Tarea_clase_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tarea_clase_1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RjxrBy0xbk4",
        "outputId": "01b8289d-1e52-46b8-a039-66d4723643e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from keras.datasets import mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "digit = train_images[9]\n",
        "\n",
        "plt.imshow(digit, cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "WnSKUVr1yoj6",
        "outputId": "7b139332-16f2-4577-9e28-7b1fa495d555"
      },
      "execution_count": 3,
      "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": "markdown",
      "source": [
        "Imagen 28x28"
      ],
      "metadata": {
        "id": "F__7zIiR0AyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_slice = train_images[10:100, 0:28, 0:28]\n",
        "my_slice.shape\n",
        "\n",
        "plt.imshow(my_slice[30], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V364yDDlzIjr",
        "outputId": "cc6e6cd2-740a-4391-cd7d-c256eae224e4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esquina inferior (imagen 14x14)"
      ],
      "metadata": {
        "id": "TWTxRXoN0Gri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_slice = train_images[10:100, -15:-1, -15:-1]\n",
        "my_slice.shape\n",
        "plt.imshow(my_slice[30], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "VAuFPBNRzt9_",
        "outputId": "3fd60e63-c490-4436-9965-d7959f30048e"
      },
      "execution_count": 8,
      "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": "markdown",
      "source": [
        "maximo valor de un vector"
      ],
      "metadata": {
        "id": "TM6k1igI0lr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def max_valor(v):\n",
        "  return np.max(v)\n",
        "\n",
        "max_valor (np.array([3, 5 ,10, 300]))\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hoX2zah0kq_",
        "outputId": "1b575ef7-747e-4402-fc0f-bb36db30b4af"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "300"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}