neuralnetimagenes.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "neuralnetimagenes.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPMjV4nbDD6T54hggqaatqV"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Nj0XljUq2gJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "import tensorflow as tf\n",
        "from keras.datasets import cifar10\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "from tensorflow.keras.optimizers import RMSprop"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-CzYY7VvvF_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a4517833-f011-41be-c4c9-25dd6ef4c266"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 3s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3nR8I-bweWw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "b1f17430-68a0-466e-f492-acf8ab533665"
      },
      "source": [
        "#Graficar imagenes imagen 11\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[11])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbc5eb19400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+e/M4tVmktNLY+Ai1DY+G53FygufPKyGSo7DGSzJNRaTIkyPD1FYnJZtykWsgHgUWZilP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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKA3K04HwtRZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "1cda02e4-54bf-4ad1-bf1c-0c2a58dab2d7"
      },
      "source": [
        "#Imprime los pixeles de la imagen\n",
        "print(x_train[11])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[142 149 152]\n",
            "  [172 172 167]\n",
            "  [176 168 154]\n",
            "  ...\n",
            "  [216 212 211]\n",
            "  [198 194 193]\n",
            "  [205 202 200]]\n",
            "\n",
            " [[191 190 192]\n",
            "  [196 192 190]\n",
            "  [174 166 159]\n",
            "  ...\n",
            "  [229 222 220]\n",
            "  [222 215 213]\n",
            "  [217 210 207]]\n",
            "\n",
            " [[220 212 212]\n",
            "  [217 209 208]\n",
            "  [192 183 182]\n",
            "  ...\n",
            "  [224 214 209]\n",
            "  [225 214 210]\n",
            "  [218 208 203]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[197 152 136]\n",
            "  [196 152 135]\n",
            "  [201 156 140]\n",
            "  ...\n",
            "  [200 165 146]\n",
            "  [199 165 146]\n",
            "  [205 164 150]]\n",
            "\n",
            " [[196 157 139]\n",
            "  [191 152 135]\n",
            "  [193 154 136]\n",
            "  ...\n",
            "  [198 164 144]\n",
            "  [199 165 145]\n",
            "  [201 161 146]]\n",
            "\n",
            " [[186 150 133]\n",
            "  [182 147 130]\n",
            "  [174 139 121]\n",
            "  ...\n",
            "  [158 124 105]\n",
            "  [158 125 106]\n",
            "  [163 125 111]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4v1wRrew2eZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "66211d91-de0d-4940-bcd3-e73025f00bb0"
      },
      "source": [
        "#Imprime la posicion en la imagen la 7 corresponde al caballo\n",
        "print(y_train[11])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dq2QmeAuxoW1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5ddc4c3f-0179-4cbc-e669-ff70a8b1c458"
      },
      "source": [
        "num_classes = 10 #defino el numero de clases posibles\n",
        "\n",
        "print('before',y_test[0])\n",
        "\n",
        "# Convierto los vactores a matrices binarias.\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "\n",
        "\n",
        "print('after',y_test[0])\n",
        "\n",
        "#Normalizo la imagenes en (1) y (0)\n",
        "x_train  = x_train / 255.0\n",
        "x_test = x_test / 255.0"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "before [3]\n",
            "after [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVmsjWqmy-1T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7def54e6-bb0d-4f94-cb12-1f82457333c9"
      },
      "source": [
        "#Imprimo la imagen ahora como un arreglo\n",
        "print(y_train[11])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hSRyjC5zjwE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Inicializo el modelo\n",
        "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), \n",
        "                                   tf.keras.layers.Dense(1024, activation=tf.nn.relu),\n",
        "                                  tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "                                  tf.keras.layers.Dense(10, activation=tf.nn.softmax)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNBcEsGR0Im0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "outputId": "c1de34fd-70bc-46a3-863b-17e17f875f2b"
      },
      "source": [
        "#Compilo y entreno el modelo con 20 epocas\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=RMSprop(lr=0.001),\n",
        "              metrics=['acc'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=20)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Train on 50000 samples\n",
            "Epoch 1/20\n",
            "50000/50000 [==============================] - 9s 179us/sample - loss: 0.3337 - acc: 0.8992\n",
            "Epoch 2/20\n",
            "50000/50000 [==============================] - 7s 148us/sample - loss: 0.2555 - acc: 0.9053\n",
            "Epoch 3/20\n",
            "50000/50000 [==============================] - 7s 147us/sample - loss: 0.2458 - acc: 0.9079\n",
            "Epoch 4/20\n",
            "50000/50000 [==============================] - 7s 142us/sample - loss: 0.2404 - acc: 0.9098\n",
            "Epoch 5/20\n",
            "50000/50000 [==============================] - 7s 146us/sample - loss: 0.2366 - acc: 0.9107\n",
            "Epoch 6/20\n",
            "50000/50000 [==============================] - 7s 148us/sample - loss: 0.2341 - acc: 0.9113\n",
            "Epoch 7/20\n",
            "50000/50000 [==============================] - 7s 145us/sample - loss: 0.2322 - acc: 0.9121\n",
            "Epoch 8/20\n",
            "50000/50000 [==============================] - 7s 144us/sample - loss: 0.2312 - acc: 0.9129\n",
            "Epoch 9/20\n",
            "50000/50000 [==============================] - 7s 144us/sample - loss: 0.2299 - acc: 0.9130\n",
            "Epoch 10/20\n",
            "50000/50000 [==============================] - 7s 146us/sample - loss: 0.2289 - acc: 0.9133\n",
            "Epoch 11/20\n",
            "50000/50000 [==============================] - 7s 145us/sample - loss: 0.2280 - acc: 0.9141\n",
            "Epoch 12/20\n",
            "50000/50000 [==============================] - 7s 144us/sample - loss: 0.2273 - acc: 0.9141\n",
            "Epoch 13/20\n",
            "50000/50000 [==============================] - 7s 148us/sample - loss: 0.2276 - acc: 0.9142\n",
            "Epoch 14/20\n",
            "50000/50000 [==============================] - 7s 146us/sample - loss: 0.2265 - acc: 0.9148\n",
            "Epoch 15/20\n",
            "50000/50000 [==============================] - 7s 145us/sample - loss: 0.2265 - acc: 0.9150\n",
            "Epoch 16/20\n",
            "50000/50000 [==============================] - 7s 147us/sample - loss: 0.2264 - acc: 0.9147\n",
            "Epoch 17/20\n",
            "50000/50000 [==============================] - 7s 144us/sample - loss: 0.2259 - acc: 0.9148\n",
            "Epoch 18/20\n",
            "50000/50000 [==============================] - 7s 144us/sample - loss: 0.2261 - acc: 0.9154\n",
            "Epoch 19/20\n",
            "50000/50000 [==============================] - 7s 145us/sample - loss: 0.2254 - acc: 0.9154\n",
            "Epoch 20/20\n",
            "50000/50000 [==============================] - 7s 144us/sample - loss: 0.2247 - acc: 0.9154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbc5e63c198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtsTq8xx52nH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9481c9bb-038d-42cb-8931-2f68247d32cb"
      },
      "source": [
        "#Evaluar el modelo\n",
        "model.evaluate(x_test, y_test)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 71us/sample - loss: 0.2384 - acc: 0.9126\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2383686560153961, 0.9126]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Bp5fRjp89rj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "db131724-0a5e-4019-c88b-96bdf7fa91e8"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            multiple                  0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                multiple                  3146752   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              multiple                  131200    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              multiple                  1290      \n",
            "=================================================================\n",
            "Total params: 3,279,242\n",
            "Trainable params: 3,279,242\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWnDsWa69sgA",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "5b0acec6-c7d2-4aa8-e50f-b76c40943e3d"
      },
      "source": [
        "#Predicción\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path = '/content/' + fn\n",
        "  img = image.load_img(path, target_size=(32, 32))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "\n",
        "  images = np.vstack([x])\n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  \n",
        "\n",
        "  print(classes)\n",
        "  \n",
        "classes2 = np.argmax(classes, axis=1)\n",
        "\n",
        "if classes2[0] == 0:\n",
        "        print('Es un Avion')\n",
        "elif classes2[0] == 1:\n",
        "        print('Es un automovil')\n",
        "elif classes2[0] == 2:\n",
        "        print('Es un pajaro')\n",
        "elif classes2[0] == 3:\n",
        "        print('Es un gato')\n",
        "elif classes2[0] == 4:\n",
        "        print('Es un ciervo')\n",
        "elif classes2[0] == 5:\n",
        "        print('Es un perro')\n",
        "elif classes2[0] == 6:\n",
        "        print('Es un sapo')\n",
        "elif classes2[0] == 7:\n",
        "        print('Es un caballo')    \n",
        "elif classes2[0] == 8:\n",
        "        print('Es una embarcacion')\n",
        "elif classes2[0] == 9:\n",
        "        print('Es un camion')    \n",
        "else:\n",
        "        print('Can\\'t recognize the image')\n",
        "\n",
        "print('done')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-dda774a2-945e-4355-8198-9c84f8d67442\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-dda774a2-945e-4355-8198-9c84f8d67442\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving truck.jpg to truck (1).jpg\n",
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n",
            "Es un camion\n",
            "done\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}