cel_fah.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WfTCHEkS2QG-"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "Nsb1L5sS5Hi3"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#capa = tf.keras.layers.Dense(units=1, input_shape=[1])\n",
        "#modelo = tf.keras.Sequential([capa])\n",
        "\n",
        "oculta1 = tf.keras.layers.Dense(units=3, input_shape=[1])\n",
        "oculta2 = tf.keras.layers.Dense(units=3)\n",
        "salida = tf.keras.layers.Dense(units=1)\n",
        "modelo = tf.keras.Sequential([oculta1, oculta2, salida])"
      ],
      "metadata": {
        "id": "j38c6gPp6Xze"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(0.1),\n",
        "    loss='mean_squared_error'\n",
        ")"
      ],
      "metadata": {
        "id": "RezMrKNj7ZIa"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Comenzando el entrenamiento...\")\n",
        "historial = modelo.fit(celsius, fahrenheit, epochs=1000, verbose=False)\n",
        "print(\"Modelo entrenado!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6800WOQO8qVT",
        "outputId": "da4557d1-8d44-4f8f-ad3a-b3d48398d84d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Comenzando el entrenamiento...\n",
            "Modelo entrenado!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.xlabel(\"# Epoca\")\n",
        "plt.ylabel(\"Magnitud de pérdida\")\n",
        "plt.plot(historial.history[\"loss\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Ir5BsOhd9o8X",
        "outputId": "617e172e-f9df-4184-e47f-f3d2afdf0b57"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1aefe47f50>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": [
        "print(\"Hagamos una predicción!\")\n",
        "resultado = modelo.predict([100.0])\n",
        "print(\"El resultado es\" + str(resultado) + \"fahrenheit!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07LdAre8-bR4",
        "outputId": "83f0a4a0-952c-45b3-a4c2-bde0d5b29a95"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hagamos una predicción!\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "El resultado es[[211.74744]]fahrenheit!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Variables internas del modelo\")\n",
        "#print(capa.get_weights())\n",
        "print(oculta1.get_weights())\n",
        "print(oculta2.get_weights())\n",
        "print(salida.get_weights())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zc2AtbmY_rHP",
        "outputId": "79d5ebb0-e3a6-4a31-ecbc-8a966d3d8bc2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variables internas del modelo\n",
            "[array([[-0.90710896,  0.25380236,  0.27244934]], dtype=float32), array([-4.417995 ,  3.960114 , -3.2440999], dtype=float32)]\n",
            "[array([[-1.3534427 ,  0.45331302, -1.0896633 ],\n",
            "       [ 0.8420482 , -0.44220173,  0.04574053],\n",
            "       [-0.9709154 , -0.20599727, -1.2183053 ]], dtype=float32), array([ 4.4648247, -1.7370847,  4.445373 ], dtype=float32)]\n",
            "[array([[ 0.95248765],\n",
            "       [-0.25747895],\n",
            "       [ 0.7898716 ]], dtype=float32), array([4.0077147], dtype=float32)]\n"
          ]
        }
      ]
    }
  ]
}