tensorflow_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TimothyElems/temp-converter/blob/main/tensorflow_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eksoL38_HL1k"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ytw2ybT3MdD1"
      },
      "outputs": [],
      "source": [
        "celsius = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)\n",
        "fahrenheit = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A2Wo8Q4tOUQ2"
      },
      "outputs": [],
      "source": [
        "layer = tf.keras.layers.Dense(units=1, input_shape=[1])\n",
        "model = tf.keras.Sequential([layer])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fx2R8FmLOX_C"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer = tf.keras.optimizers.Adam(0.1),\n",
        "    loss = 'mean_squared_error'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FWLQ4frJOrc-"
      },
      "outputs": [],
      "source": [
        "results = model.fit(celsius, fahrenheit, epochs=1600, verbose=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "92UNLc0POuKv",
        "outputId": "bc7a46dd-5eb7-43e9-d25b-4f9c8284833f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b1fb67b50c0>]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.xlabel('# epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.plot(results.history['loss'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "izx7r0CEPhxz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "179d3e8c-3af0-4e25-c37b-ab0560a1c599"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 118ms/step\n",
            "The result is [[211.74275]]fahrenheit\n"
          ]
        }
      ],
      "source": [
        "rez = model.predict([100])\n",
        "\n",
        "print(\"The result is \" +str(rez)+ \"fahrenheit\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ha-R-jdYRUyA"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOqysVzYs7g4n6Ddq/zpKpB",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}