict_probability(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00ee24bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [1]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.predict_label(example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85381cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = train.make_predictions(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c790c904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 1 ... 4 8 6] [7 2 1 ... 4 5 6]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8761"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test = y_test\n",
    "train.get_accuracy(test_predictions, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9610f762",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}