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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DimaGutierrez/Red-Neuronal-TensorFlow/blob/main/class_img.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7dG1ux0BPRiE"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos, metadatos = tfds.load('fashion_mnist', as_supervised=True, with_info=True)"
      ],
      "metadata": {
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        "id": "UcanGphEPtAo",
        "outputId": "c9ec78d9-16c5-45ff-82e6-60469249922e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to ~/tensorflow_datasets/fashion_mnist/3.0.1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "320008125bf4437083498bbabe10437e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "141bfcaab119474f840f0f6fd6890841"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c1071d84749496f89c4fbad2b3a1f29"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42d6a45a592a4961816f9ec635e77fa3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/60000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/fashion_mnist/3.0.1.incompleteKCSW7R/fashion_mnist-train.tfrecord*...:   0%|  …"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/fashion_mnist/3.0.1.incompleteKCSW7R/fashion_mnist-test.tfrecord*...:   0%|   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset fashion_mnist downloaded and prepared to ~/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metadatos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6RyrCRJQSQ2",
        "outputId": "5463bdb9-9140-4043-9f58-3dc2e5b722ed"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='fashion_mnist',\n",
              "    full_name='fashion_mnist/3.0.1',\n",
              "    description=\"\"\"\n",
              "    Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.\n",
              "    \"\"\",\n",
              "    homepage='https://github.com/zalandoresearch/fashion-mnist',\n",
              "    data_path='~/tensorflow_datasets/fashion_mnist/3.0.1',\n",
              "    file_format=tfrecord,\n",
              "    download_size=29.45 MiB,\n",
              "    dataset_size=36.42 MiB,\n",
              "    features=FeaturesDict({\n",
              "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
              "    }),\n",
              "    supervised_keys=('image', 'label'),\n",
              "    disable_shuffling=False,\n",
              "    splits={\n",
              "        'test': <SplitInfo num_examples=10000, num_shards=1>,\n",
              "        'train': <SplitInfo num_examples=60000, num_shards=1>,\n",
              "    },\n",
              "    citation=\"\"\"@article{DBLP:journals/corr/abs-1708-07747,\n",
              "      author    = {Han Xiao and\n",
              "                   Kashif Rasul and\n",
              "                   Roland Vollgraf},\n",
              "      title     = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning\n",
              "                   Algorithms},\n",
              "      journal   = {CoRR},\n",
              "      volume    = {abs/1708.07747},\n",
              "      year      = {2017},\n",
              "      url       = {http://arxiv.org/abs/1708.07747},\n",
              "      archivePrefix = {arXiv},\n",
              "      eprint    = {1708.07747},\n",
              "      timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},\n",
              "      biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},\n",
              "      bibsource = {dblp computer science bibliography, https://dblp.org}\n",
              "    }\"\"\",\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_entrenamiento, datos_pruebas = datos['train'], datos['test']"
      ],
      "metadata": {
        "id": "pZhE3feaQc8-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nombres_clases = metadatos.features['label'].names"
      ],
      "metadata": {
        "id": "aXBtP6YkRql9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nombres_clases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tywm98elR2MR",
        "outputId": "8c2b5cd8-1a8a-4e90-83c2-67c943c83e51"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalizar los datos (Pasar de 0-255 a 0-1)\n",
        "\n",
        "def normalizar(imagenes, etiquetas):\n",
        "  imagenes = tf.cast(imagenes, tf.float32)\n",
        "  imagenes/= 255 #Aqui lo pasa de 0-255 a 0-1\n",
        "  return imagenes, etiquetas\n",
        "\n",
        "#Normalizar los datos de entrenamiento y pruebas con la cuncion que hicimos\n",
        "datos_entrenamiento = datos_entrenamiento.map(normalizar)\n",
        "datos_pruebas = datos_pruebas.map(normalizar)\n",
        "\n",
        "#Agregar a cache (usar memoria en lugar de disco, entrenamiento mas rapido)\n",
        "datos_entrenamiento = datos_entrenamiento.cache()\n",
        "datos_pruebas = datos_pruebas.cache()\n"
      ],
      "metadata": {
        "id": "Dgcsuf0ISWHG"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostrar una imagen de los datos de pruebas, de momento mostremos la primera\n",
        "for imagen, etiqueta in datos_entrenamiento.take(1):\n",
        "  break\n",
        "imagen = imagen.numpy().reshape((28,28)) #Redimencionar, cosas de tensores\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Dibujar dibujar\n",
        "plt.figure()\n",
        "plt.imshow(imagen, cmap=plt.cm.binary)\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "LzNL342SXlCu",
        "outputId": "98b4d9c4-9b74-46da-c5c3-321dc6383600"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i, (imagen, etiqueta) in enumerate(datos_entrenamiento.take(25)):\n",
        "  imagen = imagen.numpy().reshape((28,28))\n",
        "  plt.subplot(5,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(imagen, cmap=plt.cm.binary)\n",
        "  plt.xlabel(nombres_clases[etiqueta])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "wKFP_srtZDTW",
        "outputId": "f1fb9279-4e27-436c-8e87-faf1d902da7e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Crear el modelo\n",
        "\n",
        "modelo = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28,1)), #1 - blanco y negro\n",
        "    tf.keras.layers.Dense(50, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(50, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax) #para redes de clasificacion                             \n",
        "])"
      ],
      "metadata": {
        "id": "SOTTMi6Tarse"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Compilar el modelo\n",
        "\n",
        "modelo.compile(\n",
        "    optimizer='adam',\n",
        "    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "H3ROzdzLb4yK"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_ej_entrenamiento = metadatos.splits[\"train\"].num_examples\n",
        "num_ej_pruebas = metadatos.splits[\"test\"].num_examples"
      ],
      "metadata": {
        "id": "Q_DCeYTZcpId"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(num_ej_entrenamiento)\n",
        "print(num_ej_pruebas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xyg-_8hPcevF",
        "outputId": "eefb8bb8-4a66-4969-9e48-778055f61adc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n",
            "10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TAMAÑO_LOTE = 32\n",
        "\n",
        "datos_entrenamiento = datos_entrenamiento.repeat().shuffle(num_ej_entrenamiento).batch(TAMAÑO_LOTE)\n",
        "datos_pruebas = datos_pruebas.batch(TAMAÑO_LOTE)"
      ],
      "metadata": {
        "id": "ehMf_flIdpFG"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "#Entrenar\n",
        "historial = modelo.fit(datos_entrenamiento, epochs=5, steps_per_epoch= math.ceil(num_ej_entrenamiento/TAMAÑO_LOTE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhEpxA7BeVDg",
        "outputId": "9c340397-249e-412b-a126-e175fc4d78f9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 12s 3ms/step - loss: 0.5196 - accuracy: 0.8166\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3895 - accuracy: 0.8605\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3426 - accuracy: 0.8752\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3264 - accuracy: 0.8805\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3098 - accuracy: 0.8864\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel(\"# Epoca\")\n",
        "plt.ylabel(\"Magnitud de perdida\")\n",
        "plt.plot(historial.history[\"loss\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "nfYKJBA3gKHH",
        "outputId": "c4d017ed-9af2-4be2-de29-25d8ce0cdd99"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc4fb985ad0>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": [
        "import numpy as np\n",
        "\n",
        "for imagenes_prueba, etiquetas_prueba in datos_pruebas.take(1):\n",
        "  imagenes_prueba = imagenes_prueba.numpy()\n",
        "  etiquetas_prueba = etiquetas_prueba.numpy()\n",
        "  predicciones = modelo.predict(imagenes_prueba)\n",
        "\n",
        "def graficar_imagen(i, arr_predicciones, etiquetas_reales, imagenes):\n",
        "  arr_predicciones, etiqueta_real, img = arr_predicciones[i], etiquetas_reales[i], imagenes[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img[...,0], cmap=plt.cm.binary)\n",
        "\n",
        "  etiqueta_prediccion = np.argmax(arr_predicciones)\n",
        "  if etiqueta_prediccion == etiqueta_real:\n",
        "    color = 'blue' #si le atino...\n",
        "  else:\n",
        "    color = 'red' #Oop, no le atino...\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(\n",
        "      nombres_clases[etiqueta_prediccion],\n",
        "      100*np.max(arr_predicciones),\n",
        "      nombres_clases[etiqueta_real],\n",
        "      color=color\n",
        "  ))\n",
        "\n",
        "def graficar_valor_arreglo(i, arr_predicciones, etiqueta_real):\n",
        "  arr_predicciones, etiqueta_real = arr_predicciones[i], etiqueta_real[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  grafica = plt.bar(range(10), arr_predicciones, color=\"#777777\")\n",
        "  plt.ylim([0,1])\n",
        "  etiqueta_prediccion = np.argmax(arr_predicciones)\n",
        "\n",
        "  grafica[etiqueta_prediccion].set_color('red')\n",
        "  grafica[etiqueta_real].set_color('blue')\n",
        "\n",
        "filas = 5\n",
        "columnas = 5\n",
        "num_imagenes = filas*columnas\n",
        "plt.figure(figsize=(2*2*columnas, 2*filas))\n",
        "for i in range(num_imagenes):\n",
        "  plt.subplot(filas, 2*columnas, 2*i+1)\n",
        "  graficar_imagen(i, predicciones, etiquetas_prueba, imagenes_prueba)\n",
        "  plt.subplot(filas, 2*columnas, 2*i+2)\n",
        "  graficar_valor_arreglo(i, predicciones, etiquetas_prueba)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "fQZok9GngsaQ",
        "outputId": "1f904550-95aa-410b-a2c1-a848b591527e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 47ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 50 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tomar cualquier indice del ser de pruebas para ver su prediccion\n",
        "imagen = imagenes_prueba[5]\n",
        "imagen = np.array([imagen])\n",
        "prediccion = modelo.predict(imagen)\n",
        "\n",
        "print(\"Prediccion: \" + nombres_clases[np.argmax(prediccion[0])])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueyYhGXPpOLp",
        "outputId": "889a70af-cd5f-4fee-af1c-f92ebb03deea"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 63ms/step\n",
            "Prediccion: Trouser\n"
          ]
        }
      ]
    }
  ]
}