1-CNN-Teoria.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Redes Neuronales Convolucionales (CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![cnn.jpeg](imgs/cnn.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- La meta es reducir el ancho/alto y aumentar la profundidad\n",
    "- Transformar información espacial en features (feature maps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Videos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro CNNs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/HrYNL_1SV2Y\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f77f7118430>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('HrYNL_1SV2Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capas convolucionales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/h5R_JvdUrUI\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f77f71181f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('h5R_JvdUrUI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/RnM1D-XI--8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f77f71180d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('RnM1D-XI--8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capas pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/OkkIZNs7Cyc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f77f83cef40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('OkkIZNs7Cyc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN en clasificación de imágenes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/l9vg_1YUlzg\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f77f7118070>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('l9vg_1YUlzg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capas en las CNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capa convolucional:\n",
    "**Su función es aumentar la profundidad (espacialidad --> features)**\n",
    "![convolve.png](imgs/convolve.png)\n",
    "- [Ver Ejemplo Auto](https://github.com/udacity/aind2-cnn/blob/master/conv-visualization/conv_visualization.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparámetros\n",
    "- Para aumentar la dimensionalidad de una CNN: Aumento la **cantidad de filtros**\n",
    "- Para incrementar el tamaño/complejidad de los patrones detectados: Aumento el **tamaño del filtro**\n",
    "- **Stride**: Paso. Determina el tamaño de las salidas del siguiente nivel\n",
    "- **Padding**: ¿Qué hacemos con los bordes? 'same': mantiene la dimensión de la entrada; 'valid': no padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/Qt5SQNcQfgo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f77f761b4f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('Qt5SQNcQfgo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como usamos esto en [keras](https://keras.io/layers/convolutional/)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Convolution2D, Dense, Flatten, MaxPooling2D, GlobalAveragePooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julian/opt/anaconda3/envs/cnns/lib/python3.9/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, input_dim=784, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m78,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">79,510</span> (310.59 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">79,510</span> (310.59 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer='Adam',\n",
    "    metrics=['accuracy'],\n",
    "    loss='categorical_crossentropy'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73856"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*3*64*128 + 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julian/opt/anaconda3/envs/cnns/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,432</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,616</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">102,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m640\u001b[0m, \u001b[38;5;34m480\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m2,432\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m240\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m160\u001b[0m, \u001b[38;5;34m120\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m80\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m40\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)     │     \u001b[38;5;34m1,180,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m1024\u001b[0m)    │     \u001b[38;5;34m4,719,616\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │       \u001b[38;5;34m102,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,393,238</span> (24.39 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,393,238\u001b[0m (24.39 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,393,238</span> (24.39 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,393,238\u001b[0m (24.39 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Convolution2D(filters=32, strides=2, kernel_size=5, padding='same',\n",
    "                        activation='relu',\n",
    "                        input_shape=(2*640, 2*480, 3)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2), strides=None, padding='valid'))\n",
    "model.add(Convolution2D(filters=64, strides=2, kernel_size=3, padding='same',\n",
    "                        activation='relu',\n",
    "                        ))\n",
    "model.add(Convolution2D(filters=128, strides=2, kernel_size=3, padding='same',\n",
    "                        activation='relu',\n",
    "                        ))\n",
    "model.add(MaxPooling2D(pool_size=(2,2), strides=None, padding='same'))\n",
    "model.add(Convolution2D(filters=256, strides=2, kernel_size=3, padding='same',\n",
    "                        activation='relu',\n",
    "                        ))\n",
    "model.add(Convolution2D(filters=512, strides=2, kernel_size=3, padding='same',\n",
    "                        activation='relu',\n",
    "                        ))\n",
    "model.add(Convolution2D(filters=1024, strides=1, kernel_size=3, padding='same',\n",
    "                        activation='relu',\n",
    "                        ))\n",
    "\n",
    "# model.add(Flatten())\n",
    "model.add(GlobalAveragePooling2D())\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2432"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*5*5*32 + 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hacer pruebas y explicar 'Output Shape' y 'Param #':\n",
    "- filters=1, strides=1, kernel_size=1, padding='same'\n",
    "- filters=16, strides=1, kernel_size=1, padding='same'\n",
    "- filters=1, strides=1, kernel_size=4, padding='same'\n",
    "- filters=10, strides=1, kernel_size=4, padding='same'\n",
    "- filters=1, strides=2, kernel_size=5, padding='same'\n",
    "- filters=1, strides=2, kernel_size=5, padding='valid'\n",
    "- filters=16, strides=2, kernel_size=5, padding='same'\n",
    "- filters=16, strides=4, kernel_size=4, padding='same', input_shape=(32,32,1)\n",
    "- filters=16, strides=4, kernel_size=4, padding='same', input_shape=(32,32,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolucionales vs Densas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mirar cantidad de **parámetros** y **Output Shape**\n",
    "\n",
    "Las CNN Reducen la cantidad de **parámetros** y la **cantidad de conexiones**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si hago una analogía con una capa densa, ¿cuántas neuronas tengo a la salida en una CNN?\n",
    "- Multiplico las dimensiones del output-shape\n",
    "\n",
    "Supongamos una capa densa de 1250 neuronas con una entrada de una imagen de 28x28x1. ¿Cuántas conexiones tengo y cuántos parámetros?\n",
    "- 784x1250 = 980.000 conexiones\n",
    "- 784x1250 + 1250 = 981.250 parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1250</span>)     │       <span style=\"color: #00af00; text-decoration-color: #00af00\">981,250</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1250\u001b[0m)     │       \u001b[38;5;34m981,250\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">981,250</span> (3.74 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m981,250\u001b[0m (3.74 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">981,250</span> (3.74 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m981,250\u001b[0m (3.74 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(1250, input_shape=(None,784)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para una capa **convolucional** con la misma entrada y la misma cantidad de neuronas a la salida?\n",
    "- Claramente depende del tamaño del kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m2\u001b[0m)      │            \u001b[38;5;34m34\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34</span> (136.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m34\u001b[0m (136.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34</span> (136.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34\u001b[0m (136.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Convolution2D(filters=2, \n",
    "                        strides=1, \n",
    "                        kernel_size=4, \n",
    "                        padding='valid',\n",
    "                        activation='relu',\n",
    "                        input_shape=(28,28,1)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos un kernel de 4x4 con 2 filtros y padding valid:\n",
    "- (4x4 conexiones por neurona) x (25x25x2 Neuronas) = 20.000 conexiones\n",
    "- (4x4)x2 + 2 = 34 parametros\n",
    "\n",
    "La poca cantidad de parámetros respecto a conexiones se debe obviamente a que comparte parametros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se podria entonces hacer que una capa densa tenga la misma salida que una convolucional usando valores de pesos convenientes.\n",
    "\n",
    "La diferencia fundamental es que el aprendizaje es diferente ya que:\n",
    "- Varios de los pesos deberian quedar en cero y no ser modificados por el algoritmo de aprendizaje\n",
    "- Varios de los pesos, los compartidos, deberián moverse iguales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entendiendo la salida y los pesos\n",
    "Una vez que aprendió los parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_simple_model(activation = None, n_filters = 6, input_shape=(28,28,1)):\n",
    "    model = Sequential()\n",
    "    model.add(Convolution2D(filters=n_filters, \n",
    "                            kernel_size=3, \n",
    "                            input_shape=input_shape, \n",
    "                            name = 'Conv1',\n",
    "                            padding= 'valid',\n",
    "                            activation=activation\n",
    "                           ))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ Conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">220</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ Conv1 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m4\u001b[0m)      │           \u001b[38;5;34m220\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">220</span> (880.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m220\u001b[0m (880.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">220</span> (880.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m220\u001b[0m (880.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor de pesos: (3, 3, 6, 4)\n",
      "Biases: (4,)\n"
     ]
    }
   ],
   "source": [
    "canales_entrada = 6\n",
    "model = get_simple_model(n_filters = 4, input_shape=(28,28,canales_entrada))\n",
    "model.summary()\n",
    "print('Tensor de pesos:', model.get_weights()[0].shape)\n",
    "print('Biases:', model.get_weights()[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()[1].shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modificar los canales de entrada y ver que:\n",
    "- El Output Shape no cambia\n",
    "- La tercer dimensión del tensor de pesos cambia (y por supuesto la cantidad de parámetros)\n",
    "\n",
    "Es decir, la dimensión de los pesos es:  \n",
    "**ancho x alto x canales x filtros**\n",
    "\n",
    "Modificar tambien la cantidad de filtros para verificar como se cambia la ultima dimensión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analizando la salida filtrada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ Conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)      │            <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ Conv1 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m6\u001b[0m)      │            \u001b[38;5;34m60\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">60</span> (240.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m60\u001b[0m (240.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">60</span> (240.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m60\u001b[0m (240.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor de pesos: (3, 3, 1, 6)\n",
      "Biases: (6,)\n"
     ]
    }
   ],
   "source": [
    "canales_entrada = 1\n",
    "model = get_simple_model(n_filters = 6, input_shape=(28,28,canales_entrada))\n",
    "model.summary()\n",
    "print('Tensor de pesos:', model.get_weights()[0].shape)\n",
    "print('Biases:', model.get_weights()[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.13626508 -0.04820126  0.09733915]\n",
      " [ 0.14677659 -0.10664614  0.15789062]\n",
      " [ 0.133237    0.12917045  0.14867961]]\n",
      "[0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Matriz de 3x3 inicializada aleatoriamente\n",
    "print(model.get_weights()[0][:,:,0,2])\n",
    "# Biases inicializados en cero\n",
    "print(model.get_weights()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from CNN_analysis_helper import plot_filter_coefs, plot_conv_filters, generate_random_image, plot_activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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n5OvrKxcXF73yyiu1pp9YWqdPnTqlyMhI6+enTp3S9ddfb7NuWFiYwsLCdPXVV6tly5Zq0KCBtm3bpo4dOzoyZIdztjGHudIil+nYsWM6e/astbLm5ORIUplsj6urq+neEu5oRUVFysvLs/58/PhxdenSRe3atVNCQoLpMmT2kJOTU+Y43dzcbOpG6UTsr7/+qs8//1yhoaGODhNV4Onpqfbt2+vAgQM2n//yyy+Kjo4us358fLz69OmjunXrOipEw/n6+ioyMlJpaWnasGGD9YVQ1P2SQf/o0aO1evVqbdy4UY0bN7ZZfrFzzJ/bEZibxWKRxWKp8HvMzMxUbGysPD09tXbt2jLvWHFmlSmjdu3aycPDQ1988YV1+YEDB3TkyBGnHwyUqmxdKhUWFqagoCBt3LhRp0+fdtoJKWfh6empdu3a2dTxoqIiffHFF7Wmjl+JC8ccHTt2VHp6unbu3GldvnHjRhUVFalDhw5GhVjtKuo/SBWfU1auXKkffvhBu3fv1u7du/Xvf/9bUsmE96hRoxxyHKi8IUOG6Mcff7R+X7t371ZUVJQmTpyoDRs22KxbXFyshIQEDR061OkuaKhM3S/vd4qLi23mJvbt26euXbtq2LBhmjlzpj1DNq2srCwdOnRIkZGR9LGc3J/n5krVq1dPfn5+ev/99+Xt7a3bb7/dgOgcr3HjxoqIiLCp75mZmdq+ffsl63tpX7u8snQ2zjbmMNUdEVlZWTp48KD158OHD2v37t0KCQlRSEiInn32WfXv318RERE6dOiQHn/8cTVr1kw9evSQVNLZDQ4O1rBhw/TMM8/Ix8dHb731lg4fPmzNGjmDS5VTaGioZs6cqT59+igyMlIpKSlasGCBjh8/rgEDBkj6XxIiOjpac+bM0ZkzZ6zbcuYMe+/evTVz5kw1bNhQ1157rb7//nu98soreuihhySV/HHffffd2rVrl9atW6fCwkLrM+lCQkIu+hxLONal6n/Dhg01ceJE3XPPPercubO6du2qxMREffzxx9ZHuJU6ePCgvvzyS33yyScOPgJjbNiwQcXFxWrevLkOHjyoiRMnqkWLFnrwwQep+//fqFGjtGzZMq1Zs0b+/v7WMggMDJSPj49atGihZs2aaeTIkZozZ45CQ0P10UcfKSkpSevWrTM4elyoonbi1ltv1cSJE+Xj46Po6Ght2bJF77zzjl555RVJ/5swysnJ0dKlS5WZmanMzExJJbe9urm5GXJc1amqZRQYGKjhw4drwoQJCgkJUUBAgMaMGaOOHTvq5ptvNuqwql1Vy0kquRK2ZcuWqlu3rrZu3ap//OMfGj9+vJo3b27EIeEyTJgwQcOGDdONN96om266SfPmzVN2drZTv2z2QlUdc7Rs2VJxcXEaMWKEFi1aJIvFotGjR+vee+9VVFSUUYdV7SrqP1TmnNK0aVObbaakpEgqKcM/PycejlFR+//ni3Y8PDwUERFRpm3fuHGjDh8+rIcfftghcTtSRXX/t99+0/vvv6/Y2FjVrVtXx44d0+zZs+Xj46M77rhDUsnjmG677Tb16NFDEyZMsG7Dzc3NqS8W++c//6nevXsrOjpaycnJmjp1qtzc3DRo0KBa08dyBlU9T0rS/PnzFRMTIz8/PyUlJWnixImaPXu2U7X9FbWn48aN04wZM3TVVVepcePGmjJliqKiotSvXz9J0vbt27Vjxw516tRJwcHBOnTokKZMmaKmTZs6TXKuNo05TJWI+O6779S1a1frzxMmTJAkDRs2TAsXLtSPP/6oJUuWKD09XVFRUYqNjdVzzz1nfSt4WFiYEhMT9dRTT+m2226TxWLRtddeqzVr1ljfSu8MLlVOixYt0v79+7VkyRKlpKQoNDRU7du311dffWV9lEhSUpIOHjyogwcPlrl10pmfr/baa69pypQp+vvf/67Tp08rKipKI0eO1DPPPCOpJEGzdu1aSSpzC9imTZvUpUsXB0eM8lyq/i9evFh//etftWjRIs2aNUtjx45V8+bNtXLlSnXq1MlmO2+//bbq16/v9M8fLZWRkaHJkyfr2LFjCgkJUf/+/TVz5kx5eHjo999/p+5L1ndi/Pl4ExIS9MADD8jDw0OffPKJJk2apN69eysrK0vNmjXTkiVLrIMpmENF7cTy5cs1efJkDR48WKmpqYqOjtbMmTP16KOPSpJ27dql7du3S1KZF5EfPnxYjRo1csyB2FFVy0iS5s6dK1dXV/Xv3195eXnq0aOHXn/9dYcfiz1VRzkdOHBAkydPVmpqqho1aqSnnnpK48ePd/ix4PLdc889OnPmjJ555hmdPHlS119/vRITE8u8UNFZVXXMIUnvvfeeRo8erW7dulnbi1dffdXhx2JPFfUfasM5xRlV1P5XVnx8vGJiYtSiRYvqDtFwFdV9b29vffXVV5o3b57S0tJUr149de7cWd988431RfYffvihzpw5o6VLl2rp0qXWbURHR5d5xJUzOXbsmAYNGqSzZ8+qbt266tSpk7Zt22ZNvtSGPpYzqI7z5LfffqupU6cqKytLLVq00BtvvKEhQ4Y4/FjsqaL29PHHH1d2drYeeeQRpaenq1OnTkpMTLTePVinTh2tWrVKU6dOVXZ2tiIjIxUXF6enn37aOh9c09WmMYdLcSVnnhMSEqq8M7M/F3Ht2rU6e/ZslbZx/PjxKscRERFh2ism/v3vf5d5c/2V2LVrVzVEY6tJkyaaM2dOtW+3Mv75z3/qt99+q/btrlq1qtq3aU8XezHf5QgODrbe5WQ2GzZsUFpaWpW3s379+mqIxlZ0dLRmzJhR7dutjKefflp//PFHtW/3z3exVIfrrrtOn376abVvtzJ69uypvXv3Vvt2jx49Wu3btJfqKvuAgADdcsst1bKt6vbf//7XeqXrlSosLKxyHAEBAercuXOVt2MvX375ZZXLqU6dOlWOw8/PTzfddFOVt2Mv3377rbKysqq0jdtuu62aonGMyrz4tTq0bdvWLv3RG264Qd9//321b/fPpk+fXuVtREREaMSIEdUQjX289dZbVR53VMd7wDw9PW2eW20mJ06cUH5+fpW3U94jTM1s/vz5Vd5G3bp1dc8991RDNPbx/vvv2zy54EpceNX1lXJ3dzftI1vPnj2rgoKCKm/HHhcUtWjRQu+99161b7cyBg8erP3791f7di98zF5NUB1zQ+Hh4aZ9n+Q777xT5j1MV+LCq/CvVIMGDfTUU09VeTv2MHPmzGoZLw8cOLBKv18bxhxS5cYdzv9iAAAAAAAAAAAAYBgSEQAAAAAAAAAAwG5IRAAAAAAAAAAAALshEQEAAAAAAAAAAOyGRAQAAAAAAAAAALAbEhEAAAAAAAAAAMBuSEQAAAAAAAAAAAC7cWgi4vz58yooKHDkLiutoKBA58+fNzoMSVJOTo4py6mgoEA5OTlGh3FRmZmZys/Pd/h+8/PzlZmZ6fD9Oqu8vDwVFhYaHUYZhYWFysvLMzqMi8rKyjKs/mdlZTl8v1cqNTXVkLb+/PnzSk1Ndfh+nVV+fr5p2wkj/g7LY9YyksxVThaLxdTlZLFYjA7DaaWkpCg3N7dat5mbm6uUlJRq3aY95eTkmLaOWSwW04w7CgsLVVRUZHQYZRQVFZm2/aoJcnNzTTnmlkrG3dXdPl2poqIiFRcXGx1GGcXFxab8uyyVnp5uyNgxLy9P6enpDt+vszJrO2GmNkIqmYswY3/CYrGYZq6CMcf/uDtsT5Kys7O1atUqeXt7O3K3lXL+/HllZ2cbHYakkgn1119/XXXq1DE6FBs5OTmmnnBPSUnR6NGjFRAQ4ND9ZmZm1qhBp9nl5ORo/fr18vLyMjoUG3l5eaYZEJfn7Nmzevzxx+Xn5+fQ/WZlZens2bMO3WdVJCcn69Zbb1VISIhD95uamqrk5GSH7tOZnT9/Xl9++aU8PT2NDsVGfn6+aS5qOH/+vDZt2mS6MpLMVU55eXnatm2bPDw8jA6lDIvFYuoEeE139OhRNW/eXGFhYdW2zZSUFB09erTatmdvZh1zSOYadxQWFio5OVlubm5Gh2KjsLDQtJMaNUFWVpaWLl0qHx8fo0MpIzc31zSTZ0VFRUpJSZGrq7keplFUVGTqRMTJkyd11113KSgoyKH7TU9P18mTJx26T2d27tw5xcfHm66dyM3N1blz54wOwyotLU3PPPOMw+ciKpKVlaW0tDSjw5DEmONCDk1ESCXJCLNM+JtZZmamaTrfNUlKSgpJASeQk5Nj6kl/szp79myNSgoYJTk5maSAEzh//rxpJrPNijKqnLy8PCb8a6mjR4/WqMSBPTDmqBwm/Z1TVlaWaSb8zczsk/5mdfLkSZICTuDcuXOmmvQ3q7S0NNNM+psVY44S5kprAwAAAAAAAAAAp0IiAgAAAAAAAAAA2A2JCAAAAAAAAAAAYDckIgAAAAAAAAAAgN2QiAAAAAAAAAAAAHZDIgIAAAAAAAAAANgNiQgAAAAAAAAAAGA3JCIAAAAAAAAAAIDdkIgAAAAAAAAAAAB2QyICAAAAAAAAAADYDYkIAAAAAAAAAABgNyQiAAAAAAAAAACA3ZCIAAAAAAAAAAAAdkMiAgAAAAAAAAAA2A2JCAAAAAAAAAAAYDckIgAAAAAAAAAAgN2QiAAAAAAAAAAAAHZDIgIAAAAAAAAAANgNiQgAAAAAAAAAAGA3JCIAAAAAAAAAAIDdkIgAAAAAAAAAAAB2QyICAAAAAAAAAADYDYkIAAAAAAAAAABgNyQiAAAAAAAAAACA3ZCIAAAAAAAAAAAAduNSXFxcbHQQAAAAAAAAAADAOXFHBAAAAAAAAAAAsBsSEQAAAAAAAAAAwG5IRAAAAAAAAAAAALshEQEAAAAAAAAAAOyGRAQAAAAAAAAAALAbEhEAAAAAAAAAAMBuSEQAAAAAAAAAAAC7IREBAAAAAAAAAADshkQEAAAAAAAAAACwm/8HE98qIYv4UIoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_filter_coefs('Conv1', model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = model.get_layer('Conv1').get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights[0][:,:,0,0] = np.array([[0, 0, 0], [0 , 1,  0], [0, 0, 0]])\n",
    "weights[0][:,:,0,1] = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]])\n",
    "weights[0][:,:,0,2] = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])\n",
    "weights[0][:,:,0,3] = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]]).T\n",
    "weights[0][:,:,0,4] = np.array([[-1,-1, 0], [-1 , 0,  1], [0, 1, 1]])\n",
    "weights[0][:,:,0,5] = np.array([[1 , 1,  1], [1, 1, 1], [1, 1, 1]])\n",
    "\n",
    "# Normalizo\n",
    "weights[0] = weights[0]/np.sqrt((weights[0]*weights[0]).sum(axis=0).sum(axis=0))\n",
    "model.get_layer('Conv1').set_weights(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_filter_coefs('Conv1', model, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 28, 28, 1)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step\n",
      "(5, 26, 26, 6)\n"
     ]
    }
   ],
   "source": [
    "images_to_filter = np.load('images_to_filter.npy')\n",
    "print(images_to_filter.shape)\n",
    "activations_conv1_output = model.predict(images_to_filter)\n",
    "print(activations_conv1_output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sin función de activación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_activations(activations_conv1_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Con Relu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julian/opt/anaconda3/envs/cnns/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_relu = get_simple_model('relu')\n",
    "model_relu.set_weights(weights)\n",
    "activations_output_relu = model_relu.predict(images_to_filter)\n",
    "plot_activations(activations_output_relu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Con sigmoidea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_sigmoid = get_simple_model('sigmoid')\n",
    "model_sigmoid.set_weights(weights)\n",
    "activations_output_sigmoid = model_sigmoid.predict(images_to_filter)\n",
    "plot_activations(activations_output_sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sigmoidea normalizando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_to_filter.max(), images_to_filter.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_sigmoid_2 = get_simple_model('sigmoid')\n",
    "model_sigmoid_2.set_weights(weights)\n",
    "activations_output_sigmoid = model_sigmoid_2.predict(images_to_filter/256)\n",
    "plot_activations(activations_output_sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imagenes grandes con capa densa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julian/opt/anaconda3/envs/cnns/lib/python3.9/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_9\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_9\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">18,750,100</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">505</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)      │    \u001b[38;5;34m18,750,100\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)        │           \u001b[38;5;34m505\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,750,605</span> (71.53 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m18,750,605\u001b[0m (71.53 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,750,605</span> (71.53 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,750,605\u001b[0m (71.53 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, input_shape=(None,500*375)))\n",
    "model.add(Dense(5, input_shape=(None,500*375)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capa Pooling\n",
    "### MaxPooling\n",
    "**Su función es reducir la cantidad de parámetros, bajar la dimensionalidad**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Max Pooling\n",
    "![Max-polling.png](imgs/Max-polling.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparámetros\n",
    "MaxPooling\n",
    "- Pool size\n",
    "- Strides: En este caso está en None por default lo que significa que strides=pool_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### En [keras](https://keras.io/layers/pooling/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julian/opt/anaconda3/envs/cnns/lib/python3.9/site-packages/keras/src/layers/pooling/base_pooling.py:23: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(name=name, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(MaxPooling2D(pool_size=(2,2), strides=None, padding='same', input_shape=(32,32,1)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hacer pruebas y explicar 'Output Shape' y 'Param #':\n",
    "- pool_size=(2,2), strides=None, padding='same'\n",
    "- pool_size=(4,2), strides=None, padding='same'\n",
    "- pool_size=(3,3), strides=None, padding='same'\n",
    "- pool_size=(3,3), strides=None, padding='valid'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_simple_model_pool(activation = None, n_filters = 6, input_shape=(28,28,1)):\n",
    "    model = Sequential()\n",
    "    model.add(Convolution2D(filters=n_filters, \n",
    "                            kernel_size=3, \n",
    "                            input_shape=input_shape, \n",
    "                            name = 'Conv1',\n",
    "                            padding= 'valid',\n",
    "                            activation=activation\n",
    "                           ))\n",
    "    model.add(MaxPooling2D(3))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pool = get_simple_model_pool(activation='relu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pool.get_layer('Conv1').set_weights(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f77fb3030d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
      "(5, 8, 8, 6)\n"
     ]
    }
   ],
   "source": [
    "activations_pool_output = model_pool.predict(images_to_filter)\n",
    "print(activations_pool_output.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERJlpLfs97ur+X7t2bbQndl7y9xjkWukdOUi74p5ermbNmlVmujpHyXOS3/XWsjs7eWdNfo+3bt1aZnyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABArNcPp5sOclDs7sqg2MFxHmvO4+A4jzXncbCcyZozOTjOY815HBznsZacR18iAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQ6/VNNwUAACDkSyQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABAbDgN9nq9X+c+Xhb6/f5LvYVXDOex5jwOjvNYcx4Hy5msOZOD4zzWnMfBcR5ryXn0JRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACA2PBLvYH/k16vV2Zuuumm6FnLly8vM0NDQ2Wm3+9H68H/TXKOjjjiiDLz8MMPd7EddkOf/OQny8w555wTPevRRx8tM6eeemqZWbp0abQewK9bcv997nOfi561xx57lJnR0dFOnsPLU/L7+OEPfzh61iGHHFJmzjzzzDKzffv2aL2KL5EAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgNp8EZM+q+mQxSv+mmm8rMoYceWmYWLlxYZlpr7cknnywz4+PjZWbmzJnRerwyJcOGW2vttttuKzNHH310mVm9enW0HoPR6/XKzMc//vEyc/jhh5eZ2bNnJ1uKXHzxxWUmudevvPLKLrbDy9jcuXOj3ObNm3/NO+GlkNyRp59+epn56le/WmaSszY5OVlmWmvtDW94Q5lZsWJFmTnnnHOi9dh1LFmypMxcdNFFZeY973lPmUk7xsjISJl57LHHyszatWuj9Sq+RAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAINbrJ5OkW2s//vGPy0wyBHv+/PllZsuWLWVmbGyszLTW2vT0dJnZa6+9omdVFixY0MlzqCWDixN77rlnmdm0aVOZSc9jMuB4n332KTPPPfdcmRkfH4/2xM5Lhvt+5StfKTOnnHJKmbnwwgvLzIYNG8pMa60l13+S2W+//crMgw8+GO2JbiR35MKFC8tMco8kmdHR0TLTWvabfeyxx0bPqtxxxx2dPIfaAw88UGaWLl1aZoaHh8vMgQceWGbmzJlTZlprbevWrWUmOdtHHHFEmbn11lujPbHzvv3tb5eZZcuWlZnkfS15PxwZGSkzqa7ej5N9+xIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBsOA3OnDmzzBx66KFlZmxsLF1ylzI5OflSb4H/zZw5czrJbNq0qcz0+/0yMz4+XmZay87/unXryszwcPxPlwH4jd/4jTLzox/9qMzcddddZWZiYqLMbN++vcykuV6vV2YeeeSRaD0G57//+7/LzPT0dJn51re+VWaSOzK5j1trbf78+WXG/bf7ufzyy8vME088UWaSs3bAAQeUma1bt5aZ1rL7b2hoqMz86le/itZjMJYtW1ZmFi9eXGaSd7qkPyWZ1rLzmNzrSSbhSyQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGK9fjK5tWUDLg855JAyc+yxx5aZ559/vsxMTU2VmdaygZrJesn/TA888EC0J3Zech4To6OjZWbHjh2drJWulw6Kr3Q1TJbaX/7lX5aZn//852Vm7dq1ZeanP/1ptKeuJP/WZsyo///IycnJLrZD6DOf+UyZ+bM/+7Myk9xZu6LkHSEZEk83uvrNfjkLX8fpQNJFksyiRYvKTPIulr6vJbnknTU5a8lviC+RAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACI9frhdFODYmsGxQ6O81hzHgfHeaw5j4PlTNacycFxHmvO4+A4j7XkPPoSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAWK9vuikAAAAhXyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYsNpsNfr/Tr38bLQ7/df6i28YgzyPCZrdfm3P+aYY8rM/fffX2ampqa62A6BXe1+TPdz5JFHlpmHHnqozCxZsqTMPP3009Ge6MaudiZ3RX6zB6er89jVc4aGhqLcOeecU2ZWrVpVZn75y1928hy6savdj+l+Dj300DIzMjJSZh5//PEys3nz5jLjSyQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGLDL/UG/k9mzKi77be+9a3oWUuXLi0zyUDNE044IVoP/m9mzZpVZk488cQys2zZsg52w8vVVVddFeXGx8fLzEc/+tEys//++0fr8fKUDMr+/d///ehZV155ZZk56KCDysxee+0Vrccr06233hrljjjiiDLz7W9/u8ycddZZ0Xq8Mn3mM5+Jcueff36ZWbhwYZn52te+Fq1X8SUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAACxXr/f70fBYJhwV2bMqLvtkiVLomfNnTu3zDz66KNl5pJLLikzV1xxRbQndt6udh6np6ejZx188MFlJjmzM2fOLDP33HNPtCd23iDPY7LWhRdeGD3r+uuvLzNDQ0Nl5vvf/36ZOe6446I90Y1BnsnEN7/5zSi37777lpnkTv7Yxz5WZr773e9Ge2LndXUek+ck5yN89Y2MjIyUmR/96Edl5uijj+5iOwR2td/sd7/73dGzRkdHy8yOHTvKzDve8Y4y8/a3v73M+BIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAIDY8KAXTIbAJtasWdPZemeddVaZ2X///aP1eGVKz/Vjjz3WybOSgdzsfroagHzttddGuaGhoTJzyy23lJnbb7+9zBx33HHRntj9JOf2D//wD6NndTUofvHixdF67F6SszY9Pd3Zeskd+cQTT5SZlStXdrEdXqauu+66KJecxyuuuKLMvOlNb4rWq/gSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACA2HCXD0sHru9q3vGOd5SZ3/zN3/z1b4RYMnC4q8HtiXStZAjy3Llzy8y5554brceuo6vzmDwnGcbeWmunnXZamZk3b16ZufHGG8vMJZdckmyJAeryLHUlWS+5Rw8//PAutkNHkrOWvEMmf/suf/vPP//8MrNgwYIy8573vKfMrFy5MtoTg7Er3o+vetWrykxyZufPn9/FdnyJBAAAIKdEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgNpwGZ8yo++bo6GiZ2b59e5mZnp4uM8PD2dZXrFhRZk4++eQyc/nll5eZq6++OtkSA9Lv98vMwQcfXGYeffTRMjM0NBTt6bLLLiszy5cvLzNf//rXo/XYdey3335l5qmnniozvV6vzCRnqLXW3vWud5WZD37wg2Xmhz/8YbQeVM4666wy8+lPf7rMTE1NdbEdBuiwww4rM0888USZ2bZtW5k5++yzoz1de+21ZeaOO+4oM3/3d38XrceuY9GiRWUmuWdeeOGFMpO8i7bW2pVXXllmVq5cWWYefvjhMnPiiSeWGV8iAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQG+7yYcnw1tHR0TJzzTXXlJkbbrgh2tPee+9dZg444IAykwzBZdfS7/fLzEUXXVRmPvKRj5SZjRs3Jltq//AP/1BmkgHwyfn/3Oc+F+2JnTcyMlJm/viP/7jMXH755WVmzz33LDPvfve7y0xrrT344INl5t577y0za9asKTNLliyJ9kQ3hofrn/fJyckB7OT/k9zHrWW/teeee26Zue+++8pMem+z85I78oILLigz3/nOd8rMAw88UGbS38cbb7yxzPzRH/1Rmfn85z8frcdgDA0NlZm///u/LzNTU1Nl5tJLLy0zV111VZlprbWJiYkyc+GFF5aZ5A498cQTy4wvkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiNXTiP9/H/rQh8rM8ccfXy8YDEBevnx5mZk1a1aZaa21U045pczs2LGjzGzbti1aj8HYe++9y8y6devKzNjYWJl5/etfX2aSYbKttfaDH/ygzKxYsaLMpIO7GYyjjjqqzBxxxBFlZu7cuWXmDW94Q5m54447ykxrrf3jP/5jmRkdHS0zs2fPjtZjcP76r/+6zCR3W3JHrlq1qswccMABZaa1bJh8co++//3vj9ZjMD7wgQ+Umd/5nd8pM3feeWeZ+cIXvlBm0nfId77znWVmZGSkzKTnn8FI7oekPyxatKjMJHdo6rrrriszTzzxRJmZnJzsYju+RAIAAJBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAINbrm1oOAABAyJdIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiSiQAAAAxJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgNhwGuz1ep1k3vrWt5aZM888s8ysXLmyzLTW2vHHH19m7rrrrjJz5ZVXlpl+vx/tiZ2XnLWunvOnf/qnZebmm2+O1jv99NPLzMKFC8vMhz/84TIzNTUV7Ymd19X9ePbZZ5eZ8fHxMvPzn/+8zLTW2vLly8vM1VdfHT2r4n4crK7uyMSqVavKzPbt26NnHXLIIWWmq/82Z3Jwkr/Z8HD9SvqJT3yizIyOjpaZiy++uMy01tqb3vSmMnPnnXeWmeSsOY+D09Vv9gknnFBm5s+fX2bS97UlS5aUmQ0bNpSZG264ocxMT0+XGV8iAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQqye7/j9IBrxedNFFZeaMM84oM8nA7dZamzGj7sl33313melq4Da7lv/6r/8qMw899FCZ+eIXvxitd9VVV5WZe+65p8wsXbo0Wo9dxz777FNmTj755DLz/ve/v4vttNZaO/DAA8vM17/+9TJz7rnndrEdBmxoaKjMHH/88WXmk5/8ZJm55pproj195CMfKTO/+7u/W2Z++MMfRuux60juyMMOO6zMnHnmmV1sp7WWDYG/8MILy8y1117bwW4YpFmzZpWZvffeu8w8//zzZeaWW26J9vQHf/AHZWbfffctM7Nnz47Wq/gSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiCmRAAAAxJRIAAAAYkokAAAAMSUSAACA2HAaHBkZKTM7duwoM6973evKzPbt28tMr9crM61lw5QXLlxYZroazMngzJhR/38kRx11VJn5vd/7vTLT7/ejPSW2bt1aZv7nf/6ns/XYeclZu+GGG8rMz372sy62E7vkkkvKTDK0Pb2PGZzkN3t6errMJHfbP//zP5eZycnJMtNaa5dffnmZOfzww6NnsetI3sW+8pWvlJkXXnihzHR5H919991l5uMf/3iZue666zrYDV1JzshJJ51UZvbdd98y841vfKPMpO+Q//Ef/1FmPvrRj5aZ0047LVqv4kskAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIKZEAAADElEgAAABiw2nw4YcfLjPJMOHx8fEykwx3Pf3008tMa6198YtfLDPJENz7778/Wo9dx8KFC8vML37xizJz6qmnlpnVq1dHe3rwwQfLzKWXXlpmvvCFL5SZo48+OtoTO2/lypVl5pprrikz//qv/9rFdtr8+fOj3OLFi8tMMpB+n332idZjcCYmJjp5zp133llmuhzunpy3RDq8m8FI3g+Te/Sf/umfykwybP3EE08sM61lw+STd8jbb789Wo/BePLJJ8vMnDlzyszatWvLzDHHHFNmbrvttjLTWmtHHXVUmXnLW95SZt72trdF61V8iQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQKzXDyfyrlu3rsw899xzZWbGjLq3JltKntNaa1u3bu3kWfvtt1+ZWbRoUbQndl4y3Pqggw4qM1u2bCkzy5YtKzPJ2W+ttW3btpWZ9evXl5n999+/zDz++OPJlujAs88+W2aSodXJoPV58+aVmWT4dWut7bHHHmUmuUOHh4fLzFNPPRXtiW4k52T79u1lJvntW7JkSZnZtGlTmWmttRdffLHMJHf7hg0bysxPfvKTekN04uabby4zp5xySpmZO3dumVmwYEGZufjii8tMa62tWrWqzHz5y18uM2NjY2XmmWeeifbEzkt+s0dHR8vMxo0by0xypyX3dWvZnT1//vwyk7xDJ8/xJRIAAICYEgkAAEBMiQQAACCmRAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgNhwGtxnn33KzPPPP19mtm/fXmampqbKTK/XKzOttdbv9zvJrF+/vswsWrQo2hODMW/evDKzZs2aMvPII4+UmeHh7J/S9PR0mRkbGyszyX8bg7P33nt38pzkXtu4cWOZWbhwYbTec889V2aS+3HfffeN1mNw9thjjzKzYcOGMvPkk0+WmV/96ldlZsaM7P+zTu7IZE9HHnlktB6DMTQ0VGbmzJlTZpJztG7dujLzqU99qsy01trMmTPLTPJ7vHTp0mg9BmNiYqLMJL/rSafp6v2gtdZGRkY6eU7SsxK+RAIAABBTIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIJZNSA8lQ1mTgbPJEMy5c+dGe0oGik5OTpaZdFAyu45t27aVmeSsJecjPY/j4+Od7OnAAw+M1mMwduzYUWaSIeqJ5C5Kzn5rrW3evLnMjI2NlZnVq1dH6zE4y5YtKzMbNmwoM5s2bepiO52d/1Tyb5LBeeGFF8pM8jdL3iET6R3Z1flfu3ZtJ8+hG88991yZ2W+//crMrFmzuthO1J9ay94Pk0y/34/Wq2hGAAAAxJRIAAAAYkokAAAAMSUSAACAmBIJAABATIkEAAAgpkQCAAAQUyIBAACIDafBNWvWlJlk4PqWLVvKTDLcuqtBma1lw2uTAd/sWp5++ukyk/ztJycny8z27dujPSVDYBP/9m//1slz6EYyuHjFihVl5oILLigzIyMjZSY5s6lkKHdXA8DpzmWXXVZmXnzxxTLzF3/xF2UmGcg+Z86cMtNadpYmJibKzLve9a5oPQYjOSMnnnhimbn11lvLzOzZs8tMcoZaa23r1q1lptfrlRl35K7l4YcfLjNHHHFEmRkermvUoDtG8qzkzEZrdfIUAAAAXhGUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiPX6/X7/pd4EAAAAuwdfIgEAAIgpkQAAAMSUSAAAAGJKJAAAADElEgAAgJgSCQAAQEyJBAAAIKZEAgAAEFMiAQAAiP0v/7sKOW6pvS4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_activations(activations_pool_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global Average Pooling  \n",
    "- Un resultado(Promedio) por cada canal\n",
    "- Utilizada para reducir la dimensionalidad en las ultimas capas\n",
    "![GAP.png](imgs/GAP.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### En [keras](https://keras.io/layers/pooling/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julian/opt/anaconda3/envs/cnns/lib/python3.9/site-packages/keras/src/layers/pooling/base_global_pooling.py:12: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_12\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_12\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ global_average_pooling2d_1      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ global_average_pooling2d_1      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(GlobalAveragePooling2D(input_shape=(32,32,20)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AveragePooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import AveragePooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_13\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_13\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ average_pooling2d               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AveragePooling2D</span>)              │                        │               │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ average_pooling2d               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m20\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m)              │                        │               │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(AveragePooling2D(4, 4, input_shape=(32,32,20)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preguntas:\n",
    "\n",
    "- Como cierro la red? Que utlilzar? GAP o DENSA?\n",
    "- Que implica hacer mas grande o mas pequeño el filtro?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Armamos la red AlexNet:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AlexNet [(paper)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\n",
    "- 2012 \n",
    "- Error de top-5 de 15.3%, más de 10.8% arriba del segundo. \n",
    "- ImageNet: En 1.2 millones de imágenes de alta resolución, 1000 clases\n",
    "- Diseñada por: SuperVision group, consisting of Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever.\n",
    "\n",
    "**Lo novedoso**: Dropout, RELUs\n",
    "(Dropout a la salida de las capas de 2048)\n",
    "\n",
    "**Arquitectura:**\n",
    "- Rectangulares con center-crop (Lado mas corto 256)\n",
    "- 60 millones de parámetros\n",
    "- 650 mil neuronas\n",
    "- 5 capas convolucionales\n",
    "- Algunos seguidos de capas pooling\n",
    "- 3 capas densas\n",
    "- SoftMax de 1000 categorias (1000 neuronas a la salida)\n",
    "- RELUs en vez de Sigmoideas\n",
    "- Dropout para evitar regularización\n",
    "\n",
    "**Entrenamiento:**\n",
    "- CUDA para soporte de GPU\n",
    "- Data augmentation\n",
    "- SGD\n",
    "- Batch size = 128\n",
    "- Momentum 0.9\n",
    "- Learning rate = 0.01\n",
    "- weight decay of 0.0005 (equivalente a L2 regularization)\n",
    "- 5 a 6 días de entrenamiento en 2 [GTX 580](https://www.geforce.com/hardware/desktop-gpus/geforce-gtx-580) 3GB GPUs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alexnet2.png](imgs/alexnet2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Armarla con Keras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from solutions import get_alexnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_14\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_14\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">34,944</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">614,656</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">885,120</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,327,488</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">884,992</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9216</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │    <span style=\"color: #00af00; text-decoration-color: #00af00\">37,752,832</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │    <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1000</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,097,000</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │        \u001b[38;5;34m34,944\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m96\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m614,656\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │       \u001b[38;5;34m885,120\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m384\u001b[0m)    │     \u001b[38;5;34m1,327,488\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m884,992\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9216\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │    \u001b[38;5;34m37,752,832\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │    \u001b[38;5;34m16,781,312\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m)           │     \u001b[38;5;34m4,097,000\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">62,378,344</span> (237.95 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m62,378,344\u001b[0m (237.95 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">62,378,344</span> (237.95 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m62,378,344\u001b[0m (237.95 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alexnet_model = Sequential()\n",
    "alexnet_model.add(Convolution2D(filters=96, kernel_size=11, strides=4,\n",
    "                                input_shape=(227,227,3), \n",
    "                                padding='valid', activation='relu'))\n",
    "alexnet_model.add(MaxPooling2D(pool_size = 3, strides=2))\n",
    "alexnet_model.add(Convolution2D(filters=256, kernel_size=5, padding='same', activation='relu'))\n",
    "alexnet_model.add(MaxPooling2D(pool_size = 3, strides=2))\n",
    "alexnet_model.add(Convolution2D(filters=384, kernel_size=3, padding='same', activation='relu'))\n",
    "alexnet_model.add(Convolution2D(filters=384, kernel_size=3, padding='same', activation='relu'))\n",
    "alexnet_model.add(Convolution2D(filters=256, strides=2, kernel_size=3, padding='valid', activation='relu'))\n",
    "alexnet_model.add(Flatten())\n",
    "alexnet_model.add(Dense(4096, activation='relu'))\n",
    "alexnet_model.add(Dropout(0.5))\n",
    "alexnet_model.add(Dense(4096, activation='relu'))\n",
    "alexnet_model.add(Dropout(0.5))\n",
    "alexnet_model.add(Dense(1000, activation='softmax'))\n",
    "alexnet_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reemplazo de FC por Convolucionales e implementación de sliding window eficiente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que tengo una entrada de 5x5. Si hago flatten para poner una FC, me queda una entrada de 25x1.  \n",
    "La cantidad de parámetros a aprender suponiendo que tengo una salida con 4 neuronas es:  \n",
    "25*4 + 4 = 104"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_15\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_15\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m104\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> (416.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m104\u001b[0m (416.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> (416.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m104\u001b[0m (416.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.layers import Dense\n",
    "model_dense = Sequential()\n",
    "model_dense.add(Dense(4, input_shape=(25,)))\n",
    "model_dense.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si utilizo una convolucional con la misma entrada que salida, para tener la misma cantidad de parámetros, tengo que poner el tamaño del filtro igual a la entrada: 5x5  \n",
    "Y la cantidad de filtros igual a la salida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_16\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_16\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m4\u001b[0m)        │           \u001b[38;5;34m104\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> (416.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m104\u001b[0m (416.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> (416.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m104\u001b[0m (416.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_slide = Sequential()\n",
    "model_slide.add(Convolution2D(filters=4, kernel_size=5, input_shape=(5,5,1)))\n",
    "model_slide.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que pasa si aumento la entrada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_20\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_20\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m4\u001b[0m)        │           \u001b[38;5;34m104\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> (416.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m104\u001b[0m (416.00 B)\n"
      ]
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     "metadata": {},
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">104</span> (416.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m104\u001b[0m (416.00 B)\n"
      ]
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
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   "source": [
    "width = 6\n",
    "height = width\n",
    "model_slide = Sequential()\n",
    "model_slide.add(Convolution2D(filters=4, kernel_size=5, input_shape=(height,width,1)))\n",
    "model_slide.summary()"
   ]
  },
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   "metadata": {},
   "source": [
    "![](imgs/sliding-wind-implem.png)"
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   "source": [
    "<pre>Layer         kernel  stride  output shape\n",
    "---------------------------------------------\n",
    "Input                          (416, 416, 3)\n",
    "Convolution    3×3      1      (416, 416, 16)\n",
    "MaxPooling     2×2      2      (208, 208, 16)\n",
    "Convolution    3×3      1      (208, 208, 32)\n",
    "MaxPooling     2×2      2      (104, 104, 32)\n",
    "Convolution    3×3      1      (104, 104, 64)\n",
    "MaxPooling     2×2      2      (52, 52, 64)\n",
    "Convolution    3×3      1      (52, 52, 128)\n",
    "MaxPooling     2×2      2      (26, 26, 128)\n",
    "Convolution    3×3      1      (26, 26, 256)\n",
    "MaxPooling     2×2      2      (13, 13, 256)\n",
    "Convolution    3×3      1      (13, 13, 512)\n",
    "MaxPooling     2×2      1      (13, 13, 512)\n",
    "Convolution    3×3      1      (13, 13, 1024)\n",
    "Convolution    3×3      1      (13, 13, 1024)\n",
    "Convolution    1×1      1      (13, 13, 125)\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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