Clase01.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Clase01.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM9LiMHEki9xLz+HEwD4Drt",
      "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/estebanvz/alura/blob/clase01/Clase01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0f8v_g-gZLj4"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = keras.datasets.fashion_mnist\n",
        "(( imagenes_entrenamiento, etiquetas_entrenamiento ),(imagenes_test, etiquetas_test)) =dataset.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKU2rD32Zlb1",
        "outputId": "66e69c18-ecc1-4892-a9b7-fedcf0c72b6e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(imagenes_entrenamiento[55])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "x6if9qBvaIeV",
        "outputId": "fb417618-b718-4348-d27b-266ff903fcd1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f25709a4dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUQklEQVR4nO3df2yd5XUH8O+5v/zbJiaOMSEk5Me0UaoG5IYfZYyWEVHYFJhW1KAyNiFcVQW1WzuN0kqwbp3QtLbqHxVSWqIma6GrBJRMjVpY2okxWIqhGSRkkBAc4mA7ASe2Y8c/7r1nf/iCTPBzHnPf+9734uf7kSI79/i59/i1j99773mf5xFVBREtfqmkEyCi6mCxEwWCxU4UCBY7USBY7ESByFTzwXJSp/VoquZDLgqFdvuY5ZvdHRWZEXNsZtJ+bCna3Zpi1r7/fKORW8a+79SYfS7KHB834yGaxDimdWreH0qkYheR6wB8D0AawA9V9X7r6+vRhEvlmigPGaSRGy4z40NXFJ2x+kH7R7zkFfdYAMidKpjx8U77/t/6uHt89qwpc2zT0/YfuWXff8aMRyL2HzHUaMt6t+5yxsp+Gi8iaQDfB/BpABcC2CwiF5Z7f0QUryiv2TcAOKiqh1R1GsBPAWyqTFpEVGlRin05gCNz/t9fuu09RKRHRHpFpHcG9tM2IopP7O/Gq+oWVe1W1e4s6uJ+OCJyiFLsRwGsmPP/80q3EVENilLszwFYJyIXiEgOwGcB7KhMWkRUaWW33lQ1LyJ3AvgVZltvW1V1X8Uyq7QYWymplhYz/uo3P2LG79r4SzN+cOJ3ZrwjN+aM3b7kt+bYneO/Z8a3vn6FGX/io9vN+KG8+1fskZPd5tgbLt1jxif/JmvGe3bc4Yyt/ev/McfWamstikh9dlXdCWBnhXIhohjxclmiQLDYiQLBYicKBIudKBAsdqJAsNiJAiHVXF22Vdo1timuMU9J7PvHy52x8y/vN8ee13TSjB+fbDbjQ6fsPv6JEfdU0FTKnsJ6wbK3zfia1rfM+GujS8348XF3bmc3TZhjl9TZ8abMtBlf1eD+3vonl5hjX/kn+9qIhsft6xeSslt3YVSH5y0GntmJAsFiJwoEi50oECx2okCw2IkCwWInCkRVl5KOVcTW2sm/cLfWAODjn9rvjPWNtptj+8bseFPWbiGlPe2z9ecfccYOj9iPPTJVb8anCvavSF7t88XFy9zrmQycbjXH1qdnzHhX3YgZf+7ESmdsVdOwOfaqv3/Wvu9/z5lxFO1VeZPAMztRIFjsRIFgsRMFgsVOFAgWO1EgWOxEgWCxEwVi8fTZI8p/xp7qOTDh7gkX1J5eq4W0GW/L2X30qRn7xzRddMfPajhtjj34xjIz3lZn7+m8sdN9/QEA/OLNi5wx33Hrahg149mU3ctuzbpzH83buxP9QdObZvzI1/7UjK/4Vow7zJaJZ3aiQLDYiQLBYicKBIudKBAsdqJAsNiJAsFiJwpEMH12ydrzj7s73XPCAeDgaIcz1pqbMseOTts93YynX+zrs+dSefdjF+z56ijYve4Tkw1m/NXxTjM+ctr9+M319nFr8MxnL3jm0p+Vc19j0JCy1xAYyTea8VV/3GfGC98yw4mIVOwi0gdgDEABQF5V7Q23iSgxlTizf1JV7Z0EiChxfM1OFIioxa4AnhCR50WkZ74vEJEeEekVkd4Z2K/RiCg+UZ/GX6mqR0VkGYAnReT/VPWpuV+gqlsAbAFm93qL+HhEVKZIZ3ZVPVr6eAzAYwA2VCIpIqq8sotdRJpEpOWdzwFsBLC3UokRUWVFeRrfCeAxmd0qOQPgIVX9ZUWyikF6+Tlm/NCY/QqjUHT/XexqtuddD4zZWy4fmjnbjGczdh9+cNw9177VMx8912L3mwtFuw/fP36WZ7z7uDVm7T76jKePXvTMhz+3zr1V9kjevn7ghKfPfnNXrxl/GOea8SSUXeyqegjAxyqYCxHFiK03okCw2IkCwWInCgSLnSgQLHaiQAQzxXVw43Iz3iFvmPEZuJeDbs+Nm2MnJuwprimx236ZrN16G59yT99d1jhmjs167ntktMmMT+c9Wzrnyz+f5Iv2Etw+WXF/b81p+9LtoWl7O+mP1vWb8Qf/7CYz3vjobjMeB57ZiQLBYicKBIudKBAsdqJAsNiJAsFiJwoEi50oEMH02Ycvs6dTrki7l2MG7GWLWzP2NFJfHz2Vtrdsbm20778p556mWu/5voqeKaziy913jUDG/b2lYI/1LSXt05x2H7dGz1LS/ZNLzPgzE+vM+OAG+zy6+lEzHAue2YkCwWInCgSLnSgQLHaiQLDYiQLBYicKBIudKBDB9Nm/dvlOM/7syBozXoS7H31yxl52+NyzR8z4iQl7WeOWOnvudZ3RS19Wb89nz3iWqU6l7F54i2fbZWuufUvOvn6gNePecnkhOjLuJb5fn1oW6bFX1w3ZD35+tNzjwDM7USBY7ESBYLETBYLFThQIFjtRIFjsRIFgsRMFIpg++0NfvcGMz9z1thm/rKPPGZsqZs2xUwV7/XO7k+1nbV08XbR/xL4+edozX73g2TbZys235XIh4rloON/sjE16fmbXtu0z41998c/N+Opb9pjxJHiPpohsFZFjIrJ3zm3tIvKkiBwofbRn+hNR4hbyp/NHAK4747a7AexS1XUAdpX+T0Q1zFvsqvoUgOEzbt4EYFvp820AbqxwXkRUYeW+Zu9U1YHS54MAOl1fKCI9AHoAoB72NeREFJ/I78arqsJ4j0lVt6hqt6p2Z2FvcEhE8Sm32IdEpAsASh+PVS4lIopDucW+A8Btpc9vA/B4ZdIhorh4X7OLyMMArgawVET6AdwL4H4APxOR2wEcBnBznElWQt0vnvPE7fH7fu3e3/2Wc+29tp8dXGnGMyl73fiJGbsnnJbyL5cYGLS7ppl6e+32CzrOfO/2vSaM+ey+79vH16evT7lzv7fjZXPsml//lRlf+7nfmfFa5P0tUdXNjtA1Fc6FiGLEy2WJAsFiJwoEi50oECx2okCw2IkCEcwU16j0U0edsW8+9Cfm2OvW7Tfjvtacb5pp2mhhnS7YbTtfa+2cJfZS1HljK2vA3vLZt91zvmjfd3PGnp7bknIvVX3p3V8wx67d/qwZ/zDimZ0oECx2okCw2IkCwWInCgSLnSgQLHaiQLDYiQLBPnsFrPEsG9z3n11mvCHr3nIZ8PejrfjYjL06UDpt37dvGumMZ5nsTNp9DUCLp09+qmDn/rGmN8z4Nx76nDN2/vZnzLGLEc/sRIFgsRMFgsVOFAgWO1EgWOxEgWCxEwWCxU4UiHD67GL3i6FRN05266g/ZcZHp+rNuDUnHACyqYIzli/affDONnu++ll1p8348dNNZtzSkJ4248PT9n1PFO0+/MqrDjtj8f20axfP7ESBYLETBYLFThQIFjtRIFjsRIFgsRMFgsVOFIhw+uy+PnrK7kej6O5lS9a9LTEALM3ZffYD2mHGU5455ZbWnN0nH8/bub/l6aP75rs35dy99KJnzfnWrHvddwAYmmkz43ec91/O2BasNscuRt4zu4hsFZFjIrJ3zm33ichREdlT+nd9vGkSUVQLeRr/IwDXzXP7d1V1fenfzsqmRUSV5i12VX0KwHAVciGiGEV5g+5OEXmx9DR/ieuLRKRHRHpFpHcG9ppjRBSfcov9AQBrAKwHMADg264vVNUtqtqtqt1Z2BMXiCg+ZRW7qg6pakFViwB+AGBDZdMiokorq9hFZO7ayDcB2Ov6WiKqDd4+u4g8DOBqAEtFpB/AvQCuFpH1mJ0W3Afg8zHmWBWSsvvF6l7+PDLfuvDW/usAUDD61Q1pe//1lGdm98S0vb97nWfN+7Prx92PLZ6D6unD+9aVPzy91BnLrDjPHJs/0m/Gk1wfoVzeYlfVzfPc/GAMuRBRjHi5LFEgWOxEgWCxEwWCxU4UCBY7USDCmeLqoQX3FFaf9DJ3iwcAlmYPlH3fgL1UdFTZtH3f6pnCmva0DVuy5V8infF831NF+9e3MzvijL39R3brre3Hvtab5zyp8f3MysUzO1EgWOxEgWCxEwWCxU4UCBY7USBY7ESBYLETBYJ99gqYWneOGf/vYXsqp2+Kqy9u8fXBM55ppinP9NqmrL3tcsHo0+fVXr67Qez7zqs9/Xay6I4PfdKemtv2YzNsLi1eq3hmJwoEi50oECx2okCw2IkCwWInCgSLnSgQLHaiQCyePnvUpX0jzE8+uc5e0rg+wpbLC5ExeuG5lN1P9smm7T57EeXPd/ctYz3j6cP75rMPF9zbTW++5Lfm2OcX4Xlw8X1HRDQvFjtRIFjsRIFgsRMFgsVOFAgWO1EgWOxEgVg8ffaIW+RG2bL59FJ7rL/XbffpGzP2vO4ofNsmN+Xsx65L299bUzq+3H3r6Y/kG52xG9r2mGOfxyVl5fSuGtzS2XtmF5EVIvIbEXlZRPaJyJdKt7eLyJMicqD0cUn86RJRuRbyND4P4CuqeiGAywB8UUQuBHA3gF2qug7ArtL/iahGeYtdVQdU9YXS52MA9gNYDmATgG2lL9sG4Ma4kiSi6D7Qa3YRWQXgYgC7AXSq6kApNAig0zGmB0APANTD/RqKiOK14HfjRaQZwCMAvqyqo3NjqqrA/LMaVHWLqnaranfW80YUEcVnQcUuIlnMFvpPVPXR0s1DItJVincBOBZPikRUCd6n8SIiAB4EsF9VvzMntAPAbQDuL318PJYMq0Tz5U8FnbrotBkvqv031de+8rGmuPr4cmvIzJhxX1vR2na56Jn6m5VoyzWfKrifSe6ZXGmOTa+9wIwXDr5eVk5JWshr9k8AuBXASyLyTnPyHswW+c9E5HYAhwHcHE+KRFQJ3mJX1acB5woF11Q2HSKKCy+XJQoEi50oECx2okCw2IkCwWInCsTimeLqk7KXJY6yBe8Vq18z4wMTbWa8LWf36XOeqZx5o1c+PG1fonxyqsGMN3q2ZK73XCMw7Vnu2eLrszek7WsAxvL1ztg5mRFz7OC19jbcHb4+e4SlyePCMztRIFjsRIFgsRMFgsVOFAgWO1EgWOxEgWCxEwUimD57Kpc148VJu+9Z/MOLnbHR6TfNsfmi/TfV10f3mcjnnLFjEy3mWN+c8ubslBmfLNi/QqeM3DrrxsyxpwvusQBQgH3crC2hB/P2tQ/LPvOGGdcHzHCk6zbiwjM7USBY7ESBYLETBYLFThQIFjtRIFjsRIFgsRMFIpg+e3Hanvvsc+Qu97ztFUYvGQBacnav2pqPDvj78O11487Y2pbj5tjldSfM+OCU3Y+eKtrXLzQYWzZPRZjrDgB1njXrG1Pux54o2j8z9Vx/8GHEMztRIFjsRIFgsRMFgsVOFAgWO1EgWOxEgWCxEwViIfuzrwCwHUAnAAWwRVW/JyL3AbgDwDuN3HtUdWdciSYt9ZJ7Xvgdt+4wxx6ctNcgT4u9v3pdyr5GwNpj3bf2emPKvgZgZctbZnw432zGC8b5JA37+06Jez46ANSLZ01747h9JDdojv3hzzea8VXoN+O1aCFXNeQBfEVVXxCRFgDPi8iTpdh3VfVf4kuPiCplIfuzDwAYKH0+JiL7ASyPOzEiqqwP9JpdRFYBuBjA7tJNd4rIiyKyVUSWOMb0iEiviPTOwH7KSETxWXCxi0gzgEcAfFlVRwE8AGANgPWYPfN/e75xqrpFVbtVtTuLugqkTETlWFCxi0gWs4X+E1V9FABUdUhVC6paBPADABviS5OIovIWu4gIgAcB7FfV78y5vWvOl90EYG/l0yOiSlnIu/GfAHArgJdEZE/ptnsAbBaR9Zhtx/UB+HwsGVaK2m0enxX/8Iwz9vXmW8yxX7jhV2b8koY+Mz6t9nbTv59zT1M97lmOOW0st7wQ4xl7imx7atIZa0rZP5M2zzbbadjTUE8W3VNgr/63vzXHrvnGs2b8w2gh78Y/Dcx7VBdtT51oMeIVdESBYLETBYLFThQIFjtRIFjsRIFgsRMFQlSj9Vk/iFZp10vlmqo93nuIZ2ngKh6HM43ecpkZH77Qzn3mfPecg7a2CXPsdN7uZfuWVPbFi0V3PP9mozm29TX7XHTuzw+b8Xz/UTO+GO3WXRjV4XkPOs/sRIFgsRMFgsVOFAgWO1EgWOxEgWCxEwWCxU4UiKr22UXkOIC5zdGlAOy1ipNTq7nVal4AcytXJXNbqaod8wWqWuzve3CRXlXtTiwBQ63mVqt5AcytXNXKjU/jiQLBYicKRNLFviXhx7fUam61mhfA3MpVldwSfc1ORNWT9JmdiKqExU4UiESKXUSuE5FXROSgiNydRA4uItInIi+JyB4R6U04l60ickxE9s65rV1EnhSRA6WP8+6xl1Bu94nI0dKx2yMi1yeU2woR+Y2IvCwi+0TkS6XbEz12Rl5VOW5Vf80uImkArwK4FkA/gOcAbFbVl6uaiIOI9AHoVtXEL8AQkasAnAKwXVUvKt32zwCGVfX+0h/KJar6dzWS230ATiW9jXdpt6KuuduMA7gRwF8iwWNn5HUzqnDckjizbwBwUFUPqeo0gJ8C2JRAHjVPVZ8CMHzGzZsAbCt9vg2zvyxV58itJqjqgKq+UPp8DMA724wneuyMvKoiiWJfDuDInP/3o7b2e1cAT4jI8yLSk3Qy8+hU1YHS54MAOpNMZh7ebbyr6Yxtxmvm2JWz/XlUfIPu/a5U1UsAfBrAF0tPV2uSzr4Gq6Xe6YK28a6WebYZf1eSx67c7c+jSqLYjwJYMef/55VuqwmqerT08RiAx1B7W1EPvbODbunjsYTzeVctbeM93zbjqIFjl+T250kU+3MA1onIBSKSA/BZADsSyON9RKSp9MYJRKQJwEbU3lbUOwDcVvr8NgCPJ5jLe9TKNt6ubcaR8LFLfPtzVa36PwDXY/Yd+dcAfD2JHBx5rQbwv6V/+5LODcDDmH1aN4PZ9zZuB3A2gF0ADgD4DwDtNZTbvwJ4CcCLmC2sroRyuxKzT9FfBLCn9O/6pI+dkVdVjhsvlyUKBN+gIwoEi50oECx2okCw2IkCwWInCgSLnSgQLHaiQPw/LzQlUXXZC0sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imagenes_entrenamiento.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbFdc__lvEBe",
        "outputId": "c01f9737-cfee-4933-c393-05fda0159ff7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mostrar_estructura(imagenes,etiquetas,titulo):\n",
        "  print(\"--> \",titulo)\n",
        "  print(\n",
        "      \"Cantidad de imágenes: {} \\nCantidad de etiquetas: {} \\nEstructura de las imágenes: {}\\nEtiquetas: {}\"\n",
        "      .format(imagenes.shape[0],etiquetas.shape[0],imagenes.shape[1:],list(set(etiquetas)))\n",
        "  )"
      ],
      "metadata": {
        "id": "p53O6WBmueax"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mostrar_estructura(imagenes_entrenamiento,etiquetas_entrenamiento,\"Dataset de entrenamiento\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_xVUJ-AvZLF",
        "outputId": "17a0ec46-4e28-45fb-f292-6b3509fe4fc2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-->  Dataset de entrenamiento\n",
            "Cantidad de imágenes: 60000 \n",
            "Cantidad de etiquetas: 60000 \n",
            "Estructura de las imágenes: (28, 28)\n",
            "Etiquetas: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mostrar_estructura(imagenes_test,etiquetas_test,\"Dataset de test\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkRx_TFEvgZZ",
        "outputId": "2cb20c9c-da35-4724-cf21-c414fedfe021"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-->  Dataset de test\n",
            "Cantidad de imágenes: 10000 \n",
            "Cantidad de etiquetas: 10000 \n",
            "Estructura de las imágenes: (28, 28)\n",
            "Etiquetas: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VHt2xTDovp81"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}