12_Arquitecturas_avanzadas_redes_neuronales.ipynb

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  "metadata": {
    "colab": {
      "name": "12-Arquitecturas-avanzadas-redes-neuronales.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-zMKQx6DkKwt"
      },
      "source": [
        "# 12. Arquitecturas avanzadas de redes neuronales\n",
        "[**Python Deep Learning** Introducción práctica con Keras y TensorFlow 2. Jordi Torres. Editorial Marcombo ISBN: 9788426728289 ](https://www.marcombo.com/python-deep-learning-9788426728289/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HFbM9dcfxh4l",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2RV359KRHAD",
        "colab_type": "text"
      },
      "source": [
        "## 12.1 API funcional de Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZI47-lpfkZ5c"
      },
      "source": [
        "\n",
        "Modelo base:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5RjnmRVnW-3",
        "colab_type": "code",
        "outputId": "9371b35c-ddf6-4052-a8c3-7c7670dcfe57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train = x_train.reshape(60000, 784).astype('float32') / 255\n",
        "x_test = x_test.reshape(10000, 784).astype('float32') / 255\n",
        "\n",
        "model_base = Sequential()\n",
        "model_base.add(Dense(64, activation='relu', input_shape=(784,)))\n",
        "model_base.add(Dense(64, activation='relu'))\n",
        "model_base.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_base.summary()\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 55,050\n",
            "Trainable params: 55,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUwtA0LIvCS3",
        "colab_type": "text"
      },
      "source": [
        "Modelo con la *functional API* de Keras:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Yxi0LaSHkDT-",
        "colab": {}
      },
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "inputs  = keras.Input(shape=(784,))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ddIr9LPJnibj",
        "outputId": "995b0bfa-ac5e-471b-9208-3024d5602c99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "inputs.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([None, 784])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lZkLJeQonmTe",
        "outputId": "5a499d45-5f31-4eac-d9b9-df6ba67a32ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "inputs.dtype"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tf.float32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sMyyMTqDnpYV",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "dense1  = layers.Dense(64, activation='relu')(inputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DbF-MIO2ntf7",
        "colab": {}
      },
      "source": [
        "dense2  = layers.Dense(64, activation='relu')(dense1)\n",
        "outputs = layers.Dense(10, activation='softmax')(dense2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MrSfwvl-nx9s",
        "colab": {}
      },
      "source": [
        "model = keras.Model(inputs=inputs, outputs=outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7_p0S33EKl-",
        "colab_type": "code",
        "outputId": "6b2cf62f-eace-4399-f1b4-5594a0860672",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_9 (InputLayer)         [(None, 784)]             0         \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 55,050\n",
            "Trainable params: 55,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5EeeV1xJn3jW"
      },
      "source": [
        "Resumen del modelo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xkz7oqj2n1-q",
        "colab": {}
      },
      "source": [
        "inputs  = keras.Input(shape=(784,))\n",
        "dense1  = layers.Dense(64, activation='relu')(inputs)\n",
        "dense2  = layers.Dense(64, activation='relu')(dense1)\n",
        "outputs = layers.Dense(10, activation='softmax')(dense2)\n",
        "\n",
        "model = keras.Model(inputs=inputs, outputs=outputs, name='mnist_model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GirC9odQn5Ep",
        "outputId": "d0f3764c-f3ed-4924-86cb-76ae38e00bd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mnist_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 784)]             0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 55,050\n",
            "Trainable params: 55,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7FGesSSuoAG5",
        "outputId": "c6582c31-ef77-4fb7-a120-6b62f3ac2517",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "keras.utils.plot_model(model, 'model.png', show_shapes=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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4f/68vr7+Z599Nviz169fnz59Os4oPWHChLi4OKU16osvvpCaIBv7/vvvcYERERHGxsaG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\nlY8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-DS8N7Nx23l",
        "colab_type": "code",
        "outputId": "d8bbd188-1624-4f69-c60a-537b334dbf76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "       loss='sparse_categorical_crossentropy',\n",
        "       metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=5)\n",
        "\n",
        "model.evaluate(x_test, y_test)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 74us/sample - loss: 0.2740 - accuracy: 0.9193\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.1226 - accuracy: 0.9628\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.0874 - accuracy: 0.9733\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0706 - accuracy: 0.9779\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0558 - accuracy: 0.9827\n",
            "10000/10000 [==============================] - 0s 42us/sample - loss: 0.0960 - accuracy: 0.9713\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.09600311529980972, 0.9713]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lKz1WWr2LUzF"
      },
      "source": [
        "Diferentes opciones de uso de la API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZBlZbRn5uk-9",
        "outputId": "5421f71b-00ab-4d92-e9b2-ef792c8d0c26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "source": [
        "def get_model():\n",
        "  inputs = keras.Input(shape=(128,))\n",
        "  outputs = layers.Dense(1, activation='sigmoid')(inputs)\n",
        "  return keras.Model(inputs, outputs)\n",
        "\n",
        "model1 = get_model()\n",
        "model2 = get_model()\n",
        "model3 = get_model()\n",
        "\n",
        "inputs = keras.Input(shape=(128,))\n",
        "y1 = model1(inputs)\n",
        "y2 = model2(inputs)\n",
        "y3 = model3(inputs)\n",
        "outputs = layers.average([y1, y2, y3])\n",
        "ensemble_model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "ensemble_model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            [(None, 128)]        0                                            \n",
            "__________________________________________________________________________________________________\n",
            "model_1 (Model)                 (None, 1)            129         input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Model)                 (None, 1)            129         input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "model_3 (Model)                 (None, 1)            129         input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "average (Average)               (None, 1)            0           model_1[1][0]                    \n",
            "                                                                 model_2[1][0]                    \n",
            "                                                                 model_3[1][0]                    \n",
            "==================================================================================================\n",
            "Total params: 387\n",
            "Trainable params: 387\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlLIORIYI9U1",
        "colab_type": "code",
        "outputId": "84b540a8-c3fa-462b-e52a-9dfcb45e420b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        }
      },
      "source": [
        "keras.utils.plot_model(ensemble_model, show_shapes=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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3795FQEAAZsyYgX/9619YtmwZlixZgsrKSkRHR8Pd3R0jR47sVGMLEBER0S9YfBMRaYGv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\nEZHI5AA0UhdBRERERERENIid+X8miMbiTfAVnQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "R9pAPQCnKuMR",
        "outputId": "7c7cd69b-a206-4e35-bae9-9c381990986a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      },
      "source": [
        "num_tags = 12  \n",
        "num_words = 10000  \n",
        "num_departments = 4  \n",
        "\n",
        "title_input = keras.Input(shape=(None,), name='title') \n",
        "body_input = keras.Input(shape=(None,), name='body')  \n",
        "tags_input = keras.Input(shape=(num_tags,), name='tags')  \n",
        "\n",
        "title_features = layers.Embedding(num_words, 64)(title_input)\n",
        "body_features = layers.Embedding(num_words, 64)(body_input)\n",
        "\n",
        "title_features = layers.LSTM(128)(title_features)\n",
        "body_features = layers.LSTM(32)(body_features)\n",
        "\n",
        "x = layers.concatenate([title_features, body_features, tags_input])\n",
        "\n",
        "priority_pred = layers.Dense(1, activation='sigmoid', name='priority')(x)\n",
        "department_pred = layers.Dense(num_departments, activation='softmax')(x)\n",
        "\n",
        "model = keras.Model(inputs=[title_input, body_input, tags_input],\n",
        "                    outputs=[priority_pred, department_pred])\n",
        "\n",
        "keras.utils.plot_model(model, show_shapes=True)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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qavr888+zFQ8AgPFAKQLqcHFxefzxx01M/vP3Y2Jigm9uANBHMTExw35bEYvF\nuDoLAEALUIqAmv70pz9Jv6RpesWKFWwFAwCgNkdHxwULFkhOjJiamv7hD39wcnJiNyoAAGOAUgTU\nFBUVJf3N/eSTTzo6OrIbEgCAev70pz9JP/B32E8tAAAwRlCKgJrs7OyeeuopphqhaXrt2rVsRwQA\noKZnn32Ww/nXM39NTEyWLVvGbjwAAEYCpQiob+3atcwF1mZmZvjmBgD9ZWNjs3jxYg6Hw+FwlixZ\nYmtry3ZEAABGAaUIqC8iIsLCwoIQsnTpUisrK7bDAQBQ39q1a4eGhoaGhtasWcN2LAAAxgKlCKjP\n0tKSORmCq7MAQN8tXbqUz+fzeLxnnnmG7VgAAIwFJX2jXmFhYXR0NIvRAICRkM48alu5cuWxY8dG\nPw4AwBiJioo6evQo21EA6C7OyEUFBQXajwP01NDQUEFBwXPPPadG3/Pnz2dlZRnD31t0dHRycnJQ\nUBDbgegE5nPX1Gjz5s3buHGjpkYDI1dVVUVR1IwZMzQ+8t69ewkhBv+3ajxZXUnM5w4ACsgoRfBc\nJ1DJ8uXLuVyuen2zsrKM4e8tOjo6KCjIGLZUSRosRdzc3LBjQVOYhyNJptLSIOZ3cWP4WzWSrK4k\nnA8BeCjNJ1wwNmrXIQAAOmUsihAAAFAAt60DAAAAAAALUIoAAAAAAAALUIoAAAAAAAALUIoAAAAA\nAAALUIqAnjl16pRAIDhx4gTbgeiKr776KiUlRSwWL1++3N3dncvlurq6RkZGVldXP7SvvF7Hjx9/\n5513hoaGxj58AJAL6U4ach2AQUIpAnpGI4/GMxg7d+7Mzs7eunWrWCw+d+7cp59+2tHRUV5eLhQK\n58+f39zcrLi7vF4RERFcLjcsLOz+/fva2RAAGAnpTgK5DsBQoRQBPRMeHt7Z2bl06dKxXpFQKAwO\nDh7rtYzG22+/nZ+fX1hYaG1tTQgJCgoKCQnh8/menp7p6emdnZ1///vfHzqIvF5JSUkzZsxYsmTJ\n4ODgGG8HAMiGdMdArgMwYChFAGTLzc1tbW1lOwq56urqduzYsWvXLua5LhwOR/oqDi8vL0JIfX29\n4kEU90pNTa2qqtLg4wgBQDfpcrpDrgMwbChFQJ+Ul5e7u7tTFLVv3z5CSE5OjqWlJZ/PLykpefrp\np21sbNzc3I4cOcK8OTs7m8vlOjo6xsfHu7i4cLnc4ODgyspKpjUxMdHc3GWv46IAACAASURBVNzZ\n2Zl5uWHDBktLS4qi2tvbCSHJycmbN2+ur6+nKMrHx4cQcvr0aRsbm/T0dBY2W5bs7GyapiMiImS2\nCoVCQoiNjY1KYw7rZWdnt2DBgqysLFwlAqB9SHcM5DoAw4ZSBPRJSEjIDz/8IHm5fv36jRs3CoVC\na2vrgoKC+vp6Ly+vdevWiUQiQkhiYmJsbGxvb29SUlJjY+OlS5cGBwefeuqp27dvE0Kys7NXrVol\nGWr//v27du2SvMzKylq6dKm3tzdN03V1dYQQ5r5GsVistY1V7OTJk35+fnw+X2brhQsXCCEhISEq\njTmy16OPPnrnzp0rV66MIlIAUAfSHQO5DsCwoRQBQxAcHGxjY+Pg4BATE9PT03Pr1i1JE4fDmTJl\nioWFhb+/f05OTnd3d15enhqrCA8P7+rq2rFjh+aiVl9PT09DQ4O3t/fIppaWlvz8/KSkpKCgIHm/\nIyrfa9KkSYSQq1evaiRsABg9o0p3yHUABo/DdgAAmmRubk4IYX4mHCkwMJDP51+7dk27QWlea2sr\nTdMyfyYMCgrq6elZtWrVm2++aWZmpuSA8noxq2hpadFI2ACgQcaQ7pDrAAweShEwLhYWFm1tbWxH\nMVp9fX2EEAsLi5FNjo6Oubm5U6dOVWlAeb14PJ5kdQCgXwwg3SHXARg8XKAFRkQkEt2/f9/NzY3t\nQEaL+daU+VguBwcHW1tbVQeU12tgYECyOgDQI4aR7pDrAAwezoqAESkrK6Npet68ecxLDocj79oG\nHefo6EhRVGdn58gm9R7MLK8XswonJyc1xgQAFhlGukOuAzB4OCsCBk4sFt+7d29wcLC6ujo5Odnd\n3T02NpZp8vHx6ejoKC4uFolEbW1tN2/elO5ob2/f3Nzc2NjY3d0tEolKS0t1Z3ZLPp/v5eXV1NQ0\nbHldXZ2Tk1N0dLT0wpiYGCcnp0uXLskbTWYvBrOKgIAATUQNAGPL8NIdch2AwUMpAvpk3759c+bM\nIYRs2bIlMjIyJydn7969hJDp06ffuHHj0KFDmzdvJoQsXry4traW6dLX1xcQEMDj8UJDQ319fc+e\nPSu57Hj9+vULFy5cvXq1n5/fG2+8wZyaDwoKYqa/TEhIcHR09Pf3X7JkSUdHByvbq0B4eHhNTQ0z\nO76EzEnxBwYGWltbS0pK5A2lYCr9ixcvurq6Tp8+fTShAoAakO4YyHUABo6WUlBQMGwJwNjRwt9b\nXFycvb39mK5CGYSQgoICzY5ZW1vL4XA+/vjjh75zaGgoNDQ0NzdX1VW0t7dzudw9e/aoFaBcGvzc\no6KioqKiNDIUwJjSwt+qLqS7scjq+pvraOQoACXgrAgYOJn3OxoAHx+ftLS0tLS0Bw8eKHjb0NBQ\ncXFxd3d3TEyMqqtITU2dOXNmYmLiKMIEAO0xyHSHXAdg2NgpRebMmWNqajpz5szRDPLSSy9ZW1tT\nFFVVVaVM66lTpwQCgXo3uimvqKjIy8uLkmXixIlqDGjA+wpGKSUlZeXKlTExMTLv6WSUlZUVFRWV\nlpbKe1axPJmZmVVVVadOnVJ+wn4DZgyHoVgs3rt3b3BwsPJdkO5AO5DrAAwYO6XIxYsXFy5cOMpB\nPvzww0OHDinfSsu/SFSDnn322Rs3bnh7ewsEAubE0+DgYG9vb0tLi6r5kWHA+2qsbd26NS8vr7Oz\n09PT89ixY2yHMybS09MTExPfeusteW8ICws7fPiws7OzSsOWlJT09/eXlZXZ2dmNOkZDYPCHYW1t\n7fz58zdt2tTb26t8L6Q73WHw6Q65DsBQsTmZL0VR2lxdeHi4gh9Uxo6pqSmPx+PxeL6+vmoPYiT7\nSrMyMjIyMjLYjmLMLVq0aNGiRZodMzIyMjIyUrNjGgBDPQyvXLmSlpaWkJDQ09Mzyv+XI92xxRjS\nHXIdgEFi816R0Z8MVfyNpcHvM5qmjx49+sEHH4xmkOLiYrX7Gtu+AtBBhnoYzpgxo6ioaM2aNTKf\naa0epDsAAFCGOqXI0NDQ66+/7u7uzuPxpk+fzsyYkZWVZWlpaWJiMnv2bCcnJzMzM0tLy1mzZoWG\nhk6YMIHL5dra2r722mvS49TV1U2ePNnS0pKZebC8vFzxKgghNE3v3r3bz8/PwsJCIBC8+uqr0gMq\naC0vL3d3d6coat++fYSQnJwcS0tLPp9fUlLy9NNP29jYuLm5HTlyRDqAjIwMPz8/Ho83fvx4T0/P\njIyMVatWMa2nT58ezZzrRrWvAFiHlDUaSHdIdwAAY0h6Oi0lp+H7y1/+YmFhcezYsXv37m3dutXE\nxOTixYs0Te/cuZMQUllZ2dPT097evnjxYkLIyZMn29raenp6mLkpqqqqmEHCwsK8vLwaGhpEItHP\nP/88d+5cLpf722+/KV7Ftm3bKIp677337t2719vbu3//fkLI5cuXmV6KW5nZ099//33JmwkhX3/9\ndWdnZ2tra2hoqKWl5cDAANOanp5uampaUlLS29v7008/OTk5/eEPf5Dsgc8//9za2jotLU3eLpK+\neJqm6aSkpKtXr0q/wXj2lQLGM3k0GYPJfPWX9ifzRcpS0ty5c2fMmDFsIdKdRvazkUzqajxZXUlG\n8rkDjIbKpYhQKOTz+TExMczL3t5eCwuL9evX0//+vunu7maa/vGPfxBCJN9JFy5cIITk5+czL8PC\nwqS/86qrqwkhf/nLXxSsore3l8/nP/XUU5JezI9VzDeK4lZazveNUChkXjJfTnV1dczLOXPmPPbY\nY5KhXn75ZRMTk/7+fsU7R8Lb23tYySfzu9nI95XxfGmhFJGm5VIEKUt5MkuRh0K6U2Y/G8l/SY0n\nqyvJSD53gNFQ+bb169ev9/b2Tps2jXnJ4/GcnZ2vXbs28p3m5uaEkMHBQeYlc/mvSCSSOWxAQIBA\nIGC+deStoq6urre3NywsTOYIilsfiolWEl5fXx+Xy5W0Dg0NmZmZmZqaKj+gQCC4f/8+8+/k5GRl\n1m6c+6qwsFC9MPTL+fPn2Q5BV2h5VyBlaQHSnTJDNTU1GXy6Y45ug99M5TU1Nbm5ubEdBYBOU7kU\n6enpIYRs3759+/btkoUuLi6jD8XMzIxJ9/JW0dTURAhxcHCQ2V1xq6qWLFmye/fukpKSRYsW1dTU\nFBcXP/PMM2p/r2dlZWkkKglD2lfR0dEaCUPHZWVlafzPAJSBlKVlSHfyVFRUGEm6M5LNVFJUVBTb\nIQDoNJVvW2cS+t69e6XPrYz+Z87BwcGOjg53d3cFq2B+i+rv75c5guJWVaWmpj7xxBOxsbE2NjYr\nVqxYtWqVgonqtczA9pU2Tv6xjeACLSmSW421AylLrxnSfjaGC3VwgdYwqEMAHkrlUoSZ80TmU2xH\n4+zZs2KxeNasWQpWMW3aNBMTk2+//VbmCIpbVVVTU1NfX9/W1iYSiW7dupWTkzP65x/9/vvvL7zw\nwuhjM4Z9BaApSFmsQLoDAABlqFyKcLncF1544ciRIzk5OV1dXUNDQ01NTb///rsa6x4YGOjs7Bwc\nHLx06VJiYqKHh0dsbKyCVTg4ODz77LPHjh3Lzc3t6uqqrq6Wns1dcauqXnnlFXd39wcPHshsLS0t\nVWl2S5qmhUJhUVGRjY2NevHo774CYBdS1igh3UlDugMA0DDpM4lKnlrt7+/fsmWLu7s7h8NhsnxN\nTU1WVhafzyeETJw48dy5c2+//bZAICCEODk5HT58OD8/38nJiRBiZ2d35MgRmqbz8vIWLlzo6OjI\n4XDGjRu3evXqmzdvKl4FTdPd3d0vvfTSuHHjrKysQkJCXn/9dUKIm5vblStXFLe+//77zs7OhBA+\nnx8REbF//34m2kmTJtXX13/wwQfMt6aHhwcza+Q333wzbtw4yV4yMzObMmVKUVERE96pU6esra3f\nfPPNkTvns88+GzmfjMT27dtpmjaqfaWA8ZzKJ7hAS4r2J/NFylLs/Pnzjz/+uOT+GWdn5+Dg4G+/\n/ZZpRbrTyH42kpmUjCerK8lIPneA0VCnFDEG+/fvT05Olrzs7+/fuHGjhYVFb28vi1HpJrX3lfH8\nvaEUkab9UsQYIGVph9r72Uj+Vo0nqyvJSD53gNFQeQYtY/DPf/4zMTFR+opkc3Nzd3d3kUgkEol4\nPB6Lseka7CsA1uEw1A7sZwAAjVP5XhFjwOPxzMzMcnNzW1paRCJRc3Pzhx9++Prrr8fExKh99bOh\nwr4CYJ2Cw7C5uZmSLyYmhu3Y9QnSHQCAxqEUkUEgEHzxxRc///yzr68vj8fz9/fPy8t7++23mYcE\ngzTsK4376quvUlJSxGLx8uXL3d3duVyuq6trZGQk85Q3xdTrxRCJRBkZGT4+Pubm5ra2ttOmTWts\nbBz5tr6+vsmTJ0se7HD8+PF33nlnaGhI6e0DzVNwGE6ePFnBafH8/Hy2Y9cnSHcah3QHALhAS7bQ\n0NAvv/yS7Sj0A/aVBu3cufPy5cuHDx8Wi8Xnzp0rLi6eNWtWS0tLXFzc/Pnzf/nll0ceeURBd/V6\nMaKjo3/55ZfDhw/Pnj27ra0tPj5e5jRB27Ztu379uuRlREREQ0NDWFhYcXGxra2tGpsMGoHDUDuw\nnzUI6Q4ACM6KgGETCoXBwcG6NpQ8b7/9dn5+fmFhobW1NSEkKCgoJCSEz+d7enqmp6d3dnb+/e9/\nf+gg6vXKz88vLi4+evTo3LlzORyOi4tLSUnJtGnThr3thx9++Pnnn4ctTEpKmjFjxpIlSwYHB5Xc\nUgDQOKQ7pDsAfYRSBAxZbm5ua2urrg0lU11d3Y4dO3bt2sU8HJrD4Zw4cULS6uXlRQipr69XPIh6\nvQghBw4cmDVrVkBAgIL3CIXCV199NSsra2RTampqVVWVzCYA0A6kO2V6EaQ7AB2DUgR0HU3TmZmZ\nU6ZMsbCwsLOzW7Zs2bVr15imxMREc3Nz5rEAhJANGzZYWlpSFNXe3k4ISU5O3rx5c319PUVRPj4+\n2dnZXC7X0dExPj7excWFy+UGBwdXVlaqMRQh5PTp0yo99+2hsrOzaZqOiIiQ2SoUCgkhqt4aq2Sv\ngYGBioqKmTNnKn7btm3bNmzY4ODgMLLJzs5uwYIFWVlZNE2rFCEASEO6I0h3AEYGpQjoutTU1JSU\nlG3btrW2tn733Xe3b98ODQ1taWkhhGRnZ69atUryzv379+/atUvyMisra+nSpd7e3jRN19XVJSYm\nxsbG9vb2JiUlNTY2Xrp0aXBw8Kmnnrp9+7aqQxFCmDsXxWKxpjbz5MmTfn5+zOPVRrpw4QIhJCQk\nRKUxlezV3Nw8MDDw008/LVy4kPlfy5QpU/bv3y/9Rfv999/X19c/99xz8gZ59NFH79y5c+XKFZUi\nBABpSHcE6Q7AyKAUAZ0mFAozMzNXrFixdu1agUAQEBBw8ODB9vb2Dz74QL0BORwO84ujv79/Tk5O\nd3d3Xl6eGuOEh4d3dXXt2LFDvTCG6enpaWhokPng6paWlvz8/KSkpKCgIHk/Io6yF3O/poODQ3p6\nek1NTUtLy7Jly1555ZVPP/2UeYNQKExOTs7JyVEwyKRJkwghV69eVTJCABgG6Q7pDsAIoRQBnVZT\nU/PgwYPAwEDJkjlz5pibm0uuNBiNwMBAPp8vuf6BRa2trTRNy/yNMCgoKCkpadmyZaWlpWZmZkoO\nqFIvCwsLQsjUqVODg4Pt7e0FAsGuXbsEAoHkP0Bbt259+eWXXV1dFQzCBM/8fAsAakC6Q7oDMEKY\nzBd02v379wkhVlZW0gttbW27u7s1Mr6FhUVbW5tGhhqNvr4+8u/vyGEcHR1zc3OnTp2q0oAq9XJx\ncSGEMJeJM8zNzT08PJgbQMvLy69evZqZmal4EOZR08yGAIAakO6Q7gCMEM6KgE5j5m4f9k18//59\nNze30Q8uEok0NdQoMV9sMp+c5eDgoMYE9ir1srKymjRp0i+//CK9cHBwUCAQEEJyc3O//vprExMT\n5vnczH2c6enpFEX9+OOPkvcPDAxINgQA1IB0h3QHYIRQioBOmzZtmpWVlfR3QGVl5cDAwOzZs5mX\nHA5HJBKpN3hZWRlN0/PmzRv9UKPk6OhIUVRnZ+fIphMnTii+VEAmVXtFR0dfvnz5xo0bzMve3t6b\nN28yk13m5eVJP5+b+VV127ZtNE1LX0nCBO/k5KRqqADAQLpDugMwQihFQKdxudzNmzd/9tlnn3zy\nSVdX19WrVxMSElxcXOLi4pg3+Pj4dHR0FBcXi0Sitra2mzdvSne3t7dvbm5ubGzs7u5mvnfFYvG9\ne/cGBwerq6uTk5Pd3d1jY2PVGKq0tFSDs1vy+XwvL6+mpqZhy+vq6pycnKKjo6UXxsTEODk5Xbp0\nSd5oavTatGmTh4dHbGzsrVu37t69u2XLFqFQ+Ne//lX5TWCCVzxVPwAogHSHdAdghFCKgK7buXNn\nRkZGWlra+PHjFyxYMHHixLKyMktLS6Z1/fr1CxcuXL16tZ+f3xtvvMGcMQ8KCmLmrExISHB0dPT3\n91+yZElHRwchpK+vLyAggMfjhYaG+vr6nj17VnLJsqpDaVZ4eHhNTQ0zNb6EzHnrBwYGWltbS0pK\n5A2lRi87O7tz5865ubnNnDnT1dX1woULJ0+efOjU+9IuXrzo6uo6ffp05bsAwDBId8Mg3QEYPulz\nkQUFBcOWAIwd7f+9xcXF2dvba3ONDEJIQUGB4vfU1tZyOJyPP/74oaMNDQ2Fhobm5uaqFIN6vZTU\n3t7O5XL37NmjzJs1+LlHRUVFRUVpZCiAMaX9v1VW0p2SR7fxpDvkKICHwlkRMC4y75XUBT4+Pmlp\naWlpacy09/IMDQ0VFxd3d3fHxMQoP7h6vZSXmpo6c+bMxMTEsRgcANSDdDfqSGVAugPQLJQiALoi\nJSVl5cqVMTExMm/oZJSVlRUVFZWWlsp7ULEGeykpMzOzqqrq1KlTyj8HAACMHNIdADBQioCx2Lp1\na15eXmdnp6en57Fjx9gOR7b09PTExMS33npL3hvCwsIOHz7s7Oys0rDq9VJGSUlJf39/WVmZnZ2d\nxgcHAPUg3SHdAegLPOIQjEVGRkZGRgbbUTzcokWLFi1axHYUyoqMjIyMjGQ7CgD4L0h3YwHpDmAs\n4KwIAAAAAACwAKUIAAAAAACwAKUIAAAAAACwAKUIAAAAAACwQMZt6ytXrtR+HGDwaJqmKEp6SVNT\nEzGav7e9e/cePXqU7Sh0AvO5a0pFRYWR/AmBXquoqCBGkO6MKqsro6KiYt68eWxHAaDTKJqmJS/O\nnz+fmZnJYjRgqG7cuNHQ0BAWFsZ2IKArNFKYZWZmnj9/fvTjgBGqq6v7/fffQ0ND2Q4EDFxQUNCm\nTZvYjgJAd/1XKQIwRi5cuDB37tzvv/8+ODiY7VgAAEhMTMyDBw8+//xztgMBADBquFcEtOGxxx6b\nPXv2gQMH2A4EAIAQQioqKubOnct2FAAAxg6lCGhJfHz80aNHW1tb2Q4EAIxda2vrzZs3cRE/AADr\nUIqAljz33HN8Pv8f//gH24EAgLH74YcfKIoKDAxkOxAAAGOHUgS0hM/nr1279uDBg2KxmO1YAMCo\nVVZWTpkyxc7Oju1AAACMHUoR0J6EhISGhoYvv/yS7UAAwKhVVlbiRhEAAF2AUgS0Z8qUKaGhoQcP\nHmQ7EAAwXmKx+KeffkIpAgCgC1CKgFYlJCScOHHi5s2bbAcCAEbq559/7urqwj3rAAC6AKUIaNWK\nFSvGjx//0UcfsR0IABipyspKS0vLqVOnsh0IAACgFAHtMjc3f/HFFw8dOiQSidiOBQCMUWVlZWBg\nIIfDYTsQAABAKQJaFx8f39raevz4cbYDAQBjVFFRgauzAAB0BEoR0DZ3d/fFixfjyesAoH3d3d3X\nrl3DPesAADoCpQiwID4+/ptvvrl+/TrbgQCAcblw4cLQ0NBjjz3GdiAAAEAIShFgxZIlSzw8PP72\nt7+xHQgAGJeKiooJEya4urqyHQgAABCCUgRYYWJism7dury8vN7eXrZjAQAjUllZiRtFAAB0B0oR\nYMe6deuEQmFhYSHbgQCAEblw4QJuFAEA0B0oRYAdDg4Oy5Ytw5PXAUBrbty40dLSglIEAEB3oBQB\n1iQkJFRWVv70009sBwIARqGystLMzGzWrFlsBwIAAP+CUgRYs2DBgmnTpuHmdQDQjsrKyunTp/P5\nfLYDAQCAf0EpAmx6+eWXDx8+fO/ePbYDAQDDh4cbAgDoGpQiwKbnn3+eoqjDhw+zHQgAGLj+/v6q\nqircKAIAoFNQigCbBALB6tWrc3JyaJpmOxYAMGRVVVX9/f0oRQAAdApKEWDZhg0bfv3113PnzrEd\nCAAYsoqKCnt7+0mTJrEdCAAA/AdKEWDZzJkz58yZc+DAAbYDAQBDVllZOXfuXIqi2A4EAAD+A6UI\nsC8hIeGzzz5raWlhOxAAMFhMKcJ2FAAA8F9QigD7YmJirKys8vLy2A4EAAxTW1vbjRs3UIoAAOga\nlCLAPh6P9/zzzx84cGBoaIjtWADAAFVUVFAUNWfOHLYDAQCA/4JSBHTChg0bbt++febMGbYDAQAD\nVFlZ6evrO27cOLYDAQCA/4JSBHSCj4/PwoULcfM6AIyFiooKXJ0FAKCDUIqArkhISDh16lRjYyPb\ngQCAQRGLxT/++CNKEQAAHYRSBHTFsmXLnJ2dDx06xHYgAGBQfv31187Oznnz5rEdCAAADIdSBHQF\nh8N58cUXDx061N/fz3YsAGA4KioqeDxeQEAA24EAAMBwKEVAh7z88ssdHR3FxcXSC0UiEVvxAIAB\nqKysDAwMNDMzYzsQAAAYDqUI6JAJEyaEh4cfPHiQefnzzz+/8soruMIbAFQSHx+/du3affv2Xbx4\nUSQS4eGGAAA6i8N2AAD/JSEh4emnn969e/f//d//nT9/3sTERCAQsB0UAOgTmqY//fTTgoKCwcFB\nMzMzU1PTCRMmFBYWzps3z93dne3oAADgP1CKgA65c+fO+fPnraystmzZYmJiQggRi8U9PT1sxwUA\n+sTDw8Pc3Jy560wkEolEoi+//PLUqVM0TY8bN+4Pf/jD4cOHLSws2A4TAABQioAOGBoaOnXq1P79\n+7/44gsOh8PcHCJ58vrAwIBIJMJ13gCgpIkTJw67x2xgYID5x927d728vFCHAADoCIqmabZjAGNX\nWVm5YMGCgYEBeX+Nd+/etbe313JUAKCnvv/++5CQkJHLKYpycnKqra21srLSflQAADASblsH9s2d\nO7ewsJCiKHlvePDggTbjAQC95uHhIXM5TdMHDhxAHQIAoDtQioBOiIiI2Ldvn7zWrq4ubQYDAHrt\nkUceGXlJp5mZ2aJFi5YtW8ZKSAAAIBNKEdAVCQkJmzZtYu5WH6a7u1v78QCAnjIxMXF2dh62kKIo\nyUThAACgI1CKgA7Zs2fP6tWrOZzhsymgFAEAlXh5eUm/NDU13bVrl6enJ1vxAACATChFQIdQFPXR\nRx+FhIRIX1xBURQu0AIAlfj4+Eh+1DA1NXV3d9+4cSO7IQEAwEgoRUC3mJubl5SUTJo0SVKNmJiY\n4KwIAKjEw8PD1NSU+bdYLP7www8xgS8AgA5CKQI6x8bG5osvvhg/fjzzo6apqSlKEQBQieTRImZm\nZmvWrHniiSfYjggAAGRAKQK6yNXV9csvv+TxeCYmJrhACwBUNXHiRLFYTAjhcrnvvfce2+EAAIBs\nKEVAR02dOrWkpMTExKS/vx9nRQBAJZJHi+zevdvR0ZHdYAAAQJ7hUxXBMIWFhWyHYNTWr1+fnZ1d\nVVWFD8KQBAcHu7m5sR0FIYScP3/+9u3bbEcBmicWi01MTDw8PGxtbZE99MiECROCgoJGPw4+dACd\nNfz/ADQoxN4nBWCwCgoK2D6y/yUqKortnQEA/xEVFaWRQ5vt7QAAuYb9HwBnRR6uoKBg1apVbEdh\ngCiKUnLfnj59evHixVoIaSysXLmSEHL06FG2A9EVFEWxHcJ/iYqKwqejazRy1JSUlERGRmooojFR\nWFgYHR2N/zdLMJ+7puC7W+8YzxGh/P9/DM/I/wPgXhHQA/pbhwAAW3S8DgEAAIJSBAAAAAAAWIFS\nBAAAAAAAWIBSBAAAAAAAWIBSBAAAAAAAWIBSBAAAAAAAWIBSBPTMqVOnBALBiRMn2A5krHz11Vcp\nKSlisXj58uXu7u5cLtfV1TUyMrK6uvqhfdXrxRCJRBkZGT4+Pubm5ra2ttOmTWtsbBz5tr6+vsmT\nJ2/fvp15efz48XfeeWdoaEjp7QMYK0gOCiA5gKEy1AM/Pj6e+re1a9dKN40mFUiIxeK9e/cGBwcP\nW56Wlubv729jY2NhYeHj4/Paa689ePCAaRp5UBcXF0uCHD9+vHpbilIE9Ixhzzi+c+fO7OzsrVu3\nisXic+fOffrppx0dHeXl5UKhcP78+c3NzYq7q9eLER0d/b//+7+HDx/u7e399ddfvb29JdlH2rZt\n265fvy55GRERweVyw8LC7t+/r9KWAmgckoMCSA5gqAz4wLe3ty8tLb1+/Xpubq5k4ShTAaO2tnb+\n/PmbNm3q7e0d1vTNN9+88sorjY2N7e3tGRkZWVlZkgf+jDyoIyMjm5qavvvuuyVLlqi/nRp5rKkB\nI7r0ZGgDo+P7tre3NygoaPTjREVFKfn84LfeesvX11coFNI0LRKJnnnmGUnThQsXCCHp6emKR1Cv\nF03TR44coSiqurpa8du+//77RYsWEUK2bdsmvTwxMTEoKEgkEj10RbSOfe7KfzqgTTr+uWgqORQU\nFCj5LWwkyUGDn7tO5RlQkvJHBCs0deDTyv19xsXFubq6Dls4+lRA03RVVdWKFSs++eSTmTNnzpgx\nY1hreHj44OCg5CXzHMZbt25Jlsg8qJOSksaNG/fQVdOyth1nRQBky83NbW1t1drq6urqduzYsWvX\nLi6XSwjhcDjSp5u9vLwIIfX19YoHUa8XIeTAgQOzZs0KCAhQ8B6hOeVb1wAAIABJREFUUPjqq69m\nZWWNbEpNTa2qqpLZBGB4kByGQXIAY6DlA38kjaQCQsiMGTOKiorWrFljYWExsvXzzz83NTWVvGQu\nu5I+eaLxgxqlCOiT8vJyd3d3iqL27dtHCMnJybG0tOTz+SUlJU8//bSNjY2bm9uRI0eYN2dnZ3O5\nXEdHx/j4eBcXFy6XGxwcXFlZybQmJiaam5s7OzszLzds2GBpaUlRVHt7OyEkOTl58+bN9fX1FEX5\n+PgQQk6fPm1jY5Oenj5Gm5adnU3TdEREhMxWoVBICLGxsVFpTCV7DQwMVFRUzJw5U/Hbtm3btmHD\nBgcHh5FNdnZ2CxYsyMrKog33RDnoOCQHlcZEcgDDYMAH/khjkQoe6s6dOzwez9PTU7JE4wc1ShHQ\nJyEhIT/88IPk5fr16zdu3CgUCq2trQsKCurr6728vNatWycSiQghiYmJsbGxvb29SUlJjY2Nly5d\nGhwcfOqpp27fvk0Iyc7OZk47Mvbv379r1y7Jy6ysrKVLl3p7e9M0XVdXRwhh7tMSi8VjtGknT570\n8/Pj8/kyW5kTryEhISqNqWSv5ubmgYGBn376aeHChUx2njJlyv79+6WzzPfff19fX//cc8/JG+TR\nRx+9c+fOlStXVIoQQFOQHFQaE8kBDIMBH/gjjUUqUKy3t/ebb75Zt26dubm59HLNHtQoRcAQBAcH\n29jYODg4xMTE9PT03Lp1S9LE4XCmTJliYWHh7++fk5PT3d2dl5enxirCw8O7urp27Nihuaj/o6en\np6Ghwdvbe2RTS0tLfn5+UlJSUFCQvN9CRtmLuQPVwcEhPT29pqampaVl2bJlr7zyyqeffsq8QSgU\nJicn5+TkKBhk0qRJhJCrV68qGSGAdiA5jKYXkgPoKX0/8EfSeCpQRkZGhouLy5tvvjlsuWYPapQi\nYFCYwp35/WOkwMBAPp9/7do17Qb1cK2trTRNy/ypIygoKCkpadmyZaWlpWZmZkoOqFIv5mrRqVOn\nBgcH29vbCwSCXbt2CQSCDz74gHnD1q1bX375ZVdXVwWDMMG3tLQoGSGAliE5qNELyQH0nZ4e+CNp\nPBU81GeffVZYWHjmzBlra+thTZo9qDkaGQVAX1hYWLS1tbEdxXB9fX3k39/6wzg6Oubm5k6dOlWl\nAVXq5eLiQghhLodlmJube3h4MHe/lZeXX716NTMzU/EgPB6P/HtDAPQRksNISA5g8HTzwB9J46lA\nsfz8/MzMzLKyskceeWRkq2YPapwVASMiEonu37/v5ubGdiDDMUe1zGeBOTg42NraqjqgSr2srKwm\nTZr0yy+/SC8cHBwUCASEkNzc3K+//trExIR5hhFzZ2p6ejpFUT/++KPk/QMDA5INAdA7SA4yITmA\nYdPZA38kjacCBd5///1PPvnkm2++kVmHEE0f1ChFwIiUlZXRND1v3jzmJYfDkXfSVsscHR0piurs\n7BzZdOLECcUXP8ikaq/o6OjLly/fuHGDednb23vz5k1m+s68vDzp+b+ZX4+YRwcEBgZKRmCCd3Jy\nUjVUAF2A5CAPkgMYMJ098EfSeCqQiabpLVu2XL16tbi42MrKSt7bNHtQoxQBAycWi+/duzc4OFhd\nXZ2cnOzu7h4bG8s0+fj4dHR0FBcXi0Sitra2mzdvSne0t7dvbm5ubGzs7u4WiUSlpaVjN20fn8/3\n8vJqamoatryurs7JySk6Olp6YUxMjJOT06VLl+SNpkavTZs2eXh4xMbG3rp16+7du1u2bBEKhX/9\n61+V3wQmeMUPHwDQKUgOyvRCcgADoxcH/kiaTQXy/PLLL+++++6hQ4fMzMwoKXv27JF+m2YPapQi\noE/27ds3Z84cQsiWLVsiIyNzcnL27t1LCJk+ffqNGzcOHTq0efNmQsjixYtra2uZLn19fQEBATwe\nLzQ01NfX9+zZs5JLLdevX79w4cLVq1f7+fm98cYbzKnGoKAgZl6/hIQER0dHf3//JUuWdHR0jPWm\nhYeH19TUMPOCS8ictHtgYKC1tbWkpETeUGr0srOzO3funJub28yZM11dXS9cuHDy5MmHPkxA2sWL\nF11dXadPn658FwANQnIgSA5gfAz4wB9JU6mgoqIiJCTkkUceqaysvHLliouLy+OPP/7dd9/JG20k\nDR/Uyjyk3ZiREQ+oB03Rwr6Ni4uzt7cf01U8VFRUVFRU1EPfVltby+FwPv7444e+c2hoKDQ0NDc3\nV6Uw1OulpPb2di6Xu2fPHmXerFPHlJKfDmiZFj4XXUgOBQUFynwLG09y0ODnrlN5BpSk5BExGrpw\n4NPK/X3GxcW5urpKLxnrVKAkmQd1UlLSuHHjlOk+cttxVgQMnMx7vHSQj49PWlpaWloaM5G/PEND\nQ8XFxd3d3TExMcoPrl4v5aWmps6cOTMxMXEsBgcYI0gOavdSHpID6Bp9OfAJIUKh8MyZM7W1tcxt\n4mOaCpQnfVDTNN3c3FxeXs489lE9KEVGa8+ePcy9RAcPHtT+2ouKiry8vJgr+ZydndeuXSvvnVeu\nXImJifH09LSwsBg/fvyMGTMkz6yJiYmhFPr888+lVyTvgT6ZmZkURZmYmEyePJk50wcqSUlJWbly\nZUxMjMz70hhlZWVFRUWlpaXynreqwV5KyszMrKqqOnXqlAanM9cp7B7jEmKxeO/evcHBwcp3QX4w\nGEgOBqOiomLKlCnMtGNOTk4jHx43dpRPCKAjOjo6Fi9e7Ovr++KLLzJLxi4VKGnYQV1SUuLq6hoa\nGnry5En1B9XgKRuDRJQ4icZcgHjgwAHthDSSt7e3QCBQ8Ibq6mo+n5+UlNTQ0CAUCq9fv/7aa6+F\nhYUxrdHR0V988cX9+/dFItHvv/9OCImIiBgYGOjp6WltbV23bt2JEyckKyKEODs7DwwMDFvF4OCg\nh4cHIUQy7EMps29HIyUlhXm20cSJE48ePTp2K1JM1UsOzpw5s2XLlrGLR7OKi4szMjIGBweV7zLW\nn7tKlL98jt1j/Lfffnv88ccJITNmzFC1rz7mh7G+QEtHkoOql6MYfHIwngu0/vjHPxJC7t27p/1V\nPzQhsGisL9DSkQOfHvXfJ1upQI2DeqSR246zIloiFApV+jlTs/bs2WNra5uVlTVx4kQul+vr6yu5\nH4sQQlHU448/LhAIOByOZImZmRmfz3dwcJg9e7b0ULNnz/7nP/9ZXFw8bBVFRUWamktOUzIyMvr7\n+2mabmhoiIqKYjscZS1atOjtt99mOwplRUZGpqSkmJqash0I+8buGL9y5cpf//rXhIQEle4VVp4R\n5gckBy3Q/eTA7veydhjDNipPTw/8kdhKBWN0UKMU0ZLc3NzW1la21n737t3Ozk7pCR/Mzc1PnDjB\n/PvIkSMKzuLFxcU988wzkpfr168nhBw4cGDY2zIzM5l5KgCM09gd4zNmzCgqKlqzZo3M5+yOHvID\nGCf1jlmapo8ePfrBBx+MRUgax+7/PQCUgVJE87799tvHHnuMz+fb2NgEBAR0dXUlJydv3ry5vr6e\noigfH5+srCxLS0sTE5PZs2c7OTmZmZlZWlrOmjUrNDR0woQJXC7X1tb2tddekwx4+vTpUc5dPWfO\nnJ6enieeeOL7778f5dY98cQTU6ZMOXv27PXr1yULv//++97e3kWLFo1ycAC9oPFjfDSQHwDUMOyY\nJYScO3fO399fIBBwudyAgIAzZ84w7xwaGsrIyPDz8+PxeOPHj/f09MzIyFi1ahXTOjIbEBWPypyc\nHEtLSz6fX1JS8vTTT9vY2Li5uR05coRpzc7O5nK5jo6O8fHxLi4uXC43ODi4srKSaU1MTDQ3N3d2\ndmZebtiwwdLSkqKo9vZ2mduoDJn74aWXXmJuMvH29r58+TIh5IUXXuDz+QKB4Pjx48xeev31193d\n3Xk83vTp05lLrd59910+n29tbd3a2rp582ZXV1fpzADAQCmiYT09PREREVFRUR0dHbW1tb6+vgMD\nA1lZWUuXLvX29qZpuq6uLjk5+dVXX6Vp+sCBAw0NDf/85z/nz59/+fLllJSUy5cvd3R0/PnPf969\ne/eVK1eYMZnZHsRisdpRvfbaa4GBgVeuXAkJCZk6deq77747mimx4+PjCSHSt/C+9957mzZtUntA\nAD0yFsf4aCA/AKhh2DFLCGlpaYmOjm5sbGxubrayslqzZg3zznfe+f/s3WlAU1f+N/ATICSAICCg\nLKIgbggKVf8CotZdQVYJ0OpUrLWItuAy41YXpEpBO0pxqUut7WgViFhQXHChVFFxqSKCowKKIKiA\nCGEJEkieF/eZTIZdCLkh+X5emXtuzv3dxHO4v9xz7onctGnTjh07ysvLL168WFdXp6urq6urS1rp\nDcgHtsqlS5euWLGCz+dra2vHxsbm5eVZWlouXryYWvY7ODg4ICCgtrY2JCQkPz//3r17DQ0N06dP\np1a6iI6OFidFhJC9e/du2bKljXPsiBY/h59++mnu3LmqqqrXrl2zt7cnhBw5csTLy+vYsWPu7u6E\nkLVr127fvn3Xrl2vXr1yc3P79NNP7969u3r16pUrV1ZXV4eHh1tYWDg4OIg6tmwFKBWkIlKWn5/P\n4/FGjBjBZrP79u0bHx9vYGDQ2s7W1taampp9+vT55JNPCCHm5uYGBgaamprUcy0eP35M7ebq6srj\n8Vp7Lk1HaGho3Lhx44cffhg2bNijR4/WrFkzfPjwP//8s3O1LViwQEtL69dff6XW2Xn27NmdO3c+\n/fTTTocH0IN0RxvvCvQPAFLh4+OzefNmPT09fX19d3f3t2/flpaWEkISEhJGjx7t7u6uoaHx0Ucf\neXh4XL16lUo5WusNOtcqnZycdHR0DA0N/f39a2pqCgoKxEVqamrDhw9nsVjW1tb79u2rqqo6cuSI\nVM/+v1r7HIKCghobG8XH5fF4d+7ccXFxIYTU1dXt27fPy8tr7ty5urq6GzZsYDKZkhFGRER89dVX\n8fHxw4YN66awoedSozsARWNpaWlkZDR//vyQkJCAgICBAwd25F3UIx0aGhqol9Qj0qhfRKSFyWQG\nBwcHBwffunUrIiIiISGBw+E8efJET0/vQ6vq3bv3p59+eujQoZiYmIULF+7atWvp0qXq6upU1/xB\ndu3axeVyP/RdPUt6ejohhMPh0B0ISIfctvGukMP+IT09XeFbzcuXLwk6Bwnp6ekODg50R0HIf1oo\ndXOjrq6OzWaLixobG5lMJjVzt3O9Qbuo7qK1/mHMmDGamppS+SGjXZKfw5QpU4YMGfLzzz+vX7+e\nwWDExMT4+/tTn8OTJ09qa2ttbGyod2loaPTr16/TESpJi1CG658Owl0RKdPQ0EhJSXF2dt62bZul\npaW/vz/106D8GDdu3O+//x4UFFRaWvrHH390rhJqcur+/fsrKiq4XC41JANAGch/G+8K9A+gtM6e\nPfvxxx8bGhqyWCzJqVwuLi5//fVXYmIin8+/e/duQkLCnDlzqEtwunoDFotF3anoDq19DgwGY8mS\nJc+ePbty5Qoh5F//+teiRYuoopqaGkLIhg0bxMsNvXjxora2tpsiBAWDuyLSN2LEiDNnzpSWlu7c\nuTMiImLEiBFdGTvRaVevXv3rr79WrFhBCJk7d25sbKz4WZyEkL/97W8//vhjp3sKOzs7BweH9PT0\nwMBADofTiZ9OKStWrJAc56qQqB948OOHGIPBoDuErpKTNt4V8t8/ODg4KHyriYuL8/PzU/jT7Dga\nfw4vKCjw8vLy9vb++eefTUxMdu/eLb4KDw0N/euvvwICAqqrq42NjX19fSXno8u+NxAIBBUVFWZm\nZlKsU9whtPE5EEICAgLWr1//008/9e/fX0dHh1osiBBiaGhICNm1a9fy5cu7HowytAgGg6EM1z8t\nan4NgFREyoqLiysqKqytrQ0NDb/77ruLFy8+evSIlkj++usvLS0t6t/v379/9OjRyJEjxaXUUywk\nt3yopUuXpqennzx5klr9DUBJyE8b7wr0DwCSHj58KBAIli5damlpSf73aik7OzsvL6+0tFQyXafQ\n0hukpqaKRCLxSDY1NbWuD/UUdwhtfA6EED09PT8/v5iYGG1t7cWLF4u3Uw8GzMjI6GIYoJwwQEvK\niouLlyxZ8vjx4/r6+vv377948YLqL/T19YuLi/Pz86uqqj601zh//vwHPaxTIBC8efMmNTVVfKlB\nCPHy8oqLi6uoqKisrExMTFy7dq2Hh0dXLjV8fX0NDAy8vLyoDgtASXRHG+8K9A8AnSPZZo2NjQkh\nly9frqury8nJET8tlxDy1VdfmZubV1dXN6+htd7gQ1tlu4RC4bt37xoaGjIzM5cvX25ubh4QEEAV\nWVlZlZeXJyQkCASC0tLSFy9etHaOLfZLTToEc3Pz1j4HSlBQ0Pv375OSktzc3MQb2Wz2woULT5w4\nsW/fPh6P19jY+PLly1evXknr9EHBdWXxdmVAmi1Q38Q///nPvn37EkK0tLS8vb3z8/OdnJz09PRU\nVVVNTEy++eabhoYGkUh07969AQMGaGhoODs7r1u3jloybODAgdeuXYuIiOjduzchpG/fvr/99ltM\nTAxVoZ6e3okTJ0Qi0blz57S1tbdu3dr86KdOnRo0aFBrX+6pU6eo3S5evOjn5zdo0CAWi6Wurj50\n6NDQ0NC6ujrJqng83sSJE/X19QkhKioqVlZW27Zta34gAwODr776itq4evXqGzduUP/esGED9Whz\nFRUVa2vra9eudf2zVQw+Pj4+Pj50RyFH5Op778i3I4M23rabN2+OHz+eulQihPTr18/JyenPP/+k\nShWyf1CSVkOtvUB3FHJEit97R/oZyTb7+vXrNWvW6Ovr6+rqcjicPXv2EEIGDRpUUFCQkpLSp08f\nccNhMpnDhw+Pj48XiUSt9QZttMr09PQRI0aoqKhQbXnbtm179+6luovBgwfn5eUdPHhQR0eHEDJg\nwICnT5+KRKLAwEAmk2lqaqqmpqajo+Pp6ZmXlyeu8O3bt5MnT2az2RYWFl9//fU//vEPQoiVlVVB\nQUGTc/zxxx870iG09jmIj2hvb79u3bom5/X+/fs1a9aYm5urqakZGhrOnTs3Ozs7MjJSQ0ODENK/\nf/+jR4+2+60pT4uQq7+DMtb83JXiK+8KZf7v0t2U5LNVkouqjpOr7x3fjnxSku9FeS68OkjGqUgH\n7d27d/ny5eKX79+/X7FiBYvFqq2tlUr97QoMDNTX15fNsTrCxcXl2bNn3VGz8rQIufo7KGPNzx1z\nRQAAAABa8Pr16+DgYMlZEOrq6ubm5gKBQCAQUD/5ywD1OF0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19X79+lEvly1b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\nCAAAAAAAaAG6IgAAAAAAoAX/Bzv6uaf2HSyPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIPSt1a8RLrx",
        "colab_type": "text"
      },
      "source": [
        "## 12.2 Redes neuronales preentrenadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zLYV__vRTvM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "\n",
        "\n",
        "input_shape = (224, 224, 3)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(64, (3, 3), input_shape=input_shape, \n",
        "                 padding='same',activation='relu'))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', padding='same',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(256, (3, 3), activation='relu', padding='same',))\n",
        "model.add(Conv2D(256, (3, 3), activation='relu', padding='same',))\n",
        "model.add(Conv2D(256, (3, 3), activation='relu', padding='same',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same',))\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same',))\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same',))\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same',))\n",
        "model.add(Conv2D(512, (3, 3), activation='relu', padding='same',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(4096, activation='relu'))\n",
        "model.add(Dense(4096, activation='relu'))\n",
        "model.add(Dense(1000, activation='softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoRs6lyaRTx9",
        "colab_type": "code",
        "outputId": "2e86bf8a-0dc7-43c6-c9c6-b92cc6809131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_19 (Conv2D)           (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_21 (MaxPooling (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_22 (MaxPooling (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_23 (MaxPooling (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_24 (MaxPooling (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_30 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_31 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_25 (MaxPooling (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 138,357,544\n",
            "Trainable params: 138,357,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pagWnJ_vS-hx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.applications import VGG16\n",
        "\n",
        "model = VGG16(weights='imagenet')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GJzabxWTKqt",
        "colab_type": "code",
        "outputId": "aa961b5e-8d92-4e49-d391-1305fc17f507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 950
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_10 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 138,357,544\n",
            "Trainable params: 138,357,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAam-RtATv0d",
        "colab_type": "text"
      },
      "source": [
        "CIFAR workload"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhxIce_8TW36",
        "colab_type": "code",
        "outputId": "fa93151b-9fa2-42e2-cc73-3fa80f5225c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "from tensorflow.keras.datasets.cifar10 import load_data\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = load_data()\n",
        "# Normalize pixel values to be between 0 and 1\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0\n",
        "\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "\n",
        "    plt.xlabel(class_names[train_labels[i][0]])\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G/vLyc99K7Ic/jMzfuT4F//2haA1AiYgVS+1eF3NFQDKOz2OsgznJIU22VkWeORVLIppk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\nijY2kJPL66Aibi5fSezLV3DOww8i6OW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6b+Vjacih2uEv1AzJcA3K3cTeZynyRMzGkvPA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1vdHEHGyTYpafXZ43WXrLiBE/UTbux76MKbmEFXp4ZvToHhtJ/QcO2vfKLYzV5WX0TXWa1cxY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\n2hRuLNqDFbPjIJXNmJKWFUJYYzo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z8OfsyxS8vUfTu8VgGMq123FoOutjA/9MFR4uU9SG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\nwbAhYC89BoPBYDAYNgT+H3v+xg9i5SjaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RxYtoW5Fp55",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_history(history):\n",
        "  val_loss = history.history['val_loss' ]\n",
        "  loss =     history.history['loss' ]\n",
        "  acc =      history.history['accuracy' ]\n",
        "  val_acc =  history.history['val_accuracy' ]\n",
        "\n",
        "  epochs    = range(1,len(acc)+1,1)\n",
        "\n",
        "  plt.plot  ( epochs,     acc, 'r--', label='Training acc'  )\n",
        "  plt.plot  ( epochs, val_acc,  'b', label='Validation acc')\n",
        "  plt.title ('Training and validation accuracy')\n",
        "  plt.ylabel('acc')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "\n",
        "  plt.plot  ( epochs,     loss, 'r--', label='Training loss' )\n",
        "  plt.plot  ( epochs, val_loss ,  'b', label='Validation loss' )\n",
        "  plt.title ('Training and validation loss')\n",
        "  plt.ylabel('loss')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN4Or3FDUzAS",
        "colab_type": "text"
      },
      "source": [
        "RESNET50 sin preentreno"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHo-Hh-R9tI2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS=10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY1WVcucUmP_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modelresnet50v2 = tf.keras.applications.ResNet50V2(include_top=True, weights=None,\n",
        "            input_shape=(32, 32, 3), classes=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnCEJRP-UmSy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#modelresnet50v2.summary()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3aIanZGKq39",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#keras.utils.plot_model(modelresnet50v2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFCM4Z-UUmXX",
        "colab_type": "code",
        "outputId": "e349af44-f77f-48fc-81b2-fcc90470bdd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "opt = tf.keras.optimizers.SGD(0.002)\n",
        "\n",
        "modelresnet50v2.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=opt,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = modelresnet50v2.fit(train_images, train_labels, epochs=EPOCHS, \n",
        "          validation_data=(test_images, test_labels))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 90s 2ms/sample - loss: 2.0170 - accuracy: 0.2737 - val_loss: 1.7832 - val_accuracy: 0.3588\n",
            "Epoch 2/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 1.6503 - accuracy: 0.4035 - val_loss: 1.5201 - val_accuracy: 0.4491\n",
            "Epoch 3/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 1.4999 - accuracy: 0.4592 - val_loss: 1.5671 - val_accuracy: 0.4437\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 1.3989 - accuracy: 0.4964 - val_loss: 1.4471 - val_accuracy: 0.4945\n",
            "Epoch 5/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 1.3086 - accuracy: 0.5302 - val_loss: 1.3835 - val_accuracy: 0.5017\n",
            "Epoch 6/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 1.2255 - accuracy: 0.5617 - val_loss: 1.3119 - val_accuracy: 0.5377\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 1.1615 - accuracy: 0.5867 - val_loss: 1.3408 - val_accuracy: 0.5213\n",
            "Epoch 8/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 1.0869 - accuracy: 0.6151 - val_loss: 1.2254 - val_accuracy: 0.5649\n",
            "Epoch 9/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 1.0232 - accuracy: 0.6383 - val_loss: 1.2761 - val_accuracy: 0.5444\n",
            "Epoch 10/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.9739 - accuracy: 0.6548 - val_loss: 1.2103 - val_accuracy: 0.5725\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mfp07Fh1Umav",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "24a04cd6-710f-4ec6-83c6-efa1e1894822"
      },
      "source": [
        "plot_history(history)\n",
        "\n",
        "test_loss, test_acc = modelresnet50v2.evaluate(test_images,  test_labels, verbose=2)\n",
        "print (\"Test Accuracy:\", test_acc)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 - 5s - loss: 1.2103 - accuracy: 0.5725\n",
            "Test Accuracy: 0.5725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZZ7FkyRKKFYt1M5TxvyuXr0yfDnffbdXO+/fbl3/r1vDOO3DiibGO7rCIyExVTQy3Lz6+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\nB1xoZ2j3dxS2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u3VzcqataR0D79jaEKQ9SU+HFF21LzthYGDHCOr29duBc5PKmoSLgjz9se8uq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\nnIty/w+V3G81M0XTtgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLWifY7aVDKO",
        "colab_type": "text"
      },
      "source": [
        "RESNET50 with pretrained weight from imagenet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X13717DtU_A_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modelresnet50v2pre = tf.keras.Sequential()\n",
        "\n",
        "modelresnet50v2pre.add(tf.keras.applications.ResNet50V2(include_top=False, weights='imagenet',\n",
        "            pooling='avg', input_shape=(32, 32, 3)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLUY7Yv7U_Dg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#modelresnet50v2pre.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qoCokG3U_GJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modelresnet50v2pre.add(layers.Dense(10, activation=\"softmax\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGKVwZ5GU_Md",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#modelresnet50v2pre.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNfU72f0VO5e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "6eecda6b-04b0-415b-c72e-876b76937f56"
      },
      "source": [
        "opt = tf.keras.optimizers.SGD(0.002)\n",
        "\n",
        "modelresnet50v2pre.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=opt,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = modelresnet50v2pre.fit(train_images, train_labels, epochs=EPOCHS, \n",
        "          validation_data=(test_images, test_labels))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 91s 2ms/sample - loss: 1.7905 - accuracy: 0.3788 - val_loss: 1.3926 - val_accuracy: 0.5518\n",
            "Epoch 2/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 1.2591 - accuracy: 0.5742 - val_loss: 1.2707 - val_accuracy: 0.6422\n",
            "Epoch 3/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 1.0570 - accuracy: 0.6452 - val_loss: 1.0631 - val_accuracy: 0.6761\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.9533 - accuracy: 0.6800 - val_loss: 0.9741 - val_accuracy: 0.6960\n",
            "Epoch 5/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.8588 - accuracy: 0.7119 - val_loss: 0.9620 - val_accuracy: 0.7150\n",
            "Epoch 6/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.7889 - accuracy: 0.7328 - val_loss: 1.0046 - val_accuracy: 0.6625\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.7258 - accuracy: 0.7550 - val_loss: 0.8461 - val_accuracy: 0.7397\n",
            "Epoch 8/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.6420 - accuracy: 0.7838 - val_loss: 1.1803 - val_accuracy: 0.7401\n",
            "Epoch 9/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.5901 - accuracy: 0.8012 - val_loss: 0.7850 - val_accuracy: 0.7519\n",
            "Epoch 10/10\n",
            "50000/50000 [==============================] - 87s 2ms/sample - loss: 0.5355 - accuracy: 0.8200 - val_loss: 0.8531 - val_accuracy: 0.7357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zj8GSdIVO-V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "62c90da2-a47b-4d45-f8a9-e631d3995e28"
      },
      "source": [
        "plot_history(history)\n",
        "\n",
        "test_loss, test_acc = modelresnet50v2pre.evaluate(test_images,  test_labels, verbose=2)\n",
        "print (\"Test Accuracy:\", test_acc)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 - 5s - loss: 0.8531 - accuracy: 0.7357\n",
            "Test Accuracy: 0.7357\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KUSwsI8OGjujWDU4+OYoFOedcwSRsG8H+/dZWm5ZmE9DXqXP0zxyX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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BR+RceP34IzRsCOecAwkJ8NRT1kVUv37QkeVtIjJfVRPTey6zLYIXgF2hRV+3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\nxThPBM45F+M8ETjnXIzzROCcczHu/wHgvZN3lea4EwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLQQfCJLWUgP",
        "colab_type": "text"
      },
      "source": [
        "VGG19"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdfl5oX8V4Zy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "5273074c-77de-4a50-9c6a-ccbcd83a3c3f"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "\n",
        "model.add(tf.keras.applications.VGG19(include_top=False, weights='imagenet',\n",
        "            pooling='avg', input_shape=(32, 32, 3)))\n",
        "model.add(layers.Dense(10, activation=\"softmax\"))\n",
        "\n",
        "opt = tf.keras.optimizers.SGD(0.002)\n",
        "\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=opt,\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history= model.fit(train_images, train_labels, epochs=EPOCHS, \n",
        "          validation_data=(test_images, test_labels))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 1.0229 - accuracy: 0.6406 - val_loss: 0.8570 - val_accuracy: 0.7075\n",
            "Epoch 2/10\n",
            "50000/50000 [==============================] - 86s 2ms/sample - loss: 0.6659 - accuracy: 0.7682 - val_loss: 0.6591 - val_accuracy: 0.7702\n",
            "Epoch 3/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.5556 - accuracy: 0.8062 - val_loss: 0.6154 - val_accuracy: 0.7857\n",
            "Epoch 4/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.4803 - accuracy: 0.8308 - val_loss: 0.7036 - val_accuracy: 0.7637\n",
            "Epoch 5/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.4235 - accuracy: 0.8512 - val_loss: 0.5667 - val_accuracy: 0.8073\n",
            "Epoch 6/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.3760 - accuracy: 0.8677 - val_loss: 0.6189 - val_accuracy: 0.7935\n",
            "Epoch 7/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.3291 - accuracy: 0.8843 - val_loss: 0.5119 - val_accuracy: 0.8253\n",
            "Epoch 8/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.2872 - accuracy: 0.8998 - val_loss: 0.5636 - val_accuracy: 0.8148\n",
            "Epoch 9/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.2525 - accuracy: 0.9117 - val_loss: 0.5693 - val_accuracy: 0.8179\n",
            "Epoch 10/10\n",
            "50000/50000 [==============================] - 85s 2ms/sample - loss: 0.2186 - accuracy: 0.9235 - val_loss: 0.6754 - val_accuracy: 0.8064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E37nfAxWaIT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "9093127f-0e83-446e-dd96-7673240525eb"
      },
      "source": [
        "plot_history(history)\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "print (\"Test Accuracy:\", test_acc)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 - 4s - loss: 0.6754 - accuracy: 0.8064\n",
            "Test Accuracy: 0.8064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SHPqgqo6OZazOuSLgqafgo4/gq6/gjz9s28CBliBq1oQ1a6BOnXBjLERiNlBOREoCzwJn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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x27b2vxAJngjyQaVK1j1y+YbSPHrHj9CuXdAhhU1Kig2gq1PH2gSefRa++MJ6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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbtcvaNkWaRP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}