diff --git a/README.md b/README.md
index c9ec859..2529487 100644
--- a/README.md
+++ b/README.md
@@ -7,3 +7,6 @@
     - 👀 Programación. 
         
 Aquí se encuentra todo el código explicado en el canal de YouTube Código Máquina https://www.youtube.com/c/CodigoMaquina/
+
+
+Mi primera contribución desde VSCode
\ No newline at end of file
diff --git a/multi_class_mlp.ipynb b/multi_class_mlp.ipynb
new file mode 100644
index 0000000..a5d75e1
--- /dev/null
+++ b/multi_class_mlp.ipynb
@@ -0,0 +1,846 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMZT3rZRBziHHQ8R2Kxmish",
+      "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/AbimaelDD/code/blob/main/multi_class_mlp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "ZCiIG-nq9JTQ"
+      },
+      "outputs": [],
+      "source": [
+        "from keras import layers, models\n",
+        "from keras.datasets import reuters\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(train_data, train_labels), (test_data, test_labels) =reuters.load_data(num_words=10000)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nl6nKmu_9eBM",
+        "outputId": "14423f5e-4885-4afc-b08c-8e4bd5764d6e"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters.npz\n",
+            "\u001b[1m2110848/2110848\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EMyFrboA9xPK",
+        "outputId": "960fe2ff-7bca-4d13-9b58-d2f7c81e27d8"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[1,\n",
+              " 2,\n",
+              " 2,\n",
+              " 8,\n",
+              " 43,\n",
+              " 10,\n",
+              " 447,\n",
+              " 5,\n",
+              " 25,\n",
+              " 207,\n",
+              " 270,\n",
+              " 5,\n",
+              " 3095,\n",
+              " 111,\n",
+              " 16,\n",
+              " 369,\n",
+              " 186,\n",
+              " 90,\n",
+              " 67,\n",
+              " 7,\n",
+              " 89,\n",
+              " 5,\n",
+              " 19,\n",
+              " 102,\n",
+              " 6,\n",
+              " 19,\n",
+              " 124,\n",
+              " 15,\n",
+              " 90,\n",
+              " 67,\n",
+              " 84,\n",
+              " 22,\n",
+              " 482,\n",
+              " 26,\n",
+              " 7,\n",
+              " 48,\n",
+              " 4,\n",
+              " 49,\n",
+              " 8,\n",
+              " 864,\n",
+              " 39,\n",
+              " 209,\n",
+              " 154,\n",
+              " 6,\n",
+              " 151,\n",
+              " 6,\n",
+              " 83,\n",
+              " 11,\n",
+              " 15,\n",
+              " 22,\n",
+              " 155,\n",
+              " 11,\n",
+              " 15,\n",
+              " 7,\n",
+              " 48,\n",
+              " 9,\n",
+              " 4579,\n",
+              " 1005,\n",
+              " 504,\n",
+              " 6,\n",
+              " 258,\n",
+              " 6,\n",
+              " 272,\n",
+              " 11,\n",
+              " 15,\n",
+              " 22,\n",
+              " 134,\n",
+              " 44,\n",
+              " 11,\n",
+              " 15,\n",
+              " 16,\n",
+              " 8,\n",
+              " 197,\n",
+              " 1245,\n",
+              " 90,\n",
+              " 67,\n",
+              " 52,\n",
+              " 29,\n",
+              " 209,\n",
+              " 30,\n",
+              " 32,\n",
+              " 132,\n",
+              " 6,\n",
+              " 109,\n",
+              " 15,\n",
+              " 17,\n",
+              " 12]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "word_index = reuters.get_word_index()\n",
+        "word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
+        "\n",
+        "for _ in train_data[0]:\n",
+        "  print(word_index.get(_ - 3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pfj_vSIK91ZB",
+        "outputId": "7ef32fef-c9fd-46fe-f427-5869855408a2"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters_word_index.json\n",
+            "\u001b[1m550378/550378\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+            "None\n",
+            "None\n",
+            "None\n",
+            "said\n",
+            "as\n",
+            "a\n",
+            "result\n",
+            "of\n",
+            "its\n",
+            "december\n",
+            "acquisition\n",
+            "of\n",
+            "space\n",
+            "co\n",
+            "it\n",
+            "expects\n",
+            "earnings\n",
+            "per\n",
+            "share\n",
+            "in\n",
+            "1987\n",
+            "of\n",
+            "1\n",
+            "15\n",
+            "to\n",
+            "1\n",
+            "30\n",
+            "dlrs\n",
+            "per\n",
+            "share\n",
+            "up\n",
+            "from\n",
+            "70\n",
+            "cts\n",
+            "in\n",
+            "1986\n",
+            "the\n",
+            "company\n",
+            "said\n",
+            "pretax\n",
+            "net\n",
+            "should\n",
+            "rise\n",
+            "to\n",
+            "nine\n",
+            "to\n",
+            "10\n",
+            "mln\n",
+            "dlrs\n",
+            "from\n",
+            "six\n",
+            "mln\n",
+            "dlrs\n",
+            "in\n",
+            "1986\n",
+            "and\n",
+            "rental\n",
+            "operation\n",
+            "revenues\n",
+            "to\n",
+            "19\n",
+            "to\n",
+            "22\n",
+            "mln\n",
+            "dlrs\n",
+            "from\n",
+            "12\n",
+            "5\n",
+            "mln\n",
+            "dlrs\n",
+            "it\n",
+            "said\n",
+            "cash\n",
+            "flow\n",
+            "per\n",
+            "share\n",
+            "this\n",
+            "year\n",
+            "should\n",
+            "be\n",
+            "2\n",
+            "50\n",
+            "to\n",
+            "three\n",
+            "dlrs\n",
+            "reuter\n",
+            "3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_labels[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DvMbS_UD-uOm",
+        "outputId": "1e69b503-2615-4cce-acd3-e64977c9cb8c"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(3)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data.shape"
+      ],
+      "metadata": {
+        "id": "jb98sgfS-_QR",
+        "outputId": "79e1d569-35e8-4202-ee58-27a7eb5f8168",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8982,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "1Ob6xKSVe-Go"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def vectorizar(sequences, dimension=10000):\n",
+        "  results = np.zeros((len(sequences), dimension))\n",
+        "  for i, sequences in enumerate(sequences):\n",
+        "    results[i, sequences] = 1\n",
+        "  return results"
+      ],
+      "metadata": {
+        "id": "XbfhavtnebVq"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train = vectorizar(train_data)\n",
+        "x_test = vectorizar(test_data)\n",
+        "\n",
+        "from keras.utils import to_categorical\n",
+        "\n",
+        "y_train = to_categorical(train_labels)\n",
+        "y_test = to_categorical(test_labels)"
+      ],
+      "metadata": {
+        "id": "1SuEy0xNfEzP"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modelo = models.Sequential()\n",
+        "modelo.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\n",
+        "modelo.add(layers.Dense(64, activation='relu'))\n",
+        "modelo.add(layers.Dense(46, activation='softmax'))\n",
+        "\n",
+        "modelo.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Vwj6l7_cf28H",
+        "outputId": "34d6c3a8-2dcb-480f-ccc9-47c2e030137d"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modelo.summary()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        },
+        "id": "Qe3AnKfWgnk1",
+        "outputId": "1bff0afd-4140-44f4-e5cb-2143a8b8ede8"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │       \u001b[38;5;34m640,064\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m4,160\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m)             │         \u001b[38;5;34m2,990\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │       <span style=\"color: #00af00; text-decoration-color: #00af00\">640,064</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,990</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m647,214\u001b[0m (2.47 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">647,214</span> (2.47 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m647,214\u001b[0m (2.47 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">647,214</span> (2.47 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_val = x_train[:1000]\n",
+        "partial_x_train =x_train[1000:]\n",
+        "\n",
+        "y_val = y_train[:1000]\n",
+        "partial_y_train = y_train[1000:]"
+      ],
+      "metadata": {
+        "id": "epGcTAmggwiT"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = modelo.fit(partial_x_train, partial_y_train, epochs=30, batch_size=512, validation_data=(x_val,y_val))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UUDBGBGohEKQ",
+        "outputId": "cafe1bc5-7e34-4141-d246-cc6eaa0b1ce0"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 88ms/step - accuracy: 0.4139 - loss: 3.2158 - val_accuracy: 0.6360 - val_loss: 1.7625\n",
+            "Epoch 2/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 56ms/step - accuracy: 0.6803 - loss: 1.5786 - val_accuracy: 0.7110 - val_loss: 1.3325\n",
+            "Epoch 3/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 78ms/step - accuracy: 0.7412 - loss: 1.1909 - val_accuracy: 0.7570 - val_loss: 1.1770\n",
+            "Epoch 4/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 80ms/step - accuracy: 0.7954 - loss: 0.9588 - val_accuracy: 0.7510 - val_loss: 1.0952\n",
+            "Epoch 5/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 129ms/step - accuracy: 0.8275 - loss: 0.7910 - val_accuracy: 0.7910 - val_loss: 1.0127\n",
+            "Epoch 6/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 72ms/step - accuracy: 0.8530 - loss: 0.6989 - val_accuracy: 0.8050 - val_loss: 0.9597\n",
+            "Epoch 7/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 65ms/step - accuracy: 0.8745 - loss: 0.5780 - val_accuracy: 0.8080 - val_loss: 0.9336\n",
+            "Epoch 8/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - accuracy: 0.8958 - loss: 0.4934 - val_accuracy: 0.8130 - val_loss: 0.9036\n",
+            "Epoch 9/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - accuracy: 0.9123 - loss: 0.4085 - val_accuracy: 0.8260 - val_loss: 0.8922\n",
+            "Epoch 10/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 70ms/step - accuracy: 0.9261 - loss: 0.3511 - val_accuracy: 0.8100 - val_loss: 0.9012\n",
+            "Epoch 11/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 97ms/step - accuracy: 0.9370 - loss: 0.2942 - val_accuracy: 0.8240 - val_loss: 0.8833\n",
+            "Epoch 12/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 64ms/step - accuracy: 0.9435 - loss: 0.2566 - val_accuracy: 0.8200 - val_loss: 0.8807\n",
+            "Epoch 13/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - accuracy: 0.9518 - loss: 0.2159 - val_accuracy: 0.8230 - val_loss: 0.9052\n",
+            "Epoch 14/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - accuracy: 0.9472 - loss: 0.2083 - val_accuracy: 0.8220 - val_loss: 0.9385\n",
+            "Epoch 15/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - accuracy: 0.9511 - loss: 0.1950 - val_accuracy: 0.8150 - val_loss: 0.9180\n",
+            "Epoch 16/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - accuracy: 0.9556 - loss: 0.1655 - val_accuracy: 0.8040 - val_loss: 0.9455\n",
+            "Epoch 17/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - accuracy: 0.9574 - loss: 0.1520 - val_accuracy: 0.8140 - val_loss: 0.9434\n",
+            "Epoch 18/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - accuracy: 0.9598 - loss: 0.1471 - val_accuracy: 0.8180 - val_loss: 0.9341\n",
+            "Epoch 19/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 99ms/step - accuracy: 0.9606 - loss: 0.1307 - val_accuracy: 0.7930 - val_loss: 1.0453\n",
+            "Epoch 20/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 92ms/step - accuracy: 0.9615 - loss: 0.1270 - val_accuracy: 0.8200 - val_loss: 0.9471\n",
+            "Epoch 21/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 51ms/step - accuracy: 0.9617 - loss: 0.1171 - val_accuracy: 0.8040 - val_loss: 1.0499\n",
+            "Epoch 22/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - accuracy: 0.9650 - loss: 0.1141 - val_accuracy: 0.8090 - val_loss: 0.9949\n",
+            "Epoch 23/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - accuracy: 0.9644 - loss: 0.1052 - val_accuracy: 0.8170 - val_loss: 1.0123\n",
+            "Epoch 24/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - accuracy: 0.9626 - loss: 0.1090 - val_accuracy: 0.8180 - val_loss: 0.9936\n",
+            "Epoch 25/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - accuracy: 0.9596 - loss: 0.1088 - val_accuracy: 0.8110 - val_loss: 1.0222\n",
+            "Epoch 26/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 56ms/step - accuracy: 0.9609 - loss: 0.1014 - val_accuracy: 0.8170 - val_loss: 1.0287\n",
+            "Epoch 27/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - accuracy: 0.9636 - loss: 0.1022 - val_accuracy: 0.8160 - val_loss: 1.0009\n",
+            "Epoch 28/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - accuracy: 0.9612 - loss: 0.1018 - val_accuracy: 0.8180 - val_loss: 1.0479\n",
+            "Epoch 29/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - accuracy: 0.9638 - loss: 0.0966 - val_accuracy: 0.8110 - val_loss: 1.0437\n",
+            "Epoch 30/30\n",
+            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 96ms/step - accuracy: 0.9678 - loss: 0.0874 - val_accuracy: 0.8130 - val_loss: 1.0545\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history_dict =history.history\n",
+        "\n",
+        "loss_values = history_dict['loss']\n",
+        "val_loss_values = history_dict['val_loss']\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig = plt.figure(figsize=(10,10))\n",
+        "epoch = range(1, len(loss_values)+1)\n",
+        "\n",
+        "plt.plot(epoch, loss_values, '-', label='train')\n",
+        "plt.plot(epoch, val_loss_values, '--', label='validation')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 830
+        },
+        "id": "p1oyoHjqhm7z",
+        "outputId": "9c6442eb-15d7-437f-bba5-e8f0ffba9450"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history_dict =history.history\n",
+        "\n",
+        "loss_values = history_dict['accuracy']\n",
+        "val_loss_values = history_dict['val_accuracy']\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig = plt.figure(figsize=(10,10))\n",
+        "epoch = range(1, len(loss_values)+1)\n",
+        "\n",
+        "plt.plot(epoch, loss_values, '-', label='train')\n",
+        "plt.plot(epoch, val_loss_values, '--', label='validation')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 830
+        },
+        "id": "mGqgletHi04U",
+        "outputId": "81d73479-a286-45a6-cdcd-36cbe8b33412"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = modelo.predict(x_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WXQXk0TpjPZ8",
+        "outputId": "a49ff6a5-779e-48a1-8bd2-eeff53f05771"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kVQ7USuojV_G",
+        "outputId": "8ace9ba3-e82a-4420-c57c-188ffbe73038"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1.54481867e-08, 2.50683911e-07, 2.14742460e-12, 9.99249578e-01,\n",
+              "       5.24237752e-04, 3.02096281e-09, 9.40961961e-11, 1.54185447e-08,\n",
+              "       5.11149919e-05, 8.41553458e-07, 8.20134094e-09, 5.41775016e-08,\n",
+              "       4.24254907e-08, 5.94401506e-09, 1.13011644e-08, 5.86039572e-09,\n",
+              "       2.34656824e-07, 4.50194881e-10, 3.19954840e-09, 5.07157392e-06,\n",
+              "       1.60277370e-04, 2.58981981e-06, 1.12249550e-10, 1.59406035e-08,\n",
+              "       1.13176455e-08, 1.11469378e-09, 9.83475523e-10, 1.49766592e-07,\n",
+              "       8.54501607e-08, 2.93030553e-07, 1.84620404e-07, 3.90949850e-09,\n",
+              "       1.33108458e-08, 2.29903407e-09, 2.18185050e-07, 2.18967355e-09,\n",
+              "       4.61185391e-06, 7.41230688e-12, 9.88271895e-11, 1.52158307e-07,\n",
+              "       7.58466800e-09, 3.28452785e-08, 2.09018122e-10, 1.15828751e-08,\n",
+              "       2.91095828e-11, 5.68540354e-12], dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.sum(predictions[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DCT_2-bija7i",
+        "outputId": "02c39e02-dfb6-4aac-bb66-bd08794d9c3b"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float32(1.0000001)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.argmax(predictions[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lW6NEslZjfxK",
+        "outputId": "f8586eba-930d-4b8f-cf2d-e555b8cf0e1b"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(3)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "modelo.evaluate(x_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SYNkF_SCj0-5",
+        "outputId": "fa270dd0-304f-474a-89ab-652501cfd08f"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m281/281\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9142 - loss: 0.4238\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.18908558785915375, 0.9505677819252014]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Agregar tamaño de las capas ocultas, regularización y dropout"
+      ],
+      "metadata": {
+        "id": "FhWKWzrakGmP"
+      }
+    }
+  ]
+}
\ No newline at end of file