Untitled0.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMah588tz3L8JiroIKpnZbm",
      "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/Marksman007577/Autonomous-Drive/blob/main/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVuCozyZCMIe",
        "outputId": "0db6ecac-a2ce-4e73-b836-956da3ee71a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "5HvsoMESDIyl"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hcwX2PeoQZfx",
        "outputId": "fb9517a0-af34-4ab5-b136-28fee2a73734"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.12.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# building the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(units=1, input_shape=[1]),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "# compiling the model\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "\n",
        "Xs = np.array([-1.0,0.0,1.0,2.0,3.0,4.0], dtype=float)\n",
        "ys = np.array([-3.0,-1.0,1.0,3.0,5.0,7.0], dtype=float)\n",
        "\n",
        "# fit model to the feature and label\n",
        "model.fit(Xs, ys, epochs=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-oRdQDHCuhU",
        "outputId": "b86ca260-5f9a-4e1a-97f3-eaba595cebca"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
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            "Epoch 2/500\n",
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            "Epoch 4/500\n",
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            "Epoch 5/500\n",
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            "Epoch 6/500\n",
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            "Epoch 7/500\n",
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            "Epoch 8/500\n",
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            "Epoch 9/500\n",
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            "Epoch 368/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.2084e-10\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 5.7261e-10\n",
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            "1/1 [==============================] - 0s 11ms/step - loss: 5.4131e-10\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 1.7643e-10\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 1.6660e-10\n",
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            "1/1 [==============================] - 0s 8ms/step - loss: 1.5789e-10\n",
            "Epoch 399/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.4852e-10\n",
            "Epoch 400/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.3963e-10\n",
            "Epoch 401/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.3127e-10\n",
            "Epoch 402/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.2494e-10\n",
            "Epoch 403/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.1836e-10\n",
            "Epoch 404/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.1073e-10\n",
            "Epoch 405/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.0433e-10\n",
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            "1/1 [==============================] - 0s 10ms/step - loss: 1.0176e-10\n",
            "Epoch 407/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 9.3811e-11\n",
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            "1/1 [==============================] - 0s 11ms/step - loss: 8.6831e-11\n",
            "Epoch 409/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 8.3215e-11\n",
            "Epoch 410/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 7.7970e-11\n",
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            "1/1 [==============================] - 0s 11ms/step - loss: 6.7429e-11\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 6.5344e-11\n",
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            "1/1 [==============================] - 0s 10ms/step - loss: 6.0321e-11\n",
            "Epoch 415/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.9892e-11\n",
            "Epoch 416/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.4735e-11\n",
            "Epoch 417/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 5.2495e-11\n",
            "Epoch 418/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 4.9585e-11\n",
            "Epoch 419/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.7647e-11\n",
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            "1/1 [==============================] - 0s 10ms/step - loss: 4.2666e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 4.1896e-11\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 3.7983e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 3.6986e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 3.4320e-11\n",
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            "1/1 [==============================] - 0s 10ms/step - loss: 3.2799e-11\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 3.0309e-11\n",
            "Epoch 427/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 2.9833e-11\n",
            "Epoch 428/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.7493e-11\n",
            "Epoch 429/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 2.5238e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 2.4788e-11\n",
            "Epoch 431/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.2804e-11\n",
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            "1/1 [==============================] - 0s 16ms/step - loss: 2.2001e-11\n",
            "Epoch 433/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 2.1979e-11\n",
            "Epoch 434/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.0644e-11\n",
            "Epoch 435/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.9246e-11\n",
            "Epoch 436/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.8098e-11\n",
            "Epoch 437/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.8024e-11\n",
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            "1/1 [==============================] - 0s 16ms/step - loss: 1.5215e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 1.4393e-11\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 1.4213e-11\n",
            "Epoch 443/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.3105e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 1.1343e-11\n",
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            "1/1 [==============================] - 0s 12ms/step - loss: 1.1058e-11\n",
            "Epoch 446/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.0765e-11\n",
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            "1/1 [==============================] - 0s 16ms/step - loss: 9.4668e-12\n",
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            "1/1 [==============================] - 0s 13ms/step - loss: 8.9392e-12\n",
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            "1/1 [==============================] - 0s 10ms/step - loss: 9.3507e-12\n",
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            "1/1 [==============================] - 0s 9ms/step - loss: 8.0931e-12\n",
            "Epoch 452/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 7.4329e-12\n",
            "Epoch 453/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 7.3612e-12\n",
            "Epoch 454/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 7.2215e-12\n",
            "Epoch 455/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 6.1699e-12\n",
            "Epoch 456/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 5.9313e-12\n",
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            "1/1 [==============================] - 0s 11ms/step - loss: 5.7838e-12\n",
            "Epoch 458/500\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 6.2036e-12\n",
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            "1/1 [==============================] - 0s 15ms/step - loss: 5.4120e-12\n",
            "Epoch 460/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 5.0496e-12\n",
            "Epoch 461/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 4.5522e-12\n",
            "Epoch 462/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.4983e-12\n",
            "Epoch 463/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 4.0856e-12\n",
            "Epoch 464/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.9654e-12\n",
            "Epoch 465/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.9151e-12\n",
            "Epoch 466/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9151e-12\n",
            "Epoch 467/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.9151e-12\n",
            "Epoch 468/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 3.8659e-12\n",
            "Epoch 469/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.7540e-12\n",
            "Epoch 470/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.4319e-12\n",
            "Epoch 471/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.4319e-12\n",
            "Epoch 472/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 3.3259e-12\n",
            "Epoch 473/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 3.2803e-12\n",
            "Epoch 474/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 3.3940e-12\n",
            "Epoch 475/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 3.5835e-12\n",
            "Epoch 476/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.7474e-12\n",
            "Epoch 477/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.7474e-12\n",
            "Epoch 478/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 2.6580e-12\n",
            "Epoch 479/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.3993e-12\n",
            "Epoch 480/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.3993e-12\n",
            "Epoch 481/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.3448e-12\n",
            "Epoch 482/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.3051e-12\n",
            "Epoch 483/500\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 2.2666e-12\n",
            "Epoch 484/500\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 2.2169e-12\n",
            "Epoch 485/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.2169e-12\n",
            "Epoch 486/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.9806e-12\n",
            "Epoch 487/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.9806e-12\n",
            "Epoch 488/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9806e-12\n",
            "Epoch 489/500\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 1.9806e-12\n",
            "Epoch 490/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.9806e-12\n",
            "Epoch 491/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 1.9806e-12\n",
            "Epoch 492/500\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.9806e-12\n",
            "Epoch 493/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9806e-12\n",
            "Epoch 494/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.9806e-12\n",
            "Epoch 495/500\n",
            "1/1 [==============================] - 0s 14ms/step - loss: 1.9806e-12\n",
            "Epoch 496/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 1.9806e-12\n",
            "Epoch 497/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 1.9806e-12\n",
            "Epoch 498/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 1.9806e-12\n",
            "Epoch 499/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 1.9806e-12\n",
            "Epoch 500/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 1.9806e-12\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5b9c2a73a0>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict on a new dataset\n",
        "model.predict([10.0,20.0,21.0,25.0,29.0,35.0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLzKAC7BE656",
        "outputId": "f528986e-8c3d-442f-85b9-790dde700392"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 8 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f5b9c67fa30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 129ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18.999996],\n",
              "       [38.99999 ],\n",
              "       [40.999985],\n",
              "       [48.999985],\n",
              "       [56.99998 ],\n",
              "       [68.99997 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    }
  ]
}