Upload modelo_y_entrenamiento.ipynb

modelo_y_entrenamiento.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "6B5SMiEcB4KF"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "from tensorflow.keras.datasets import fashion_mnist\n",
+        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
+        "from tensorflow.keras.preprocessing import image\n",
+        "from tensorflow.keras.models import Model\n",
+        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L7gOA-_llaXt",
+        "outputId": "1fa144e0-55e5-420c-a63c-85bb6d4662e9"
+      },
+      "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",
+            "29515/29515 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
+            "26421880/26421880 [==============================] - 1s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
+            "5148/5148 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
+            "4422102/4422102 [==============================] - 0s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def format_images(images):\n",
+        "  images = images / 255.0\n",
+        "  images  = np.expand_dims(images, axis=-1)\n",
+        "  images  = tf.image.resize(images, [80, 80])\n",
+        "  images = np.repeat(images[:, :, :, np.newaxis], 3, axis=3)\n",
+        "  images = np.squeeze(images)\n",
+        "  return images\n"
+      ],
+      "metadata": {
+        "id": "8VRLEaQoETtq"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_images = format_images(train_images)\n",
+        "test_images = format_images(test_images)\n"
+      ],
+      "metadata": {
+        "id": "54MG0ww_Bwtg"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "input_tensor = Input(shape=(80, 80, 3))\n",
+        "base_model = InceptionV3(input_tensor = input_tensor, weights ='imagenet', include_top = False)\n",
+        "# add a global spatial average pooling layer\n",
+        "x = base_model.output\n",
+        "x = GlobalAveragePooling2D()(x)\n",
+        "# let's add a fully-connected layer\n",
+        "x = Dense(1024, activation='relu')(x)\n",
+        "# and a logistic layer -- let's say we have 200 classes\n",
+        "predictions = Dense(10, activation='softmax')(x)\n",
+        "model = Model(inputs=base_model.input, outputs=predictions)\n",
+        "for layer in base_model.layers:\n",
+        "    layer.trainable = False\n",
+        "model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+        "model.fit(train_images, train_labels, epochs=5, )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jnkQxkQPCI1N",
+        "outputId": "234349dd-6463-4be9-d22a-f9119187b56e"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "1875/1875 [==============================] - 563s 297ms/step - loss: 0.6177 - accuracy: 0.8018\n",
+            "Epoch 2/5\n",
+            "1875/1875 [==============================] - 551s 294ms/step - loss: 0.4777 - accuracy: 0.8495\n",
+            "Epoch 3/5\n",
+            "1875/1875 [==============================] - 546s 291ms/step - loss: 0.4332 - accuracy: 0.8702\n",
+            "Epoch 4/5\n",
+            "1875/1875 [==============================] - 543s 290ms/step - loss: 0.4008 - accuracy: 0.8863\n",
+            "Epoch 5/5\n",
+            "1875/1875 [==============================] - 542s 289ms/step - loss: 0.3582 - accuracy: 0.9005\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7f8ecac538e0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save('modelo.h5')"
+      ],
+      "metadata": {
+        "id": "IlTi2-WJSfUy"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "files.download('modelo.h5')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "hoRhJsjhSoGx",
+        "outputId": "59ba12d5-b56a-4499-e132-13cdc71bf13b"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_7a4bda3f-a31b-46c1-a859-1873b79d883b\", \"modelo.h5\", 104970160)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
+        "\n",
+        "print('\\ntest_accuracy:', test_acc)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GnWxbi9BTa-a",
+        "outputId": "afb104dd-0560-417a-9238-ae83ed3a68e2"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 - 84s - loss: 0.8012 - accuracy: 0.8377 - 84s/epoch - 270ms/step\n",
+            "\n",
+            "test_accuracy: 0.8377000093460083\n"
+          ]
+        }
+      ]
+    }
+  ]
+}