Linear_Regression.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMOflPQbWzUg4V4HlDLIJD9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tusharbansal22/Tensorflow-Notebooks/blob/main/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "md1feexSImhA"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "g2cYUe6NJl5G"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vUEyqY8J0Nc",
        "outputId": "e05dc1bf-27e2-404d-809c-345c002244f7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-23 13:08:19--  https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2302 (2.2K) [text/plain]\n",
            "Saving to: ‘moore.csv’\n",
            "\n",
            "\rmoore.csv             0%[                    ]       0  --.-KB/s               \rmoore.csv           100%[===================>]   2.25K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-01-23 13:08:19 (29.7 MB/s) - ‘moore.csv’ saved [2302/2302]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('moore.csv',header=None).to_numpy()"
      ],
      "metadata": {
        "id": "WwQbqLi2KO4J"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WC7rYzrIKejC",
        "outputId": "abd4854c-17aa-4f17-a286-ed621bb28175"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=data[:,0].reshape(-1,1)\n",
        "Y=data[:,1]"
      ],
      "metadata": {
        "id": "G8zbiUGbvygk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X,Y);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "YXE80QdZwPba",
        "outputId": "60866d78-44b5-41d6-d1e4-0cccde1d2642"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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0sRjaMsI9m94b+G3cnXs2vU7fmXPaIvgr8IvItNKIp3BvfnA7Y+MTb/6OjTs3P7hdgV9EpJGGtowweN/Wo0F55MAog/dtBZKfwg1dLELpb74d/0BWKL3VKPCLyLSRpSaeZfKWdqfALyLTRpaaeJbJWwxin/Ztrce0whT4RaTt5NmOn2XyltAQD80ZACc9BX4RaStJA65lqYl3z+qM/UZgBnGxv/zwVmjsn3agJ3dFpK0kjaaZpSYeGqey67gZwad9k54Ebgeq8YtIy4pr0kkacK0nMOBaUk384Gh8+//o2BH+7NrzE5uU2nXyFgV+EWlJoSadWTM7+NW7x46E2T2rs1Qbr+jOCdDZYYk18dDonPO7uxKf9m2VidOzUFOPiLSkUJPO2zFBHyqabKqbbia54zrYv5jOjol3AcoXi6EtI6xc9whnrfk+K9c9wtCWkcBe2otq/CLSEGl74sTVwiEcxw+OjrF+4w7GjlT14z/ik86mFXexGH5tP/dsej31w2DtQIFfROouy7y2HWbBLpVxumd1Btv/y8cLTb0Yd7H4h6d2UZXcVsMyJFFTj4iklrYJZLJ5beOkCfpQauoJjZOfNJtW6GJRHfTL2mVYhiQK/CKSSrn2PhI9yFSuPScF/6SeOCFp+8QfHB3LNJtW6GIxnSnwi0gqWWrvSVMfhoRuunZ3dQb3NbC0h6uX9UyYAevqZT2Js2mFjjOdKfCLSCpZau+ZH3iKuen6ifNOD+4rNE7+b39obvLxY44zqzM+PIYuPO1EgV9EUslSew/Na5t0kzR00/XRF/fF1uoHlvYER+f8/rNvBI8fOs7M4zronFH1TWCG8fUrzgnmuV2oV4+IpDLYvzhxLtqQtA88hbpzjhwY5f7NIxNq9fdvHqHvzDmJo3OGjh/6pnJwdGzSJ3fblQK/iKQy2Vy0aWUZaTPtPYak42R9credmafsMpWXvr4+Hx4ebsqxRaR+0gTy6v79UPr2cOtVS/jyPc+kOm5oZM7K/cYdBwjmoRWDvpltdve+qexDNX4RyU3Sg1rDr+0/OrlJhxmrli/g0Rf3Zaq9x5nf3cWvfn2YAzGDrhnhbwmPr7kEaN8B17JQjV+k4PKc1GTlukdim01mBwZWyyqp9j5479YJN2s7Z9gxN2/LDPjJuo/nlq9GUI1fRGoWF+CB1EMpJAndkE0b9DvM+MD7TggOsTzYvzh4sar+ZnHthaVvFqF2/CJS4BcpgFATzAmdM4JNIEmB/6tD245ptrllYMmk7ey1GndP7D0Uuuk6tGUktsfP1ct6JkycXrmvIlLgF2lDoeaZUHroadvqtLJy7TguwAP8/ZO7jq477n70fV4Nxz1RjxpI1/Ye+pyPvriPW69a0pB2/DybzupFbfwibSbUE+bqZT0ThhGG0tAD6z95Hl+555lUQbkc5CsD/GRmWHhgs+A2wPGB9voswfKsNd8PzrnbiLb8pF5KeQX/hrXxm9llwJ8DHcC33X1d1fLjgTuBZcAvgGvd/dWpZCxO0pU0tCz0lRTg+v/1BI+/sv/o/leePYe7vnBR4nFC2yQJ5SHLvpLyFjpOlm2S8hZa9tHbfsjLe391NH3RvNk8/J8uTjxOaJsseU76W+e5TZbzI2mbc2/6AYd+/V6gOPn4Dp69+bLgvkK12rue2nXM/LHlYYRDs1aFjLtzV4qgD6Wgn7ap532zOrnpd87JrYac1Ce/EZLGMWqlWv+kNX4z6wBeAj4K7AY2Aavc/fmKdX4fONfdf8/MrgP+rbtfm7TftDX+pCspxPfDvaD3fRP+ccpWr+jlJ/t+Gbts0bzZ7H7zndjj3Du8K3abpID91aFtsbWm006ayc/eejfVvpLKYPi1/bHHWXn2HJ7edTDVNkl5A2LL4IQO453xY8+lRfNms/yD7489zsnHd0wIeJXHP/TOeKo8L5o3e8IFpGz1il6A3LZJKs/Q+ZF0Tn1taFtsGYQC6Mqz58QeYzJpA3LasfDLOmfA2JF027yaY028ETXuJI34xpFHjb+WwH8R8HV374/erwVw91sr1tkYrfOEmR0H/BSY6wk7Txv4Q93EyqPuhXoTxMlyUocmcS4Lnbxnr30o9bFC+0oqg58efCfVcbJsk1XWIFItS57L47nUe5vJzo+8toH8yjNvXZ0zGE0Z9TvMeOXWy3PNRzPb2JP+R8vPC0xVo5p6eoDXK97vBpaH1nH3w2Z2EHg/8PPKlczsRuBGgN7e3lQZzTIiYEiWf5osx8l6rLR52BONi552X40KHXmVQZY8Zzl2o86PRp1T3V2dvPXO4VwvZDOAI1Xvb73q3NRP29bjAtbMYRayjmPUaA0dndPdb3f3Pnfvmzt3bqptk0YETNt+V67R5XH8ehwrbR7md3elPk6WbbLK6zhZ8txh1pBtspwfWc+p0Njy3V2dwdEky71xqq08e05wuOJyk1e11St6ue3a8yeMdHnbteczsLSHU2bFD1kcKs60k620uiyjkDZDLYF/BKg8a86I0mLXiZp63kfpJm9uksbzDi0rt0lXW7V8QXDZonmzg8cJbRNKLx8rzmknzUy9r6QyyPKPnSVvofydEJi4YtG82cHjnHx8R2z6aSfNTJ3nRfNmx6avWr4g122SyjPLORUqg9BlZ+XZc4LnwdevOIf115w3Ieisv+Y8Bpb2cMvAElav6J0wlPHqFb3c9YWLgoEqtM0tA6Xlj6+5hJ+s+ziPr7nkaGC76XfOiZ3U5PrlvdnG429DobJpJbU09WwCFpnZWZQC/HXAp6rW2QDcADwBfBJ4JKl9P4ta+vTWu1fPwNKe1D1xysfKo1dPUhmUl6XphZK0Td69ekLHSdurJynPSX/rPLfJcn4kbZO2V09Z6H8hFGhuGVgy4bOVJTWNhLYJSTpH+86c0/L924uipn78ZnY58D8odee8w93/xMy+AQy7+wYzOwH438BSYD9wnbv/OGmf6scvIpJew/rxu/tDwENVaV+reP0OcM1UMiIiIo2hqRdFRApGgV9EpGAU+EVECkaBX0SkYJo2OqeZ7QNem2S1U6l6+reAil4GRf/8oDIAlQG8VwZnunu6J2CrNC3w18LMhqfabandFb0Miv75QWUAKgPItwzU1CMiUjAK/CIiBdPqgf/2ZmegBRS9DIr++UFlACoDyLEMWrqNX0RE8tfqNX4REcmZAr+ISME0NPCb2R1mttfMnqtIO8/MnjCzbWb2oJmdHKVfb2bPVPwcMbPzo2XLovV3mtlfmDVoRpEcpCyDTjP7bpT+Qnnay2jZZWa2IyqDNc34LFmlLIOZZvZ3UfpWM7u4Ypu2PA/MbIGZPWpmz5vZdjP7UpQ+x8weNrOXo9+nROkWfb6dZvasmV1Qsa8bovVfNrMbmvWZ0spQBh+Kzo9fm9kfVe2rLf8XMpTB9dHff5uZ/cjMzqvYV7oycPeG/QAfAS4AnqtI2wT8m+j154A/jtluCfBKxft/AVZQmq/iH4GPNfJzNKoMKM178L3o9SzgVWAhpeGxXwE+CMwEtgIfbvZnq1MZfBH4u+j1PGAzMKOdzwPgdOCC6PVJwEvAh4H/DqyJ0tcA/y16fXn0+Sz6vE9F6XOAH0e/T4len9Lsz1enMpgH/CbwJ8AfVeynbf8XMpTBb5X/vsDHKs6D1GXQ6KkXH6M0Xn+l3wAei14/DFwds+kq4HsAZnY6cLK7P+mlT30nMFCfHOcvZRk4MNtKs5p1Ae8Ch4ALgZ3u/mN3f5dS2VxZ77znJWUZfBh4JNpuL3AA6Gvn88Dd33D3p6PXbwEvUJq3+krgu9Fq3+W9z3MlcKeXPAl0R5+/H3jY3fe7+5uUyu2yBn6UzNKWgbvvdfdNwFjVrtr2fyFDGfwo+jsDPElpNkTIUAat0Ma/nfcyeQ0Tp3ksuxa4O3rdQ2nC97LdUVo7C5XBfcCvgDeAXcCfuvt+Kia3j0znMtgKXGFmx1lpFrhl0bJpcR6Y2UJKExg9BZzm7m9Ei34KnBa9Dv29p8V5UGMZhBS1DD5P6VsgZCiDVgj8nwN+38w2U/q6827lQjNbDrzt7s/FbTxNhMrgQmAcmA+cBfyhmX2wOVmsu1AZ3EHpRB6mNAvcjyiVSdszsxOB+4Evu/uhymXRt5hp39daZZC+DMzstykF/v+c9Zg1zcBVT+7+InApgJn9BvDxqlWu473aPpTm/T2j4n3c5O9tJaEMPgX8wN3HgL1m9jjQR+nqXvnNaNqWgbsfBr5SXs/MfkSpLfRN2vg8MLNOSv/sd7n7A1Hyz8zsdHd/I2rK2RuljxD/9x4BLq5K/2E9852nlGUQEiqbtpC2DMzsXODblO5n/SJKTl0GTa/xm9m86PcM4KvAtyqWzQD+HVH7PpTaxYBDZrYi6sXxaeD/NDTTOUsog13AJdGy2ZRu7L1I6UboIjM7y8xmUro4bmh0vvMUKgMzmxV9dszso8Bhd3++nc+DKL9/C7zg7rdVLNoAlHvm3MB7n2cD8Omod88K4GD0+TcCl5rZKVHPj0ujtJaXoQxC2vZ/IW0ZmFkv8ADwu+7+UsX66cugwXex76bUXj1G6ev754EvUarBvQSsI3qaOFr/YuDJmP30Ac9RupP9zcptWv0nTRkAJwL3Umr/fh4YrNjP5dH6rwD/tdmfq45lsBDYQenG1/+lNCRtW58HwL+m9PX9WeCZ6Ody4P3A/wNejj7rnGh9A/4y+pzbgL6KfX0O2Bn9fLbZn62OZfCB6Fw5ROkG/25KN/fb9n8hQxl8m9I33fK6wxX7SlUGGrJBRKRgmt7UIyIijaXALyJSMAr8IiIFo8AvIlIwCvwiIgWjwC8iUjAK/CIiBfP/Ae1fVv3FPb7XAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y=np.log(Y)\n",
        "plt.scatter(X,Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "BWnIVv4JxFBF",
        "outputId": "7a88f3d2-58c6-42cd-cc6e-cff1873bea74"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4114990640>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=X-X.mean()"
      ],
      "metadata": {
        "id": "urmv0E6cxUqC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=tf.keras.models.Sequential(\n",
        "    [tf.keras.layers.Input(shape=(1,)),\n",
        "    tf.keras.layers.Dense(1)]\n",
        ")"
      ],
      "metadata": {
        "id": "OrUZnGh1xjt8"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.SGD(0.001,0.9),\n",
        "    loss='mse',\n",
        ")"
      ],
      "metadata": {
        "id": "HgLlE4btyBSF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def schedule(epoch, lr):\n",
        "  if epoch>=50:\n",
        "     return 0.0001\n",
        "  return 0.001\n",
        "\n",
        "scheduler = tf.keras.callbacks.LearningRateScheduler(schedule)"
      ],
      "metadata": {
        "id": "tTOm0P6tyoIx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r= model.fit(X,Y,epoch=200, callbacks=[scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KShvJKnzIXZ",
        "outputId": "3e5c3e57-8b9f-4f90-efd4-07e0cc051e13"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "6/6 [==============================] - 1s 5ms/step - loss: 359.1556 - lr: 0.0010\n",
            "Epoch 2/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 320.5689 - lr: 0.0010\n",
            "Epoch 3/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 417.9733 - lr: 0.0010\n",
            "Epoch 4/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 349.2636 - lr: 0.0010\n",
            "Epoch 5/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 248.5393 - lr: 0.0010\n",
            "Epoch 6/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 130.7276 - lr: 0.0010\n",
            "Epoch 7/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 107.7363 - lr: 0.0010\n",
            "Epoch 8/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 77.2915 - lr: 0.0010\n",
            "Epoch 9/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 91.9515 - lr: 0.0010\n",
            "Epoch 10/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 87.6623 - lr: 0.0010\n",
            "Epoch 11/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 62.5041 - lr: 0.0010\n",
            "Epoch 12/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 48.2030 - lr: 0.0010\n",
            "Epoch 13/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 28.1265 - lr: 0.0010\n",
            "Epoch 14/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 27.4568 - lr: 0.0010\n",
            "Epoch 15/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 17.0446 - lr: 0.0010\n",
            "Epoch 16/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 12.2117 - lr: 0.0010\n",
            "Epoch 17/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 7.7412 - lr: 0.0010\n",
            "Epoch 18/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 3.8839 - lr: 0.0010\n",
            "Epoch 19/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 4.1056 - lr: 0.0010\n",
            "Epoch 20/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 3.0618 - lr: 0.0010\n",
            "Epoch 21/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 2.3863 - lr: 0.0010\n",
            "Epoch 22/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 2.0389 - lr: 0.0010\n",
            "Epoch 23/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.5609 - lr: 0.0010\n",
            "Epoch 24/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.4513 - lr: 0.0010\n",
            "Epoch 25/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.2459 - lr: 0.0010\n",
            "Epoch 26/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.3946 - lr: 0.0010\n",
            "Epoch 27/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.6993 - lr: 0.0010\n",
            "Epoch 28/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.3426 - lr: 0.0010\n",
            "Epoch 29/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.0427 - lr: 0.0010\n",
            "Epoch 30/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9832 - lr: 0.0010\n",
            "Epoch 31/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9278 - lr: 0.0010\n",
            "Epoch 32/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9099 - lr: 0.0010\n",
            "Epoch 33/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 1.0471 - lr: 0.0010\n",
            "Epoch 34/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9353 - lr: 0.0010\n",
            "Epoch 35/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9841 - lr: 0.0010\n",
            "Epoch 36/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8933 - lr: 0.0010\n",
            "Epoch 37/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8793 - lr: 0.0010\n",
            "Epoch 38/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.0540 - lr: 0.0010\n",
            "Epoch 39/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.2382 - lr: 0.0010\n",
            "Epoch 40/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.1060 - lr: 0.0010\n",
            "Epoch 41/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.8757 - lr: 0.0010\n",
            "Epoch 42/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.0789 - lr: 0.0010\n",
            "Epoch 43/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 1.0824 - lr: 0.0010\n",
            "Epoch 44/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9768 - lr: 0.0010\n",
            "Epoch 45/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9991 - lr: 0.0010\n",
            "Epoch 46/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9867 - lr: 0.0010\n",
            "Epoch 47/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9428 - lr: 0.0010\n",
            "Epoch 48/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9127 - lr: 0.0010\n",
            "Epoch 49/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8966 - lr: 0.0010\n",
            "Epoch 50/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9816 - lr: 0.0010\n",
            "Epoch 51/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.1112 - lr: 1.0000e-04\n",
            "Epoch 52/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 1.1295 - lr: 1.0000e-04\n",
            "Epoch 53/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8999 - lr: 1.0000e-04\n",
            "Epoch 54/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9312 - lr: 1.0000e-04\n",
            "Epoch 55/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9149 - lr: 1.0000e-04\n",
            "Epoch 56/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8738 - lr: 1.0000e-04\n",
            "Epoch 57/200\n",
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            "Epoch 58/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8771 - lr: 1.0000e-04\n",
            "Epoch 59/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8846 - lr: 1.0000e-04\n",
            "Epoch 60/200\n",
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            "Epoch 61/200\n",
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            "Epoch 62/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8996 - lr: 1.0000e-04\n",
            "Epoch 63/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9137 - lr: 1.0000e-04\n",
            "Epoch 64/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8734 - lr: 1.0000e-04\n",
            "Epoch 65/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8922 - lr: 1.0000e-04\n",
            "Epoch 66/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9102 - lr: 1.0000e-04\n",
            "Epoch 67/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8879 - lr: 1.0000e-04\n",
            "Epoch 68/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 69/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8736 - lr: 1.0000e-04\n",
            "Epoch 70/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8757 - lr: 1.0000e-04\n",
            "Epoch 71/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 72/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8738 - lr: 1.0000e-04\n",
            "Epoch 73/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8746 - lr: 1.0000e-04\n",
            "Epoch 74/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8753 - lr: 1.0000e-04\n",
            "Epoch 75/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8794 - lr: 1.0000e-04\n",
            "Epoch 76/200\n",
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            "Epoch 77/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8730 - lr: 1.0000e-04\n",
            "Epoch 78/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8771 - lr: 1.0000e-04\n",
            "Epoch 79/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8860 - lr: 1.0000e-04\n",
            "Epoch 80/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8701 - lr: 1.0000e-04\n",
            "Epoch 81/200\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 82/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8716 - lr: 1.0000e-04\n",
            "Epoch 83/200\n",
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            "6/6 [==============================] - 0s 4ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 85/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8713 - lr: 1.0000e-04\n",
            "Epoch 86/200\n",
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            "Epoch 87/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8722 - lr: 1.0000e-04\n",
            "Epoch 88/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8730 - lr: 1.0000e-04\n",
            "Epoch 89/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8710 - lr: 1.0000e-04\n",
            "Epoch 90/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8793 - lr: 1.0000e-04\n",
            "Epoch 91/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8778 - lr: 1.0000e-04\n",
            "Epoch 92/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8746 - lr: 1.0000e-04\n",
            "Epoch 93/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8764 - lr: 1.0000e-04\n",
            "Epoch 94/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8919 - lr: 1.0000e-04\n",
            "Epoch 95/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8835 - lr: 1.0000e-04\n",
            "Epoch 96/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8924 - lr: 1.0000e-04\n",
            "Epoch 97/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8761 - lr: 1.0000e-04\n",
            "Epoch 98/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8846 - lr: 1.0000e-04\n",
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            "Epoch 100/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8675 - lr: 1.0000e-04\n",
            "Epoch 101/200\n",
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            "Epoch 102/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8786 - lr: 1.0000e-04\n",
            "Epoch 103/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8744 - lr: 1.0000e-04\n",
            "Epoch 104/200\n",
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            "Epoch 105/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8748 - lr: 1.0000e-04\n",
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            "6/6 [==============================] - 0s 3ms/step - loss: 0.9309 - lr: 1.0000e-04\n",
            "Epoch 108/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8789 - lr: 1.0000e-04\n",
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            "6/6 [==============================] - 0s 3ms/step - loss: 0.9256 - lr: 1.0000e-04\n",
            "Epoch 110/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8931 - lr: 1.0000e-04\n",
            "Epoch 111/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9122 - lr: 1.0000e-04\n",
            "Epoch 112/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9141 - lr: 1.0000e-04\n",
            "Epoch 113/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8737 - lr: 1.0000e-04\n",
            "Epoch 114/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8777 - lr: 1.0000e-04\n",
            "Epoch 115/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8846 - lr: 1.0000e-04\n",
            "Epoch 116/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8840 - lr: 1.0000e-04\n",
            "Epoch 117/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8716 - lr: 1.0000e-04\n",
            "Epoch 118/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
            "Epoch 119/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8718 - lr: 1.0000e-04\n",
            "Epoch 120/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8716 - lr: 1.0000e-04\n",
            "Epoch 121/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8714 - lr: 1.0000e-04\n",
            "Epoch 122/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8723 - lr: 1.0000e-04\n",
            "Epoch 123/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8697 - lr: 1.0000e-04\n",
            "Epoch 124/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8773 - lr: 1.0000e-04\n",
            "Epoch 125/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8769 - lr: 1.0000e-04\n",
            "Epoch 126/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8680 - lr: 1.0000e-04\n",
            "Epoch 127/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8805 - lr: 1.0000e-04\n",
            "Epoch 128/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8749 - lr: 1.0000e-04\n",
            "Epoch 129/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8775 - lr: 1.0000e-04\n",
            "Epoch 130/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8843 - lr: 1.0000e-04\n",
            "Epoch 131/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8729 - lr: 1.0000e-04\n",
            "Epoch 132/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8803 - lr: 1.0000e-04\n",
            "Epoch 133/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
            "Epoch 134/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9023 - lr: 1.0000e-04\n",
            "Epoch 135/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9049 - lr: 1.0000e-04\n",
            "Epoch 136/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8773 - lr: 1.0000e-04\n",
            "Epoch 137/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8854 - lr: 1.0000e-04\n",
            "Epoch 138/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 139/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8914 - lr: 1.0000e-04\n",
            "Epoch 140/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8868 - lr: 1.0000e-04\n",
            "Epoch 141/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8754 - lr: 1.0000e-04\n",
            "Epoch 142/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8742 - lr: 1.0000e-04\n",
            "Epoch 143/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8698 - lr: 1.0000e-04\n",
            "Epoch 144/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8723 - lr: 1.0000e-04\n",
            "Epoch 145/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8729 - lr: 1.0000e-04\n",
            "Epoch 146/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8734 - lr: 1.0000e-04\n",
            "Epoch 147/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8753 - lr: 1.0000e-04\n",
            "Epoch 148/200\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.8674 - lr: 1.0000e-04\n",
            "Epoch 149/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8809 - lr: 1.0000e-04\n",
            "Epoch 150/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8820 - lr: 1.0000e-04\n",
            "Epoch 151/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8753 - lr: 1.0000e-04\n",
            "Epoch 152/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8758 - lr: 1.0000e-04\n",
            "Epoch 153/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8742 - lr: 1.0000e-04\n",
            "Epoch 154/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8741 - lr: 1.0000e-04\n",
            "Epoch 155/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8721 - lr: 1.0000e-04\n",
            "Epoch 156/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8725 - lr: 1.0000e-04\n",
            "Epoch 157/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8745 - lr: 1.0000e-04\n",
            "Epoch 158/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8706 - lr: 1.0000e-04\n",
            "Epoch 159/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8791 - lr: 1.0000e-04\n",
            "Epoch 160/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8908 - lr: 1.0000e-04\n",
            "Epoch 161/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8735 - lr: 1.0000e-04\n",
            "Epoch 162/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8891 - lr: 1.0000e-04\n",
            "Epoch 163/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8973 - lr: 1.0000e-04\n",
            "Epoch 164/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8865 - lr: 1.0000e-04\n",
            "Epoch 165/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8765 - lr: 1.0000e-04\n",
            "Epoch 166/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.9052 - lr: 1.0000e-04\n",
            "Epoch 167/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8719 - lr: 1.0000e-04\n",
            "Epoch 168/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8735 - lr: 1.0000e-04\n",
            "Epoch 169/200\n",
            "6/6 [==============================] - 0s 2ms/step - loss: 0.8686 - lr: 1.0000e-04\n",
            "Epoch 170/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8780 - lr: 1.0000e-04\n",
            "Epoch 171/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8752 - lr: 1.0000e-04\n",
            "Epoch 172/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8751 - lr: 1.0000e-04\n",
            "Epoch 173/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8728 - lr: 1.0000e-04\n",
            "Epoch 174/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8714 - lr: 1.0000e-04\n",
            "Epoch 175/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8801 - lr: 1.0000e-04\n",
            "Epoch 176/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8803 - lr: 1.0000e-04\n",
            "Epoch 177/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8845 - lr: 1.0000e-04\n",
            "Epoch 178/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8956 - lr: 1.0000e-04\n",
            "Epoch 179/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8679 - lr: 1.0000e-04\n",
            "Epoch 180/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.9335 - lr: 1.0000e-04\n",
            "Epoch 181/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9834 - lr: 1.0000e-04\n",
            "Epoch 182/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8872 - lr: 1.0000e-04\n",
            "Epoch 183/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9005 - lr: 1.0000e-04\n",
            "Epoch 184/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8975 - lr: 1.0000e-04\n",
            "Epoch 185/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8736 - lr: 1.0000e-04\n",
            "Epoch 186/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8711 - lr: 1.0000e-04\n",
            "Epoch 187/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8726 - lr: 1.0000e-04\n",
            "Epoch 188/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8745 - lr: 1.0000e-04\n",
            "Epoch 189/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8717 - lr: 1.0000e-04\n",
            "Epoch 190/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8709 - lr: 1.0000e-04\n",
            "Epoch 191/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8730 - lr: 1.0000e-04\n",
            "Epoch 192/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8822 - lr: 1.0000e-04\n",
            "Epoch 193/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8861 - lr: 1.0000e-04\n",
            "Epoch 194/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8793 - lr: 1.0000e-04\n",
            "Epoch 195/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.9090 - lr: 1.0000e-04\n",
            "Epoch 196/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8806 - lr: 1.0000e-04\n",
            "Epoch 197/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8887 - lr: 1.0000e-04\n",
            "Epoch 198/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8810 - lr: 1.0000e-04\n",
            "Epoch 199/200\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.8756 - lr: 1.0000e-04\n",
            "Epoch 200/200\n",
            "6/6 [==============================] - 0s 4ms/step - loss: 0.8735 - lr: 1.0000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9PAU2fg2zWF_",
        "outputId": "a0a37b5e-f619-400b-ebf7-f70a959277e0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6wXTzSwzuGk",
        "outputId": "d88bf285-63d5-4d76-887d-1ac58be4e4fc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.core.dense.Dense at 0x7f410fa9a160>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[0].get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiLOtw9Az-Er",
        "outputId": "25b66e00-32db-47cd-c537-3f85984c0863"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.34465778]], dtype=float32), array([17.745317], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a= model.layers[0].get_weights()[0][0,0]"
      ],
      "metadata": {
        "id": "TAiQKJoo0ECH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Time to double:\", np.log(2)/a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZQZtHFO0STK",
        "outputId": "6c1c9e48-474a-4ec6-8d7d-8d88a2a38e39"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to double: 2.011117181495498\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " X=X.flatten()\n",
        " denominator= X.dot(X)- X.mean()*X.sum()\n",
        " a=(X.dot(Y)-Y.mean()*X.sum())/denominator\n",
        " b=(Y.mean( )*X.dot(X)-X.mean()*X.dot(Y))/denominator\n",
        " print(a,b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdOeE_2a-CZN",
        "outputId": "637e4414-debe-4540-e821-1471cc8966dc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.341682487387313 17.764939393631764\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Time to double:\", np.log(2)/a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CakW4d3N--Ro",
        "outputId": "0518088b-6cd7-4e24-b6f6-443bc8d97f5d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time to double: 2.0286295205239204\n"
          ]
        }
      ]
    }
  ]
}