Neural network project.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb5ca88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16133642",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ts_preprocessor(object):\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.scaleFactor = 1\n",
    "    \n",
    "    def fit_transform(self, data):\n",
    "        self.scaleFactor = data.max() + 1 if self.scaleFactor == 1.0 else self.scaleFactor\n",
    "        return np.array([(x / self.scaleFactor) for x in data])\n",
    "    \n",
    "    def inverse_transform(self, data):\n",
    "        return [(x * self.scaleFactor) for x in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "219440a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import ReLU\n",
    "from keras.layers import Dense\n",
    "from matplotlib import pyplot\n",
    "from pandas import read_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27135226",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7484bd31",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>side 1</th>\n",
       "      <th>side 2</th>\n",
       "      <th>side 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
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       "      <th>3</th>\n",
       "      <td>15</td>\n",
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       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>994</th>\n",
       "      <td>2988</td>\n",
       "      <td>3984</td>\n",
       "      <td>4980</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2991</td>\n",
       "      <td>3988</td>\n",
       "      <td>4985</td>\n",
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       "      <th>996</th>\n",
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       "      <th>998</th>\n",
       "      <td>3000</td>\n",
       "      <td>4000</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     side 1  side 2  side 3\n",
       "0         3       4       5\n",
       "1         9      12      15\n",
       "2        12      16      20\n",
       "3        15      20      25\n",
       "4        18      24      30\n",
       "..      ...     ...     ...\n",
       "994    2988    3984    4980\n",
       "995    2991    3988    4985\n",
       "996    2994    3992    4990\n",
       "997    2997    3996    4995\n",
       "998    3000    4000    5000\n",
       "\n",
       "[999 rows x 3 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = read_csv('rht_new.csv', header = 0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d78c5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ff30f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make numeric\n",
    "values = values.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c77f43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make scaler invariant (remove scale factor)\n",
    "#fit transform over tranform, so I can develop a scaler model from the data\n",
    "#to be used to reverse scale invariance\n",
    "scaler = ts_preprocessor()\n",
    "values = scaler.fit_transform(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ca90716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.9988000e-04, 7.9984003e-04],\n",
       "       [1.7996401e-03, 2.3995200e-03],\n",
       "       [2.3995200e-03, 3.1993601e-03],\n",
       "       ...,\n",
       "       [5.9868026e-01, 7.9824036e-01],\n",
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       "       [5.9988004e-01, 7.9984003e-01]], dtype=float32)"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set examples and target\n",
    "examples = values[:,0:2]\n",
    "examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fdcff3d9",
   "metadata": {},
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       "       [0.984803  ],\n",
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       "       [0.9908018 ],\n",
       "       [0.9918016 ],\n",
       "       [0.9928014 ],\n",
       "       [0.99380124],\n",
       "       [0.99480104],\n",
       "       [0.99580085],\n",
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       "       [0.9988002 ],\n",
       "       [0.9998    ]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = values[:,2:3]\n",
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3da885d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set example\n",
    "c = len(examples[0])\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9bc35bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "799"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_s_index = int(len(values) * 0.8)\n",
    "test_s_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c37a025",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the neural network model\n",
    "model = Sequential()\n",
    "model.add(Dense(10, input_shape=(c,), activation= tf.nn.sigmoid))\n",
    "model.add(Dense(20, input_shape=(c,), activation= tf.nn.sigmoid))\n",
    "outputlayer = Dense(units = 1, activation= tf.nn.sigmoid)\n",
    "model.add(outputlayer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a22fa79",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=tf.losses.mean_squared_error, optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "237826ed",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_ex = examples[test_s_index:,]\n",
    "validation_ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6b508bec",
   "metadata": {},
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       "       [0.9998    ]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_tgt = target[test_s_index:,]\n",
    "validation_tgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1efff7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
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      "Epoch 101/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 102/2000\n",
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      "Epoch 114/2000\n",
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      "Epoch 1908/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.4050e-05\n",
      "Epoch 1909/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3605e-05\n",
      "Epoch 1910/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3906e-05\n",
      "Epoch 1911/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3600e-05\n",
      "Epoch 1912/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3434e-05\n",
      "Epoch 1913/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.3303e-05\n",
      "Epoch 1914/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3255e-05\n",
      "Epoch 1915/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3213e-05\n",
      "Epoch 1916/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3499e-05\n",
      "Epoch 1917/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2962e-05\n",
      "Epoch 1918/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.3015e-05\n",
      "Epoch 1919/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2744e-05\n",
      "Epoch 1920/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2964e-05\n",
      "Epoch 1921/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2924e-05\n",
      "Epoch 1922/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.2545e-05\n",
      "Epoch 1923/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.2764e-05\n",
      "Epoch 1924/2000\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 2.2397e-05\n",
      "Epoch 1925/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2330e-05\n",
      "Epoch 1926/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2716e-05\n",
      "Epoch 1927/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2269e-05\n",
      "Epoch 1928/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2172e-05\n",
      "Epoch 1929/2000\n",
      "20/20 [==============================] - 0s 4ms/step - loss: 2.2230e-05\n",
      "Epoch 1930/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.1782e-05\n",
      "Epoch 1931/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2077e-05\n",
      "Epoch 1932/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2072e-05\n",
      "Epoch 1933/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2076e-05\n",
      "Epoch 1934/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.1748e-05\n",
      "Epoch 1935/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.2267e-05\n",
      "Epoch 1936/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.2174e-05\n",
      "Epoch 1937/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.1955e-05\n",
      "Epoch 1938/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.1614e-05\n",
      "Epoch 1939/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.1700e-05\n",
      "Epoch 1940/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.1264e-05\n",
      "Epoch 1941/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.1177e-05\n",
      "Epoch 1942/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.1031e-05\n",
      "Epoch 1943/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.1006e-05\n",
      "Epoch 1944/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0838e-05\n",
      "Epoch 1945/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 2.1535e-05\n",
      "Epoch 1946/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.1044e-05\n",
      "Epoch 1947/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0715e-05\n",
      "Epoch 1948/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0721e-05\n",
      "Epoch 1949/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0678e-05\n",
      "Epoch 1950/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0494e-05\n",
      "Epoch 1951/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0583e-05\n",
      "Epoch 1952/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0384e-05\n",
      "Epoch 1953/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0184e-05\n",
      "Epoch 1954/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0218e-05\n",
      "Epoch 1955/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0357e-05\n",
      "Epoch 1956/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0333e-05\n",
      "Epoch 1957/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0398e-05\n",
      "Epoch 1958/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0023e-05\n",
      "Epoch 1959/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9873e-05\n",
      "Epoch 1960/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.0005e-05\n",
      "Epoch 1961/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9901e-05\n",
      "Epoch 1962/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 1.9583e-05\n",
      "Epoch 1963/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9553e-05\n",
      "Epoch 1964/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9538e-05\n",
      "Epoch 1965/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9513e-05\n",
      "Epoch 1966/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9419e-05\n",
      "Epoch 1967/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9326e-05\n",
      "Epoch 1968/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 1.9448e-05\n",
      "Epoch 1969/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9158e-05\n",
      "Epoch 1970/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9187e-05\n",
      "Epoch 1971/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9182e-05\n",
      "Epoch 1972/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9452e-05\n",
      "Epoch 1973/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8861e-05\n",
      "Epoch 1974/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 1.8991e-05\n",
      "Epoch 1975/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8973e-05\n",
      "Epoch 1976/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8912e-05\n",
      "Epoch 1977/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.9192e-05\n",
      "Epoch 1978/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8665e-05\n",
      "Epoch 1979/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8478e-05\n",
      "Epoch 1980/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8468e-05\n",
      "Epoch 1981/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 1.8504e-05\n",
      "Epoch 1982/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8362e-05\n",
      "Epoch 1983/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8360e-05\n",
      "Epoch 1984/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8254e-05\n",
      "Epoch 1985/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8534e-05\n",
      "Epoch 1986/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8360e-05\n",
      "Epoch 1987/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8159e-05\n",
      "Epoch 1988/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8041e-05\n",
      "Epoch 1989/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 0s 3ms/step - loss: 1.7933e-05\n",
      "Epoch 1990/2000\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 1.7894e-05\n",
      "Epoch 1991/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.7994e-05\n",
      "Epoch 1992/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8141e-05\n",
      "Epoch 1993/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8242e-05\n",
      "Epoch 1994/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.7916e-05\n",
      "Epoch 1995/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8521e-05\n",
      "Epoch 1996/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.8080e-05\n",
      "Epoch 1997/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.7908e-05\n",
      "Epoch 1998/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.7568e-05\n",
      "Epoch 1999/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.7579e-05\n",
      "Epoch 2000/2000\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 1.7444e-05\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "ret = model.fit(examples, target, batch_size= 50, epochs = 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e0357fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max value in target: 0.9998\n",
      "Min value in target: 0.0009998\n"
     ]
    }
   ],
   "source": [
    "print(\"Max value in target:\", np.max(target))\n",
    "print(\"Min value in target:\", np.min(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d8e44db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 3ms/step - loss: 6.3450e-05\n",
      "val test loss, val test acc:  6.345023575704545e-05\n"
     ]
    }
   ],
   "source": [
    "# evaluate model performance\n",
    "vret = model.evaluate(validation_ex, validation_tgt)\n",
    "print(\"val test loss, val test acc: \", vret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e255653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot history\n",
    "pyplot.plot(ret.history['loss'], label='training error')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58e437ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter the two sides of the triangle\n",
      "\n",
      "Enter Side A:\n",
      "100\n",
      "Enter Side B:\n",
      "150\n",
      "1/1 [==============================] - 0s 162ms/step\n",
      "The predicted Third Side is: 191.0658207423985\n",
      "\n",
      "Please enter the two sides of the triangle\n",
      "\n",
      "Enter Side A:\n",
      "10\n",
      "Enter Side B:\n",
      "15\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "The predicted Third Side is: 96.27645935676992\n",
      "\n",
      "Please enter the two sides of the triangle\n",
      "\n",
      "Enter Side A:\n",
      "1000\n",
      "Enter Side B:\n",
      "1400\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "The predicted Third Side is: 1716.3746384382248\n",
      "\n",
      "Please enter the two sides of the triangle\n",
      "\n",
      "Enter Side A:\n",
      "i\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'i'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_34536\\3315370512.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Please enter the two sides of the triangle\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Enter Side A:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0msidea\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\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[0m\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Enter Side B:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0msideb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\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;31mValueError\u001b[0m: could not convert string to float: 'i'"
     ]
    }
   ],
   "source": [
    "while(True):\n",
    "    print(\"Please enter the two sides of the triangle\\n\")\n",
    "    print(\"Enter Side A:\")\n",
    "    sidea = float(input())\n",
    "    print(\"Enter Side B:\")\n",
    "    sideb = float(input())\n",
    "    holder = scaler.fit_transform(numpy.array([[sidea, sideb]]))\n",
    "    prediction = model.predict(holder)\n",
    "    ret = scaler.inverse_transform(numpy.array([[prediction[0][0], 0]]))\n",
    "    print(\"The predicted Third Side is: {}\\n\".format(ret[0][0]))\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803b67c6",
   "metadata": {},
   "source": [
    "### The neural network model has low accuracy when input test is low, and its results suggest a trend where higher input values lead to more accurate predictions."
   ]
  }
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