Converting Celsius to Fahrenheit.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Converting Celsius to Fahrenheit</b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "dt7hOxTyqoUq"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RDxPAmKuqy01",
        "outputId": "b21f07e9-2483-43f5-8f81-0f4390fab41f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-40.0 degrees Celsius = -40.0 degrees Fahrenheit\n",
            "-10.0 degrees Celsius = 14.0 degrees Fahrenheit\n",
            "0.0 degrees Celsius = 32.0 degrees Fahrenheit\n",
            "8.0 degrees Celsius = 46.0 degrees Fahrenheit\n",
            "15.0 degrees Celsius = 59.0 degrees Fahrenheit\n",
            "22.0 degrees Celsius = 72.0 degrees Fahrenheit\n",
            "38.0 degrees Celsius = 100.0 degrees Fahrenheit\n"
          ]
        }
      ],
      "source": [
        "celsius_q = np.array([-40, -10,  0,  8, 15, 22,  38],  dtype=float)\n",
        "fahrenheit_a = np.array([-40,  14, 32, 46, 59, 72, 100],  dtype=float)\n",
        "\n",
        "for i,c in enumerate(celsius_q):\n",
        "  print(\"{} degrees Celsius = {} degrees Fahrenheit\".format(c, fahrenheit_a[i]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Build a layer</b>\n",
        "\n",
        "- single dense layer ( input shape 1 = the celuis degrees coming in)\n",
        "- units how many internal variables this layer have \n",
        "- units are the neurons and are the nodes. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "l0 = tf.keras.layers.Dense(units=1, input_shape=[1]) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Assemble layers into the model</b>\n",
        "- create the model conssiteing of this single layer l0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential(l0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Compile the model</b>\n",
        "- When complied for training we give it:\n",
        "    - <b>Loss function</b>: how far from the desired outcome.\n",
        "    - <b>Optimizer function</b>: adjusting internal values in order to reduce the loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.compile(loss='mean_squared_error',optimizer=tf.keras.optimizers.Adam(0.1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Train the model</b>\n",
        "- using fit\n",
        "- and provide input, output\n",
        "- flag epochs : full iteration "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "history = model.fit(celsius_q, fahrenheit_a, epochs=500, verbose=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Display trainning statics</b>\n",
        "- using the history object\n",
        "\n",
        "the more we train, the first epochs we have high loss the more we use epochs the less error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x18a7e5d4690>]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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ZPURERET+wEAUgHjJjIiIyL8YiAIQL5kRERH5FwNRANKyh4iIiMivGIgCkLQwI8cQERER+QUDUQCK0nBhRiIiIn9iIApAXJiRiIjIvy4pEB04cACzZ8/G2LFjUVFRAQBYvXo19uzZ49XigpUrEPHmrkRERP7hcSDauHEjevfujZ9++gmfffYZamtrAQA7d+7E008/7fUCg5FrlpnF5oDZZpe5GiIiovbP40D05JNP4vnnn8fatWuhVqul7TfddBO2bNni1eKCVZQmRHrNO94TERH5nseBaNeuXRg5cmSL7UlJSTh16pRXigp2KqUCkWoVAA6sJiIi8gePA1FMTAzKyspabN+xYwc6dOjglaKo+eKMDERERES+5nEguuuuu/DEE0/AYDBAoVDA4XDgxx9/xKOPPooJEyb4osagxJlmRERE/uNxIPrrX/+Kbt26ISMjA7W1tejRowduuOEGXHPNNZg9e7YvagxKnGlGRETkPyEXb+JOrVbjrbfewpw5c7B7927U1taiX79+6NKliy/qC1racOclMxN7iIiIiHzO40DkkpmZiczMTG/WQs3oXIGogYGIiIjI11oViGbOnNnqA7700kuXXAydoQ1jICIiIvKXVgWiHTt2uL3fvn07bDYbunbtCgD4/fffoVKpMGDAAO9XGKSkHiKOISIiIvK5VgWi9evXS69feuklREdH4/3330dsbCwA4PTp05g0aRKuv/5631QZhLThzn8aI3uIiIiIfM7jWWYLFy7E/PnzpTAEALGxsXj++eexcOFCrxYXzDiGiIiIyH88DkQmkwknT55ssf3kyZOoqanxSlF0ZgwRe4iIiIh8z+NANHLkSEyaNAmfffYZjh07hmPHjuHf//43Jk+ejFGjRvmixqDEafdERET+4/G0+zfffBOPPvoo7r77blitzj/WISEhmDx5Ml588UWvFxiszlwy46BqIiIiX/M4EEVEROCNN97Aiy++iAMHDgAArrjiCkRGRnq9uGDGS2ZERET+c8kLM0ZGRqJPnz7erIWacfUQNVjtsNgcUId4fHWTiIiIWsnjQDRs2DAoFIrz7l+3bt1lFUROUWFn/mlMjVYkRGlkrIaIiKh98zgQXXXVVW7vrVYriouLsXv3bkycONFbdQU9lVKB6LAQ1DTaYGpgICIiIvIljwPRokWLzrn9mWeeQW1t7WUXRGdow0JR02jjOCIiIiIf89rAlHvuuQfvvvuutw5HaD71njPNiIiIfMlrgaiwsBBhYWHeOhwB0DXdvoOrVRMREfmWx5fMzl58UQiBsrIy/Pzzz5gzZ47XCiNOvSciIvIXjwORVqt1m2WmVCrRtWtXzJs3DyNGjPBqccFOx9WqiYiI/MLjQLR06VIflEHn4hpDxB4iIiIi3/J4DFHnzp1RWVnZYnt1dTU6d+7slaLIibfvICIi8g+PA9Hhw4dht9tbbDebzTh+/LhXiiInbRgHVRMREflDqy+ZffXVV9LrNWvWQKfTSe/tdjsKCgrQqVMnrxYX7HjHeyIiIv9odSC64447AAAKhaLFitShoaHo1KkTFi5c6NXigp2OY4iIiIj8otWByOFwAACysrKwbds2JCQk+KwocpJ6iBiIiIiIfMrjMUSHDh3yWhhasmQJ+vTpA61WC61WC71ej9WrV0v7GxsbkZ+fj/j4eERFRWH06NEoLy93O0ZpaSny8vIQERGBpKQkPPbYY7DZ3Achb9iwAf3794dGo0F2dnabmSmn40rVREREftGqHqJXX30VU6ZMQVhYGF599dULtn3ooYda/eXp6elYsGABunTpAiEE3n//fdx+++3YsWMHevbsiRkzZmDVqlVYsWIFdDodpk2bhlGjRuHHH38E4By7lJeXh5SUFGzevBllZWWYMGECQkND8de//hWAM8Dl5eVh6tSp+PDDD1FQUID7778fqampyM3NbXWtcmi+MKMQwm39JyIiIvIehRBCXKxRVlYWfv75Z8THxyMrK+v8B1MocPDgwcsqKC4uDi+++CLuvPNOJCYm4qOPPsKdd94JAPjtt9/QvXt3FBYWYsiQIVi9ejVuvfVWnDhxAsnJyQCAN998E0888QROnjwJtVqNJ554AqtWrcLu3bul77jrrrtQXV2Nb7/9tlU1mUwm6HQ6GI1GaLXay/p9nmiw2NF9rrPGPc/mIlLj8bJRREREQcuTv9+t+gt76NChc772JrvdjhUrVqCurg56vR5FRUWwWq3IycmR2nTr1g2ZmZlSICosLETv3r2lMAQAubm5eOCBB7Bnzx7069cPhYWFbsdwtZk+ffp5azGbzTCbzdJ7k8nkvR/qgbBQJUJVCljtAsYGKwMRERGRj3jt5q6XateuXYiKioJGo8HUqVPx+eefo0ePHjAYDFCr1YiJiXFrn5ycDIPBAAAwGAxuYci137XvQm1MJhMaGhrOWdP8+fOh0+mkR0ZGhjd+qscUCgV04WoAQHU9B1YTERH5isddDna7HUuXLkVBQQEqKiqk2Wcu69at8+h4Xbt2RXFxMYxGIz799FNMnDgRGzdu9LQsr5o1axZmzpwpvTeZTLKFopiIUJyqNaO6wSLL9xMREQUDjwPRww8/jKVLlyIvLw+9evW67IG+arUa2dnZAIABAwZg27ZteOWVVzBmzBhYLBZUV1e79RKVl5cjJSUFAJCSkoKtW7e6Hc81C615m7NnppWXl0Or1SI8PPycNWk0Gmg0msv6Xd4SG+EcWM0eIiIiIt/xOBAtW7YMy5cvxx//+Edf1AOHwwGz2YwBAwYgNDQUBQUFGD16NACgpKQEpaWl0Ov1AAC9Xo+//OUvqKioQFJSEgBg7dq10Gq16NGjh9Tmm2++cfuOtWvXSscIdLxkRkRE5HseB6LmPTqXa9asWbjllluQmZmJmpoafPTRR9iwYYN0a5DJkydj5syZiIuLg1arxYMPPgi9Xo8hQ4YAAEaMGIEePXpg/PjxeOGFF2AwGDB79mzk5+dLPTxTp07F4sWL8fjjj+O+++7DunXrsHz5cqxatcorv8HXXD1Ep+t5yYyIiMhXPB5U/cgjj+CVV15BK2brX1RFRQUmTJiArl27Yvjw4di2bRvWrFmDP/zhDwCARYsW4dZbb8Xo0aNxww03ICUlBZ999pn0eZVKhZUrV0KlUkGv1+Oee+7BhAkTMG/ePKlNVlYWVq1ahbVr16Jv375YuHAh3n777YBfg8glJoK37yAiIvK1Vq1D1NzIkSOxfv16xMXFoWfPnggNDXXb3zywtBdyrUMEAK+v348X15Tgvwam44U7+/r1u4mIiNoyr69D1FxMTAxGjhx5ycWRZ2KkS2bsISIiIvIVjwPRe++954s66DximgZVGxmIiIiIfEb2hRnpwjiomoiIyPc87iHq16/fOdceUigUCAsLQ3Z2Nu69914MGzbMKwUGO51rHSIOqiYiIvIZj3uIbr75Zhw8eBCRkZEYNmwYhg0bhqioKBw4cACDBg1CWVkZcnJy8OWXX/qi3qATE+Fah8jilZl9RERE1JLHPUSnTp3CI488gjlz5rhtf/7553HkyBF89913ePrpp/Hcc8/h9ttv91qhwcp1ycxqF6i32HmDVyIiIh/wuIdo+fLlGDt2bIvtd911F5YvXw4AGDt2LEpKSi6/OkJ4qApqlfOfiZfNiIiIfMPjQBQWFobNmze32L5582aEhYUBcN5+w/WaLo9CoTgz9b6OA6uJiIh8wePrLw8++CCmTp2KoqIiDBo0CACwbds2vP322/jf//1fAMCaNWtw1VVXebXQYBYTEYqKGjNXqyYiIvIRjwPR7NmzkZWVhcWLF+P//u//AABdu3bFW2+9hbvvvhuA8/5hDzzwgHcrDWKutYg49Z6IiMg3LmmE7rhx4zBu3Ljz7g8PD7/kgqgl1yUz3vGeiIjIN7gwYxvAG7wSERH5lsc9RHa7HYsWLcLy5ctRWloKi8X9Mk5VVZXXiiOn2Ka1iDiomoiIyDc87iF69tln8dJLL2HMmDEwGo2YOXMmRo0aBaVSiWeeecYHJRJXqyYiIvItjwPRhx9+iLfeeguPPPIIQkJCMHbsWLz99tuYO3cutmzZ4osag55rUHU1B1UTERH5hMeByGAwoHfv3gCAqKgoGI1GAMCtt96KVatWebc6AnBmtWoOqiYiIvINjwNReno6ysrKAABXXHEFvvvuOwDOtYg0Go13qyMAZy6Zcdo9ERGRb3gciEaOHImCggIAzkUa58yZgy5dumDChAm47777vF4gnRlUzR4iIiIi3/B4ltmCBQuk12PGjEFmZiYKCwvRpUsX3HbbbV4tjpykQNRghcMhoFQqZK6IiIiofbnsW6fr9Xro9Xpv1ELnERvpvGRmdwgYG6yIjVTLXBEREVH70upA9MMPP7Sq3Q033HDJxdC5aUJUiNaEoMZsQ2WdhYGIiIjIy1odiIYOHQqFwnmpRghxzjYKhQJ2u907lZGbuCg1asw2VHFxRiIiIq9rdSCKjY1FdHQ07r33XowfPx4JCQm+rIvOEhepxpHKelTVmeUuhYiIqN1p9SyzsrIy/O1vf0NhYSF69+6NyZMnY/PmzdBqtdDpdNKDfCO+6TJZJXuIiIiIvK7VgUitVmPMmDFYs2YNfvvtN/Tp0wfTpk1DRkYGnnrqKdhsNl/WGfTimgJRVS0DERERkbdd0t3uMzMzMXfuXHz//fe48sorsWDBAphMJm/XRs3ERToXvWQPERERkfd5HIjMZjM++ugj5OTkoFevXkhISMCqVasQFxfni/qoieuSGQdVExEReV+rB1Vv3boV7733HpYtW4ZOnTph0qRJWL58OYOQn8QxEBEREflMqwPRkCFDkJmZiYceeggDBgwAAGzatKlFuz/96U/eq44kcVEcVE1EROQrHq1UXVpaiueee+68+7kOke+cuWTGafdERETe1upA5HA4fFkHXUTzS2ZCCGmRTCIiIrp8lzTLjPwvvmmWmdUuUGPmEgdERETexEDURoSrVQgPVQHgWkRERETexkDUhsRxtWoiIiKfYCBqQ+KjOPWeiIjIFxiI2pA4zjQjIiLyCY8D0dGjR3Hs2DHp/datWzF9+nT885//9Gph1BIvmREREfmGx4Ho7rvvxvr16wEABoMBf/jDH7B161Y89dRTmDdvntcLpDPieYNXIiIin/A4EO3evRtXX301AGD58uXo1asXNm/ejA8//BBLly71dn3UjOsGrxxDRERE5F0eByKr1QqNxvmH+fvvv5du1dGtWzeUlZV5tzpyE89LZkRERD7hcSDq2bMn3nzzTfznP//B2rVrcfPNNwMATpw4gfj4eK8XSGckRDsD0alaDqomIiLyJo8D0d/+9jf84x//wNChQzF27Fj07dsXAPDVV19Jl9LIN5KiwwAAJ2sYiIiIiLzJo5u7AsDQoUNx6tQpmEwmxMbGStunTJmCiIgIrxZH7hKjnZcqK+sssDsEVErez4yIiMgbPO4hamhogNlslsLQkSNH8PLLL6OkpARJSUleL5DOiItUQ6EA7A7BgdVERERe5HEguv322/HBBx8AAKqrqzF48GAsXLgQd9xxB5YsWeL1AumMUJUScRHOcUS8bEZEROQ9Hgei7du34/rrrwcAfPrpp0hOTsaRI0fwwQcf4NVXX/V6geTOddnsJAdWExEReY3Hgai+vh7R0dEAgO+++w6jRo2CUqnEkCFDcOTIEa8XSO6kQMQeIiIiIq/xOBBlZ2fjiy++wNGjR7FmzRqMGDECAFBRUQGtVuv1AskdAxEREZH3eRyI5s6di0cffRSdOnXC1VdfDb1eD8DZW9SvXz+vF0juXIGooqZR5kqIiIjaD4+n3d9555247rrrUFZWJq1BBADDhw/HyJEjvVoctZQYxR4iIiIib/M4EAFASkoKUlJSpLvep6enc1FGP0nScnFGIiIib/P4kpnD4cC8efOg0+nQsWNHdOzYETExMXjuuefgcDg8Otb8+fMxaNAgREdHIykpCXfccQdKSkrc2jQ2NiI/Px/x8fGIiorC6NGjUV5e7tamtLQUeXl5iIiIQFJSEh577DHYbDa3Nhs2bED//v2h0WiQnZ3dZm9EK/UQcZYZERGR13gciJ566iksXrwYCxYswI4dO7Bjxw789a9/xWuvvYY5c+Z4dKyNGzciPz8fW7Zswdq1a2G1WjFixAjU1dVJbWbMmIGvv/4aK1aswMaNG3HixAmMGjVK2m+325GXlweLxYLNmzfj/fffx9KlSzF37lypzaFDh5CXl4dhw4ahuLgY06dPx/333481a9Z4+vNlx0HVREREPiA8lJqaKr788ssW27/44guRlpbm6eHcVFRUCABi48aNQgghqqurRWhoqFixYoXU5tdffxUARGFhoRBCiG+++UYolUphMBikNkuWLBFarVaYzWYhhBCPP/646Nmzp9t3jRkzRuTm5raqLqPRKAAIo9F4Wb/PG6rrLaLjEytFxydWigaLTe5yiIiIApYnf7897iGqqqpCt27dWmzv1q0bqqqqLiucGY1GAEBcXBwAoKioCFarFTk5OW7fk5mZicLCQgBAYWEhevfujeTkZKlNbm4uTCYT9uzZI7VpfgxXG9cxzmY2m2EymdwegUIbFgJNiPOfjb1ERERE3uFxIOrbty8WL17cYvvixYvdZp15yuFwYPr06bj22mvRq1cvAIDBYIBarUZMTIxb2+TkZBgMBqlN8zDk2u/ad6E2JpMJDQ0NLWqZP38+dDqd9MjIyLjk3+VtCoWi2dR7BiIiIiJv8HiW2QsvvIC8vDx8//330hpEhYWFOHr0KL755ptLLiQ/Px+7d+/Gpk2bLvkY3jJr1izMnDlTem8ymQIqFCVGa3DsdAN7iIiIiLzE4x6iG2+8Eb///jtGjhyJ6upqVFdXY9SoUSgpKZHuceapadOmYeXKlVi/fj3S09Ol7SkpKbBYLKiurnZrX15ejpSUFKnN2bPOXO8v1kar1SI8PLxFPRqNBlqt1u0RSM6sRcTFGYmIiLzB40AEAGlpafjLX/6Cf//73/j3v/+N559/Hg6HA1OmTPHoOEIITJs2DZ9//jnWrVuHrKwst/0DBgxAaGgoCgoKpG0lJSUoLS2Veqf0ej127dqFiooKqc3atWuh1WrRo0cPqU3zY7jauI7R1vCSGRERkXddUiA6l8rKSrzzzjsefSY/Px//+te/8NFHHyE6OhoGgwEGg0Ea16PT6TB58mTMnDkT69evR1FRESZNmgS9Xo8hQ4YAAEaMGIEePXpg/Pjx2LlzJ9asWYPZs2cjPz8fGo0zOEydOhUHDx7E448/jt9++w1vvPEGli9fjhkzZnjr5/tVStPijOUm9hARERF5g9cC0aVYsmQJjEYjhg4ditTUVOnxySefSG0WLVqEW2+9FaNHj8YNN9yAlJQUfPbZZ9J+lUqFlStXQqVSQa/X45577sGECRMwb948qU1WVhZWrVqFtWvXom/fvli4cCHefvtt5Obm+vX3ekuKzhmIyowMRERERN6gEEIIbxxo586d6N+/P+x2uzcOF1BMJhN0Oh2MRmNAjCf6cf8pjHv7J2QnReH7mTfKXQ4REVFA8uTvt6w9RHRpXD1EBvYQEREReUWrp903v13GuZw9E4x8J7UpENWabTA1WqENC5W5IiIiorat1YFIp9NddP+ECRMuuyC6uAh1CHThoTA2WGEwNjIQERERXaZWB6L33nvPl3WQh1J1YTA2WHGiugFXJkfLXQ4REVGbxjFEbVQqxxERERF5DQNRG5Ua41xh+wQDERER0WVjIGqjUrWuHqKWN6clIiIizzAQtVGuHiIuzkhERHT5GIjaqFSuVk1EROQ1DERtFAdVExEReQ8DURuVqnNeMnMtzkhERESXjoGojQpXqxAT4VyQkb1EREREl4eBqA1LaZppdqKaM82IiIguBwNRG9ahaabZcQYiIiKiy8JA1IZlxEUAAEqr6mWuhIiIqG1jIGrDMpsC0VEGIiIiosvCQNSGZbKHiIiIyCsYiNqwzPimQFTJQERERHQ5GIjasIxYZyAyNdpgrOdaRERERJeKgagNC1erkBitAcDLZkRERJeDgaiN4zgiIiKiy8dA1MYxEBEREV0+BqI2jmsRERERXT4GojaOaxERERFdPgaiNo6XzIiIiC4fA1Eb5wpEx6sbYLM7ZK6GiIiobWIgauOSojVQhyhhdwje5JWIiOgSMRC1cUqlAlnxkQCAgyfrZK6GiIiobWIgageyk6MAAPsqamSuhIiIqG1iIGoHuiQ1BaLyWpkrISIiapsYiNqB7KZAtP8kAxEREdGlYCBqB7okRQMA9pfXQgghczVERERtDwNRO9ApIQIqpQI1ZhvKTWa5yyEiImpzGIjaAU2ICh2b1iPaX8HLZkRERJ5iIGonXOOIONOMiIjIcwxE7UQXaeo9e4iIiIg8xUDUTkgzzRiIiIiIPMZA1E64ZprtK6/hTDMiIiIPMRC1E9lJUVApFThdb0WZsVHucoiIiNoUBqJ2IixUhSuTnb1EvxwzylwNERFR28JA1I70TdcBAHYdr5a3ECIiojaGgagd6d0UiNhDRERE5BkGonakT4cYAM5AxIHVRERErcdA1I5cmRIFtUoJY4MVR6sa5C6HiIiozWAgakc0ISp0S20aWM1xRERERK3GQNTO9HENrOY4IiIiolZjIGpnXOOIdhytlrUOIiKitoSBqJ0Z2CkWAFBcWo0Gi13maoiIiNoGBqJ2JishEqm6MFjsDmw7XCV3OURERG0CA1E7o1AocG12AgDgxwOnZK6GiIiobWAgaoeuzY4HAGzeXylzJURERG2DrIHohx9+wG233Ya0tDQoFAp88cUXbvuFEJg7dy5SU1MRHh6OnJwc7Nu3z61NVVUVxo0bB61Wi5iYGEyePBm1tbVubX755Rdcf/31CAsLQ0ZGBl544QVf/zRZXXOFs4do9wkjqustMldDREQU+GQNRHV1dejbty9ef/31c+5/4YUX8Oqrr+LNN9/ETz/9hMjISOTm5qKx8czd3MeNG4c9e/Zg7dq1WLlyJX744QdMmTJF2m8ymTBixAh07NgRRUVFePHFF/HMM8/gn//8p89/n1yStWHokhQFIYDCA+wlIiIiuigRIACIzz//XHrvcDhESkqKePHFF6Vt1dXVQqPRiI8//lgIIcTevXsFALFt2zapzerVq4VCoRDHjx8XQgjxxhtviNjYWGE2m6U2TzzxhOjatWurazMajQKAMBqNl/rz/O7pL3eLjk+sFE98ulPuUoiIiGThyd/vgB1DdOjQIRgMBuTk5EjbdDodBg8ejMLCQgBAYWEhYmJiMHDgQKlNTk4OlEolfvrpJ6nNDTfcALVaLbXJzc1FSUkJTp8+fc7vNpvNMJlMbo+2Znj3JADA2r3lsDt4XzMiIqILCdhAZDAYAADJyclu25OTk6V9BoMBSUlJbvtDQkIQFxfn1uZcx2j+HWebP38+dDqd9MjIyLj8H+RnQzrHIyYiFJV1Fmw9xOn3REREFxKwgUhOs2bNgtFolB5Hjx6VuySPhaqUGNHDGfy+2nlc5mqIiIgCW8AGopSUFABAeXm52/by8nJpX0pKCioqKtz222w2VFVVubU51zGaf8fZNBoNtFqt26MtGtkvHQDw9c4yrlpNRER0AQEbiLKyspCSkoKCggJpm8lkwk8//QS9Xg8A0Ov1qK6uRlFRkdRm3bp1cDgcGDx4sNTmhx9+gNVqldqsXbsWXbt2RWxsrJ9+jTwGZ8UhIy4ctWYbVu0qk7scIiKigCVrIKqtrUVxcTGKi4sBOAdSFxcXo7S0FAqFAtOnT8fzzz+Pr776Crt27cKECROQlpaGO+64AwDQvXt33Hzzzfjzn/+MrVu34scff8S0adNw1113IS0tDQBw9913Q61WY/LkydizZw8++eQTvPLKK5g5c6ZMv9p/lEoFxl6dCQB4Z9MhCMHB1UREROfk+0lv57d+/XoBoMVj4sSJQgjn1Ps5c+aI5ORkodFoxPDhw0VJSYnbMSorK8XYsWNFVFSU0Gq1YtKkSaKmpsatzc6dO8V1110nNBqN6NChg1iwYIFHdbbFafcup+vMotvs1aLjEyvF+t/K5S6HiIjIbzz5+60Qgt0GF2MymaDT6WA0GtvkeKLnVu7FO5sOoVcHLb7Kvw5KpULukoiIiHzOk7/fATuGiLznf4ZegUi1CruPm7D857Y3Y46IiMjXGIiCQHyUBjP+cCUA4C/f/IqjVfUyV0RERBRYGIiCxL3XdMJVGTGoabThv/+vCKZG68U/REREFCQYiIJEiEqJ18f1R3ykGnvLTBj/zlZU1DRe/INERERBgIEoiHSICccHk6+GLjwUO49WI2fhRrz34yGYbVy0kYiIghtnmbVCW59ldrZDp+qQ/+F27C1z3rQ2OiwEf+iejAGdYtEzTYcrEiMRHRYqc5VERESXx5O/3wxErdDeAhEA2B0Cy7aV4rWC/TCYWl46S4rWoHNiJDonRqFzQiSuSIxCdlIU0mPDoVBw2j4REQU+BiIva4+ByMXhEPj5yGmsL6nA7uNG/FpWg1O15vO2T9OF4ZrsBNxwZSKGd0tCpCbEj9USERG1HgORl7XnQHQuxgYrDp2qw8GTtTh4sg4HTzU9n6yDxe6Q2oWFKjG8WzJu65uKoV2TEBaqkrFqIiIidwxEXhZsgeh8Gix2bDtchR/3n8KaPQYcrjyznpE2LAS39k3D6P7p6J8Zw8tqREQkOwYiL2MgakkIgT0nTPh65wl8vfMEThjPjEPqnBCJ0QPSMbJfB6TFhMtYJRERBTMGIi9jILowh0Ngy8FKfFp0DKt3G9BgdU7jVyiAa66Ix50D0pHbMwURao43IiIi/2Eg8jIGotarNduwelcZPi06hp8OVUnbI9Uq/LF3KkYPSMfVneJ4g1kiIvI5BiIvYyC6NEer6vHZ9uP49/ZjKG12/7SMuHCM7JeO3J7J6JGq5XgjIiLyCQYiL2MgujxCCGw7fBr/LjqGVbvKUGu2SfvSdGG4qXsShndLhv6KeM5UIyIir2Eg8jIGIu9psNjx3V4Dvt55Apv2n0Kj9cw0/vBQFYZ0jsN1XRJxXXYCrkyOYu8RERFdMgYiL2Mg8o1Gqx2FByrx/a/lWPdbBcqM7itmJ0ZrcF12Aq7LTsC12QlI0YXJVCkREbVFDERexkDke0II/FpWg037T2LT/kpsPVTp1nsEANlJUVJAGtw5jvdbIyKiC2Ig8jIGIv9rtNqxvfQ0Nu07hR/3n8Ivx41o/l+qSqlArw466DvHQ39FPAZ1iuW0fiIicsNA5GUMRPKrrreg8EAlNu0/hU37T+FIs1WyASBUpUDf9Bjor4iHvnM8+neM5QBtIqIgx0DkZQxEgedEdQMKD1Si8GAlCg9U4nh1g9t+dYgS/TNjoO+cAP0V8bgqIwbqEKVM1RIRkRwYiLyMgSiwCSFwtKoBhQdPSSGp3GR2axMWqsSgTnEY0nSJrU8HHUJUDEhERO0ZA5GXMRC1LUIIHDxVJ4WjLQcqUVlncWsToVahX2YMBnaMw6BOceiXGYNIDccgERG1JwxEXsZA1LYJIbCvohab959yBqSDVTA2WN3aqJQK9EjVYmCnWFzdKQ4DOsUiKZrT/ImI2jIGIi9jIGpfHA5nQNp2uAo/H67CtsOnW4xBAoCO8RFNPUixGNgpDlckRnKhSCKiNoSByMsYiNq/E9UN+PnIaSkg/WYw4ez/M2IiQnFVRgyuyohBv8xYXJUeA10E10IiIgpUDERexkAUfEyNVmw/cho/Hz6NbYerUHy0Gmabo0W7zomR6JcRi36ZzqDULSWag7WJiAIEA5GXMRCRxebAbwYTio9WY0dpNXaUnsbhs9ZCApz3Y+udrkO/jJimkBSLZK2Gl9qIiGTAQORlDER0LlV1Fuw86gxHO45Wo/hoNWoabS3aJUZr0LuDDr066NCngw6903VI1nLANhGRrzEQeRkDEbWGwyFw8FQttpdWSz1JJQYTHOf4PywxWoM+TSGpdwcd+qTrkMSQRETkVQxEXsZARJeqwWLH3jITdh2rxq7jJuw+bsS+ippzhqSkpp6k3uk69EzToXtqNDrEhPNyGxHRJWIg8jIGIvKmeosNv5aZsOuYEb8cN2L3cSP2V9SeMyRpw0LQLVWLHqladE+NRvdULa5MjuZ92oiIWoGByMsYiMjX6i027D1hwq7jRuw6bsTeEybsr6iF7RwpSakAOidGoXuzkNQjVYukaA7eJiJqjoHIyxiISA4WmwP7K2rxa5nJ+TCYsPeECafrredsHxepRpekKFyZHI0rk6PQJTkaVyZHIy5S7efKiYgCAwORlzEQUaAQQqDcZMavZSbsdQWlMhMOnao75yU3AEiIUqNLkntIujI5CjERDEpE1L4xEHkZAxEFugaLHfsqavB7eS32ldfg9/Ia7KuoxbHTLW9J4pIYrXGGpKRoXJEUhSsSItE5MYrrJhFRu8FA5GUMRNRW1Zlt2F9RKwWk38trsK+89pz3bnOJVKuQlRiJzglRyEqIROfESFyR6HwdqQnxY/VERJeHgcjLGIiovak127CvKRz9Xl6Dg6fqcPBkLY6eboD9fNfeAKRow9A5MbIpKEWhc2IkOsZFID02AuoQ3rKEiAILA5GXMRBRsLDYHCitqsfBk7VSSDp4sg4HT9Whqs5y3s8pFUCqLhwd4yPQMT4CmXGRTc/O99FhvAkuEfkfA5GXMRARAdX1lqaQdCYoHa6sw5HKejRY7Rf8bFykWgpHHeMikBkfiYzYcHSIDUeKNow3xCUin2Ag8jIGIqLzE0LgZK0ZpZX1OFJZjyNV9SitrGt6rkflBXqWAEClVCBFG4YOseFIj3GGpPTYcHSIiUCH2HCkxYRBE8KFKInIc578/eYISSK6LAqFAknRYUiKDsPATnEt9tc0WlHaFI6OVDlDU2lVHY6dbsCJ6gZY7QLHqxtwvLoBW8/zHUnRGnSIDUeHmHCkx0agQ0wYUnTO3qUUXRjiI9VQKjkzjoguHXuIWoE9RES+4XA4e5eOna7HsdPOUHTsdAOON70+frrhopfjACBU5QxlqTpnQErVhSFZG4ZUXThSmrYlRWsQyktzREGFPURE1CYolQoka53hZUDHlvuFEKiqs0jhyBWYTlQ3oNzUiDJjI07Wmt16mc5HoQASozRIaQpLSdEaJDY9kqLDpNcJUWpeoiMKQgxERBSwFAoF4qM0iI/SoE96zDnbWO0OnKwxo8zYCIOxEQZTIwzGBpQZG6XQVG5qhNUuUFFjRkWNGYDxgt+rCw91C0yJUecOTzHhobxUR9ROMBARUZsWqlIiLSYcaTHh523jcAhU1VtgMDoDksHUiFNN4ehkjRkna8041fTaYnfA2GCFscGKfRW1F/xupQKIjVAjLlKN2Eg14iOdr13Pzm0a57YoNWIj1FyviShAMRARUbunVCqQEKVBQpQGvTrozttOCAFjg9UZkpqCUoXJ+SxtqzGjoqYRp+utcAigss5y0Zl0zUWHhSDurOAUE6GGLjwUMRGhzudwtfRaFxGKaE0Ib6dC5GMMRERETRQKBWIinAGlS3L0Bdta7Q6crrOgqt6CqlpnKKqqcz2bcbrOiso6M6qatlfVWeAQQE2jDTWNNhyprG91XSqlAtqwECk4ucJTTLgrNKml19FhIYgOcz5rw0IRFRYCFS/rEV0UAxER0SUIVSmRpA1DkjasVe0dDmfvU2WdBafrLaisdQUlM4wNVlTXW1HddKnOWG9FdYMF1fVWmG0O2B0Cp+utOF1vvaRaI9Qqt6AkPWtCzr09LATRGufrSE0IIjUqhIeq2EtF7RoDERGRHyiVCsQ2jSvyRKPVLgUm57MF1Q1WmKQQZYGxwYbqeguMDdamHijns9nmAADUW+yot9hRbjJfcv0KBRARqkKEJgSRapUzKKlDEKFROZ+btkVI+1xtW7aJVKugCXWGrFCVgkGLAgIDERFRAAsLVSEsVIXkVvZENWe22VHbdImuptGGGrP1zOtGq3Of2fna1LS9ttG9Tb3VDiEAIYA6ix11FjtOevH3qZQKhIUoEa5WSb81vOmhCVU6X6tVCAtRNWvjvj1M7Wzv2q4JUUEdooQ6RAnNWc9qlZIBjM4pqALR66+/jhdffBEGgwF9+/bFa6+9hquvvlrusoiIfEITooImSoX4KM0lH0MIgUarA3UWG+rMNtSZ7ai32FBnsaPe3PRsObO91mxDvdmOOosN9RY76sxNz5am7WYb6iw2OJqWBLY7hBS0/MUVkDRNAUkTqmp6Vro/nzNYqc68VykRolIgVKVEqEqBEKUSoSFKhCqd20JUiqY2zv2hzdsrlQgNcX7GdZwQlQKhSiWXcpBJ0ASiTz75BDNnzsSbb76JwYMH4+WXX0Zubi5KSkqQlJQkd3lERAFJoVAgXO3sjUm4jGB1NqvdgQarHY0Wu/PZ6nzfYLGj0dZye6Nrn9W5vcFqh/msz7j2m20OWGwO6dlid7h9t6Vpe43Xfo13qZQKZ4BqClghymahS+V8r1Iqmj0roVQCIUqltF3ptr/5+zOfb34M9/dKqJRo0fbs71Qpnf99qBQKKJWAUuHcp1Qomr12Xi5WnrcdpNehKiVSdJ73hHpL0Ny6Y/DgwRg0aBAWL14MAHA4HMjIyMCDDz6IJ5988oKf5a07iIjaLodDwGJ3BiOz1fVsd25rFpzMNrv03tw8UJ21z/Xeahew2h2wNT1bHQJWmwM2hwMWu4DN7jiz3+GA1Sac+2wO2BxNn7EHxZ/gVkmM1mDbUzlePSZv3XEWi8WCoqIizJo1S9qmVCqRk5ODwsLCFu3NZjPM5jODD00mk1/qJCIi71MqFQhTOscfQb4OiHMSQsDuEM5w5XA0BSohhSZbU5CTQpfd2d7mcMAhBGzSe9HivV0I2O3O40htXPtafMZxgWO4PuOAXQB2h7MOV+12ceZ32B0CQgB24fwuhxBNr+F83bTNIZyXS5u3CQuVd9HSoAhEp06dgt1uR3Jystv25ORk/Pbbby3az58/H88++6y/yiMioiClUCiaxg8B4eA99OTENeTPYdasWTAajdLj6NGjcpdEREREPhQUPUQJCQlQqVQoLy93215eXo6UlJQW7TUaDTQa7w0eJCIiosAWFD1EarUaAwYMQEFBgbTN4XCgoKAAer1exsqIiIgoEARFDxEAzJw5ExMnTsTAgQNx9dVX4+WXX0ZdXR0mTZokd2lEREQks6AJRGPGjMHJkycxd+5cGAwGXHXVVfj2229bDLQmIiKi4BM06xBdDq5DRERE1PZ48vc7KMYQEREREV0IAxEREREFPQYiIiIiCnoMRERERBT0GIiIiIgo6DEQERERUdBjICIiIqKgx0BEREREQS9oVqq+HK61K00mk8yVEBERUWu5/m63Zg1qBqJWqKmpAQBkZGTIXAkRERF5qqamBjqd7oJteOuOVnA4HDhx4gSio6OhUCi8emyTyYSMjAwcPXqUtwXxIZ5n/+G59g+eZ//gefYfX5xrIQRqamqQlpYGpfLCo4TYQ9QKSqUS6enpPv0OrVbL/9n8gOfZf3iu/YPn2T94nv3H2+f6Yj1DLhxUTUREREGPgYiIiIiCHgORzDQaDZ5++mloNBq5S2nXeJ79h+faP3ie/YPn2X/kPtccVE1ERERBjz1EREREFPQYiIiIiCjoMRARERFR0GMgIiIioqDHQCSj119/HZ06dUJYWBgGDx6MrVu3yl1Sm/PDDz/gtttuQ1paGhQKBb744gu3/UIIzJ07F6mpqQgPD0dOTg727dvn1qaqqgrjxo2DVqtFTEwMJk+ejNraWj/+isA3f/58DBo0CNHR0UhKSsIdd9yBkpIStzaNjY3Iz89HfHw8oqKiMHr0aJSXl7u1KS0tRV5eHiIiIpCUlITHHnsMNpvNnz8loC1ZsgR9+vSRFqbT6/VYvXq1tJ/n2DcWLFgAhUKB6dOnS9t4rr3jmWeegUKhcHt069ZN2h9Q51mQLJYtWybUarV49913xZ49e8Sf//xnERMTI8rLy+UurU355ptvxFNPPSU+++wzAUB8/vnnbvsXLFggdDqd+OKLL8TOnTvFn/70J5GVlSUaGhqkNjfffLPo27ev2LJli/jPf/4jsrOzxdixY/38SwJbbm6ueO+998Tu3btFcXGx+OMf/ygyMzNFbW2t1Gbq1KkiIyNDFBQUiJ9//lkMGTJEXHPNNdJ+m80mevXqJXJycsSOHTvEN998IxISEsSsWbPk+EkB6auvvhKrVq0Sv//+uygpKRH/+7//K0JDQ8Xu3buFEDzHvrB161bRqVMn0adPH/Hwww9L23muvePpp58WPXv2FGVlZdLj5MmT0v5AOs8MRDK5+uqrRX5+vvTebreLtLQ0MX/+fBmratvODkQOh0OkpKSIF198UdpWXV0tNBqN+Pjjj4UQQuzdu1cAENu2bZParF69WigUCnH8+HG/1d7WVFRUCABi48aNQgjneQ0NDRUrVqyQ2vz6668CgCgsLBRCOMOrUqkUBoNBarNkyRKh1WqF2Wz27w9oQ2JjY8Xbb7/Nc+wDNTU1okuXLmLt2rXixhtvlAIRz7X3PP3006Jv377n3Bdo55mXzGRgsVhQVFSEnJwcaZtSqUROTg4KCwtlrKx9OXToEAwGg9t51ul0GDx4sHSeCwsLERMTg4EDB0ptcnJyoFQq8dNPP/m95rbCaDQCAOLi4gAARUVFsFqtbue6W7duyMzMdDvXvXv3RnJystQmNzcXJpMJe/bs8WP1bYPdbseyZctQV1cHvV7Pc+wD+fn5yMvLczunAP979rZ9+/YhLS0NnTt3xrhx41BaWgog8M4zb+4qg1OnTsFut7v9AwNAcnIyfvvtN5mqan8MBgMAnPM8u/YZDAYkJSW57Q8JCUFcXJzUhtw5HA5Mnz4d1157LXr16gXAeR7VajViYmLc2p59rs/1b+HaR067du2CXq9HY2MjoqKi8Pnnn6NHjx4oLi7mOfaiZcuWYfv27di2bVuLffzv2XsGDx6MpUuXomvXrigrK8Ozzz6L66+/Hrt37w6488xAREQeyc/Px+7du7Fp0ya5S2mXunbtiuLiYhiNRnz66aeYOHEiNm7cKHdZ7crRo0fx8MMPY+3atQgLC5O7nHbtlltukV736dMHgwcPRseOHbF8+XKEh4fLWFlLvGQmg4SEBKhUqhYj6cvLy5GSkiJTVe2P61xe6DynpKSgoqLCbb/NZkNVVRX/Lc5h2rRpWLlyJdavX4/09HRpe0pKCiwWC6qrq93an32uz/Vv4dpHTmq1GtnZ2RgwYADmz5+Pvn374pVXXuE59qKioiJUVFSgf//+CAkJQUhICDZu3IhXX30VISEhSE5O5rn2kZiYGFx55ZXYv39/wP03zUAkA7VajQEDBqCgoEDa5nA4UFBQAL1eL2Nl7UtWVhZSUlLczrPJZMJPP/0knWe9Xo/q6moUFRVJbdatWweHw4HBgwf7veZAJYTAtGnT8Pnnn2PdunXIyspy2z9gwACEhoa6neuSkhKUlpa6netdu3a5BdC1a9dCq9WiR48e/vkhbZDD4YDZbOY59qLhw4dj165dKC4ulh4DBw7EuHHjpNc8175RW1uLAwcOIDU1NfD+m/bqEG1qtWXLlgmNRiOWLl0q9u7dK6ZMmSJiYmLcRtLTxdXU1IgdO3aIHTt2CADipZdeEjt27BBHjhwRQjin3cfExIgvv/xS/PLLL+L2228/57T7fv36iZ9++kls2rRJdOnShdPuz/LAAw8InU4nNmzY4DZ9tr6+XmozdepUkZmZKdatWyd+/vlnodfrhV6vl/a7ps+OGDFCFBcXi2+//VYkJiZymnIzTz75pNi4caM4dOiQ+OWXX8STTz4pFAqF+O6774QQPMe+1HyWmRA8197yyCOPiA0bNohDhw6JH3/8UeTk5IiEhARRUVEhhAis88xAJKPXXntNZGZmCrVaLa6++mqxZcsWuUtqc9avXy8AtHhMnDhRCOGcej9nzhyRnJwsNBqNGD58uCgpKXE7RmVlpRg7dqyIiooSWq1WTJo0SdTU1MjwawLXuc4xAPHee+9JbRoaGsT//M//iNjYWBERESFGjhwpysrK3I5z+PBhccstt4jw8HCRkJAgHnnkEWG1Wv38awLXfffdJzp27CjUarVITEwUw4cPl8KQEDzHvnR2IOK59o4xY8aI1NRUoVarRYcOHcSYMWPE/v37pf2BdJ4VQgjh3T4nIiIioraFY4iIiIgo6DEQERERUdBjICIiIqKgx0BEREREQY+BiIiIiIIeAxEREREFPQYiIiIiCnoMRERERBT0GIiIqM1TKBT44osv5C7DIxs2bIBCoWhxY0sikgcDERFdsnvvvRcKhaLF4+abb5a7tIsaOnQoFAoFli1b5rb95ZdfRqdOneQpiohkw0BERJfl5ptvRllZmdvj448/lrusVgkLC8Ps2bNhtVrlLsVrLBaL3CUQtUkMRER0WTQaDVJSUtwesbGx0n6FQoElS5bglltuQXh4ODp37oxPP/3U7Ri7du3CTTfdhPDwcMTHx2PKlCmora11a/Puu++iZ8+e0Gg0SE1NxbRp09z2nzp1CiNHjkRERAS6dOmCr7766qK1jx07FtXV1XjrrbfO2+bee+/FHXfc4bZt+vTpGDp0qPR+6NChePDBBzF9+nTExsYiOTkZb731Furq6jBp0iRER0cjOzsbq1evbnH8H3/8EX369EFYWBiGDBmC3bt3u+3ftGkTrr/+eoSHhyMjIwMPPfQQ6urqpP2dOnXCc889hwkTJkCr1WLKlCkX/d1E1BIDERH53Jw5czB69Gjs3LkT48aNw1133YVff/0VAFBXV4fc3FzExsZi27ZtWLFiBb7//nu3wLNkyRLk5+djypQp2LVrF7766itkZ2e7fcezzz6L//qv/8Ivv/yCP/7xjxg3bhyqqqouWJdWq8VTTz2FefPmuYWMS/H+++8jISEBW7duxYMPPogHHngA/+///T9cc8012L59O0aMGIHx48ejvr7e7XOPPfYYFi5ciG3btiExMRG33Xab1GN14MAB3HzzzRg9ejR++eUXfPLJJ9i0aVOLMPj3v/8dffv2xY4dOzBnzpzL+h1EQUsQEV2iiRMnCpVKJSIjI90ef/nLX6Q2AMTUqVPdPjd48GDxwAMPCCGE+Oc//yliY2NFbW2ttH/VqlVCqVQKg8EghBAiLS1NPPXUU+etA4CYPXu29L62tlYAEKtXrz7vZ2688Ubx8MMPi8bGRtGxY0cxb948IYQQixYtEh07dnT7jbfffrvbZx9++GFx4403uh3ruuuuk97bbDYRGRkpxo8fL20rKysTAERhYaEQQoj169cLAGLZsmVSm8rKShEeHi4++eQTIYQQkydPFlOmTHH77v/85z9CqVSKhoYGIYQQHTt2FHfcccd5fycRtU6IrGmMiNq8YcOGYcmSJW7b4uLi3N7r9foW74uLiwEAv/76K/r27YvIyEhp/7XXXguHw4GSkhIoFAqcOHECw4cPv2Adffr0kV5HRkZCq9WioqLiovVrNBrMmzdP6tW5VM2/X6VSIT4+Hr1795a2JScnA0CLmpqfm7i4OHTt2lXqPdu5cyd++eUXfPjhh1IbIQQcDgcOHTqE7t27AwAGDhx4yXUTkRMDERFdlsjIyBaXr7wpPDy8Ve1CQ0Pd3isUCjgcjlZ99p577sHf//53PP/88y1mmCmVSggh3LadaxD2ub6/+TaFQgEAra4JAGpra/Hf//3feOihh1rsy8zMlF43D5NEdGk4hoiIfG7Lli0t3rt6N7p3746dO3e6jeH58ccfoVQq0bVrV0RHR6NTp04oKCjwWX1KpRLz58/HkiVLcPjwYbd9iYmJKCsrc9vm6t3yhubn5vTp0/j999+lc9O/f3/s3bsX2dnZLR5qtdprNRARAxERXSaz2QyDweD2OHXqlFubFStW4N1338Xvv/+Op59+Glu3bpUGBo8bNw5hYWGYOHEidu/ejfXr1+PBBx/E+PHjpctMzzzzDBYuXIhXX30V+/btw/bt2/Haa6959Xfk5eVh8ODB+Mc//uG2/aabbsLPP/+MDz74APv27cPTTz/dYibY5Zg3bx4KCgqwe/du3HvvvUhISJBmtT3xxBPYvHkzpk2bhuLiYuzbtw9ffvlli0HVRHT5GIiI6LJ8++23SE1NdXtcd911bm2effZZLFu2DH369MEHH3yAjz/+GD169AAAREREYM2aNaiqqsKgQYNw5513Yvjw4Vi8eLH0+YkTJ+Lll1/GG2+8gZ49e+LWW2/Fvn37vP5b/va3v6GxsdFtW25uLubMmYPHH38cgwYNQk1NDSZMmOC171ywYAEefvhhDBgwAAaDAV9//bXU+9OnTx9s3LgRv//+O66//nr069cPc+fORVpamte+n4icFOLsi+NERF6kUCjw+eeft1jLh4gokLCHiIiIiIIeAxEREREFPU67JyKf4lV5ImoL2ENEREREQY+BiIiIiIIeAxEREREFPQYiIiIiCnoMRERERBT0GIiIiIgo6DEQERERUdBjICIiIqKg9/8BwlEKWgFogLsAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.xlabel('Epoch Number')\n",
        "plt.ylabel('Loss Magnitude')\n",
        "plt.plot(history.history['loss'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Predicting using the model</b>\n",
        "\n",
        "- We can now use it to pridct any degree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 50ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[211.29854]], dtype=float32)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.predict([100.0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<b>Layer calcualted weights</b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "These are the layer variables: [array([[1.8258514]], dtype=float32), array([28.713398], dtype=float32)]\n"
          ]
        }
      ],
      "source": [
        "print(\"These are the layer variables: {}\".format(l0.get_weights()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- There are two variables, and they are actually the numbers used in the formual which converts.\n",
        "    - 1.82 ...\n",
        "    - 28.71 (prettey close to 32)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "authorship_tag": "ABX9TyOkhI94MkZ8yrEjg5gGkXu5",
      "gpuType": "T4",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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",
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