mnist_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-42ced6846c93>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtqdm\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "@np.vectorize   # wrapper function\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.e ** -x)\n",
    "activation_function = sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import truncnorm\n",
    "\n",
    "def truncated_normal(mean=0, sd=1, low=0, upp=10):\n",
    "    return truncnorm((low - mean) / sd, \n",
    "                     (upp - mean) / sd, \n",
    "                     loc=mean, \n",
    "                     scale=sd)\n",
    "\n",
    "\n",
    "class NeuralNetwork:\n",
    "    \n",
    "    def __init__(self, no_of_in_nodes, no_of_out_nodes, no_of_hidden_nodes, learning_rate):\n",
    "        self.no_of_in_nodes = no_of_in_nodes\n",
    "        self.no_of_out_nodes = no_of_out_nodes\n",
    "        self.no_of_hidden_nodes = no_of_hidden_nodes\n",
    "        self.learning_rate = learning_rate \n",
    "        self.create_weight_matrices()\n",
    "        \n",
    "    def create_weight_matrices(self):\n",
    "        \"\"\" \n",
    "        A method to initialize the weight \n",
    "        matrices of the neural network\n",
    "        \"\"\"\n",
    "        rad = 1 / np.sqrt(self.no_of_in_nodes)\n",
    "        X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad)\n",
    "        self.wih = X.rvs((self.no_of_hidden_nodes, self.no_of_in_nodes))\n",
    "\n",
    "        rad = 1 / np.sqrt(self.no_of_hidden_nodes)\n",
    "        X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad)\n",
    "        self.who = X.rvs((self.no_of_out_nodes, \n",
    "                                         self.no_of_hidden_nodes))\n",
    "        \n",
    "    \n",
    "    def train(self, input_vector, target_vector):\n",
    "        \"\"\"\n",
    "        input_vector and target_vector can \n",
    "        be tuple, list or ndarray\n",
    "        \"\"\"\n",
    "        \n",
    "        input_vector = np.array(input_vector, ndmin=2).T\n",
    "        target_vector = np.array(target_vector, ndmin=2).T\n",
    "        \n",
    "        output_vector1 = np.dot(self.wih, \n",
    "                                input_vector)\n",
    "        output_hidden = activation_function(output_vector1)\n",
    "        \n",
    "        output_vector2 = np.dot(self.who, \n",
    "                                output_hidden)\n",
    "        output_network = activation_function(output_vector2)\n",
    "        \n",
    "        output_errors = target_vector - output_network\n",
    "\n",
    "        # update the weights:\n",
    "        tmp = output_errors * output_network \\\n",
    "              * (1.0 - output_network)     \n",
    "        tmp = self.learning_rate  * np.dot(tmp, \n",
    "                                           output_hidden.T)\n",
    "        self.who += tmp\n",
    "\n",
    "        # calculate hidden errors:\n",
    "        hidden_errors = np.dot(self.who.T, \n",
    "                               output_errors)\n",
    "\n",
    "        # update the weights:\n",
    "        tmp = hidden_errors * output_hidden * \\\n",
    "              (1.0 - output_hidden)\n",
    "        self.wih += self.learning_rate \\\n",
    "                          * np.dot(tmp, input_vector.T)\n",
    "        \n",
    "        \n",
    "    \n",
    "    def run(self, input_vector):\n",
    "        # input_vector can be tuple, list or ndarray\n",
    "        input_vector = np.array(input_vector, ndmin=2).T\n",
    "        output_vector = np.dot(self.wih, \n",
    "                               input_vector)\n",
    "        output_vector = activation_function(output_vector)\n",
    "        \n",
    "        output_vector = np.dot(self.who, \n",
    "                               output_vector)\n",
    "        output_vector = activation_function(output_vector)\n",
    "    \n",
    "        return output_vector\n",
    "            \n",
    "    def confusion_matrix(self, data_array, labels):\n",
    "        cm = np.zeros((10, 10), int)\n",
    "        for i in range(len(data_array)):\n",
    "            res = self.run(data_array[i])\n",
    "            res_max = res.argmax()\n",
    "            target = labels[i][0]\n",
    "            cm[res_max, int(target)] += 1\n",
    "        return cm\n",
    "\n",
    "    def precision(self, label, confusion_matrix):\n",
    "        col = confusion_matrix[:, label]\n",
    "        return confusion_matrix[label, label] / col.sum()\n",
    "    \n",
    "    def recall(self, label, confusion_matrix):\n",
    "        row = confusion_matrix[label, :]\n",
    "        return confusion_matrix[label, label] / row.sum()\n",
    "        \n",
    "    \n",
    "    def evaluate(self, data, labels):\n",
    "        corrects, wrongs = 0, 0\n",
    "        for i in range(len(data)):\n",
    "            res = self.run(data[i])\n",
    "            res_max = res.argmax()\n",
    "            if res_max == labels[i]:\n",
    "                corrects += 1\n",
    "            else:\n",
    "                wrongs += 1\n",
    "        return corrects, wrongs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-0ec56b0e18b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dataset/mnist_train.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dataset/mnist_test.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    433\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 435\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    436\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m         \u001b[0mparser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, nrows)\u001b[0m\n\u001b[0;32m   1152\u001b[0m             \u001b[0mnew_rows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1154\u001b[1;33m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1156\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_currow\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mnew_rows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    390\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[0;32m    391\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 392\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   1664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1665\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1666\u001b[1;33m         \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mform_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1667\u001b[0m         \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m         \u001b[0mmgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mform_blocks\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   1732\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'IntBlock'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1734\u001b[1;33m         \u001b[0mint_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_multi_blockify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'IntBlock'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1735\u001b[0m         \u001b[0mblocks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1736\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_multi_blockify\u001b[1;34m(tuples, dtype)\u001b[0m\n\u001b[0;32m   1817\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup_block\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1819\u001b[1;33m         \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_stack_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup_block\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1821\u001b[0m         \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplacement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\sachd\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_stack_arrays\u001b[1;34m(tuples, dtype)\u001b[0m\n\u001b[0;32m   1859\u001b[0m     \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0m_shape_compat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1860\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1861\u001b[1;33m     \u001b[0mstacked\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1862\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1863\u001b[0m         \u001b[0mstacked\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_compat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv('dataset/mnist_train.csv')\n",
    "test_data = pd.read_csv('dataset/mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac = 0.99 / 255\n",
    "train_imgs = np.asfarray(train_data.iloc[:30000, 1:]) * fac + 0.01\n",
    "test_imgs = np.asfarray(test_data.iloc[:, 1:]) * fac + 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (20000,784) into shape (20000,783)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-aea503681763>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_imgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m20000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masfarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m20000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mfac\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.01\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtrain_imgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m20001\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m30000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masfarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m20001\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m30000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mfac\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.01\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# train_imgs[40001:60000, 1:] = np.asfarray(train_data.iloc[20001:60000, 1:]) * fac + 0.01\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (20000,784) into shape (20000,783)"
     ]
    }
   ],
   "source": [
    "train_imgs[:20000, 1:] = np.asfarray(train_data.iloc[:20000, 1:]) * fac + 0.01\n",
    "train_imgs[20001:30000, 1:] = np.asfarray(train_data.iloc[20001:30000, 1:]) * fac + 0.01\n",
    "# train_imgs[40001:60000, 1:] = np.asfarray(train_data.iloc[20001:60000, 1:]) * fac + 0.01\n",
    "\n",
    "\n",
    "train_labels = np.asfarray(train_data.iloc[:, :1])\n",
    "test_labels = np.asfarray(test_data.iloc[:, :1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ANN = NeuralNetwork(no_of_in_nodes = 784, \n",
    "                    no_of_out_nodes = 10, \n",
    "                    no_of_hidden_nodes = 100,\n",
    "                    learning_rate = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = np.arange(10)\n",
    "# transform labels into one hot representation\n",
    "train_labels_one_hot = (lr==train_labels).astype(np.float)\n",
    "test_labels_one_hot = (lr==test_labels).astype(np.float)\n",
    "# we don't want zeroes and ones in the labels neither:\n",
    "train_labels_one_hot[train_labels_one_hot==0] = 0.01\n",
    "train_labels_one_hot[train_labels_one_hot==1] = 0.99\n",
    "test_labels_one_hot[test_labels_one_hot==0] = 0.01\n",
    "test_labels_one_hot[test_labels_one_hot==1] = 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in tqdm(range(len(train_imgs))):\n",
    "    ANN.train(train_imgs[i], train_labels_one_hot[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrects, wrongs = ANN.evaluate(train_imgs, train_labels)\n",
    "print(\"accruracy train: \", corrects / ( corrects + wrongs))\n",
    "corrects, wrongs = ANN.evaluate(test_imgs, test_labels)\n",
    "print(\"accruracy: test\", corrects / ( corrects + wrongs))\n",
    "cm = ANN.confusion_matrix(train_imgs, train_labels)\n",
    "print(cm)\n",
    "for i in range(10):\n",
    "    print(\"digit: \", i, \"precision: \", ANN.precision(i, cm), \"recall: \", ANN.recall(i, cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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