4_TransferLearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten\n",
    "from keras.optimizers import Adam\n",
    "from keras.applications.resnet50 import ResNet50, preprocess_input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from tqdm import tqdm_notebook, tnrange\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Descargar [dataset de flores](http://download.tensorflow.org/example_images/flower_photos.tgz)\n",
    "\n",
    "```\n",
    "tar -xvzf flower_photos.tgz\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './flower_photos/'\n",
    "img_size = 224\n",
    "batch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Especificamos la función de preprocesamiento\n",
    "train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos la función \"flow_from_directory\" para cargar las imagenes desde un folder.\n",
    "\n",
    "La estructura de la carpeta que espera la función es importante. Cada clase de nuestro dataset tiene que estar dentro de una carpeta con el nombre de la clase:\n",
    "\n",
    "- path:\n",
    "    - class1\n",
    "        - img1, img2, img3, ...\n",
    "    - class2\n",
    "        - img1, img2, img3, ...\n",
    "    - class3\n",
    "        - img1, img2, img3, ...\n",
    "        \n",
    "Es una estructura común en los datasets de imágenes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3670 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(path, target_size=(img_size, img_size), batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator.class_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['daisy/100080576_f52e8ee070_n.jpg',\n",
       " 'daisy/10140303196_b88d3d6cec.jpg',\n",
       " 'daisy/10172379554_b296050f82_n.jpg']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Si queremos el nombre de los archivos, el parámetro filenames guarda la ruta de las imágenes\n",
    "train_generator.filenames[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sunflowers/6125761554_4e72819ce4_m.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=180x240 at 0x7F194F07E550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veamos alguna de las imágenes\n",
    "random_img = np.random.choice(train_generator.filenames)\n",
    "print(random_img)\n",
    "Image.open(path + random_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((64, 224, 224, 3), (64, 5))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veamos como se extrae un batch del generator\n",
    "batch_x, batch_y = train_generator.next()\n",
    "batch_x.shape, batch_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           activation_49[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,534,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = ResNet50(include_top=False, input_shape=(img_size,img_size,3), pooling='avg')\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor 'input_1:0' shape=(?, 224, 224, 3) dtype=float32>,\n",
       " <tf.Tensor 'global_average_pooling2d_1/Mean:0' shape=(?, 2048) dtype=float32>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model.input, base_model.output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dado que solo queremos entrenar las capas densas del modelo que agregaremos\n",
    "# en el siguiente paso, vamos a setear \"trainable = False\" para que los pesos\n",
    "# de la red entrenada no cambien.\n",
    "base_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 128)               262272    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 5)                 645       \n",
      "=================================================================\n",
      "Total params: 262,917\n",
      "Trainable params: 262,917\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "flowers_model = Sequential()\n",
    "\n",
    "flowers_model.add(Dense(128, activation='relu', input_shape=(2048,)))\n",
    "flowers_model.add(Dense(5, activation='softmax'))\n",
    "\n",
    "flowers_model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer=Adam(0.001),\n",
    "                      metrics=['accuracy'])\n",
    "flowers_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Juntamos los dos modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "resnet50 (Model)             (None, 2048)              23587712  \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 5)                 262917    \n",
      "=================================================================\n",
      "Total params: 23,850,629\n",
      "Trainable params: 262,917\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "final_model = Sequential()\n",
    "\n",
    "final_model.add(base_model)\n",
    "final_model.add(flowers_model)\n",
    "\n",
    "final_model.compile(loss='categorical_crossentropy',\n",
    "                      optimizer=Adam(0.001),\n",
    "                      metrics=['accuracy'])\n",
    "final_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observa la cantidad de parametros entrenables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenando en nuestro dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "58/58 [==============================] - 45s 784ms/step - loss: 0.4790 - acc: 0.8287\n"
     ]
    }
   ],
   "source": [
    "log = final_model.fit_generator(train_generator, steps_per_epoch=58)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La red de base que estamos usando (ResNet50) tiene bastantes capas y hacer todas estas operaciones toma un tiempo considerable, en CPU hacer este entrenamiento puede resultar impractico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precompute\n",
    "\n",
    "Si tenemos en cuenta que vamos a entrenar nuestro dataset un cierto numero de epocas, en cada epoca se van a repetir exactamente las mismas operaciones en la misma data. Para no redundar, es bastante util hace un precompute de la data:\n",
    "\n",
    "1. Pasar todas nuestras imagenes por la red base (ResNet50).\n",
    "2. Guardamos los features extraidos.\n",
    "3. Entrenamos las capas densas con los features extraidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3670 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Vamos a crear nuevamente el generator, pero esta vez con el parámetro \"shuffle=False\",\n",
    "# para obtener los resultados en orden\n",
    "train_generator = train_datagen.flow_from_directory(path, target_size=(img_size, img_size),\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_batch = math.ceil(train_generator.samples / batch_size)\n",
    "n_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fade6cc1ca1e425fb24f45ff67505c9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=58), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Reseteamos el status del generador para comenzar en orden\n",
    "train_generator.reset()\n",
    "\n",
    "# Variables donde vamos a guardar los features extraidos\n",
    "outputs_x = []\n",
    "outputs_y = []\n",
    "\n",
    "# Usamos \"tnrange\" en lugar de \"range\" para tener más información del loop\n",
    "for i in tnrange(n_batch):\n",
    "    # Obtenemos la información del batch\n",
    "    batch_x, batch_y = train_generator.next()\n",
    "\n",
    "    # Guardamos el resultado del base_model\n",
    "    outputs_x.append(base_model.predict_on_batch(batch_x))\n",
    "    \n",
    "    # Guardamos tambien los labels\n",
    "    outputs_y.append(batch_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3670, 2048), (3670, 5))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usamos la función \"vstack\" de numpy, para juntar los resultados en forma de un numpy array\n",
    "outputs_x = np.vstack(outputs_x)\n",
    "outputs_y = np.vstack(outputs_y)\n",
    "outputs_x.shape, outputs_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardamos los arrays\n",
    "np.savez_compressed('data/flower_precomputed', x=outputs_x, y=outputs_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3670, 2048), (3670, 5))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cargar arrays\n",
    "data = np.load('data/flower_precomputed.npz')\n",
    "outputs_x = data['x']\n",
    "outputs_y = data['y']\n",
    "\n",
    "outputs_x.shape, outputs_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenar a partir de los features extraidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "3670/3670 [==============================] - 1s 343us/step - loss: 0.2829 - acc: 0.8973\n",
      "Epoch 2/5\n",
      "3670/3670 [==============================] - 0s 20us/step - loss: 0.1570 - acc: 0.9482\n",
      "Epoch 3/5\n",
      "3670/3670 [==============================] - 0s 19us/step - loss: 0.1098 - acc: 0.9624\n",
      "Epoch 4/5\n",
      "3670/3670 [==============================] - 0s 20us/step - loss: 0.0738 - acc: 0.9801\n",
      "Epoch 5/5\n",
      "3670/3670 [==============================] - 0s 21us/step - loss: 0.0520 - acc: 0.9899\n"
     ]
    }
   ],
   "source": [
    "# Ahora podemos usar un batch_size mas grande, ya que los features son mas pequeños\n",
    "# que las imagenes.\n",
    "log = flowers_model.fit(outputs_x, outputs_y, epochs=5, batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H7m/kV1Q457JaoFZpIadH6cxaVMDu8oozo3cmZvXi7c/28qtFBdz1wgpGpSXy/QkDyc3o4nHFItJS/BmymQMUOueKAMxsLjAROBP6zrntvnW61bONmDQypdEhmlcP68m4wd2Zv2IXs9/dwpRnl3JZZjIzxmcypFcnDyoVkZbkz+mdFGBnvee7fMv8FWdm+Wa21MwmNas68URUZARTc9L4YMZlPHT1QFYVl3PNU3/nuy+tYvvB416XJyLnIRA3Z/VxzpWYWQbwvpmtc85trb+Bmd0F3AWQlpYWgJLEH3HRkdw1th9TLkzj9x8V8Ye/b+OtdXuYmtOb714+gG4d47wuUUSayZ8j/RKgd73nqb5lfnHOlfj+WQR8AIxsZJtnnXPZzrns5ORkf3+1BEin+GjuH5/Jhw9cxs05acxdtpOxs5bw2DubOFxR5XV5ItIM/oT+cmCAmaWbWQwwFfBrFI6ZdTazWN/jrsCXqHctQNqWbglx/HTSUN6771LGD+nBf36wlbGPLeGZD7eqAbtIG9Fk6DvnqoG7gUXARuBl59x6M3vYzK4DMLMLzWwXcCPwOzNb73v5ICDfzNYAS4BHG4z6kTaoT5f2PDl1JG9+9xJGpSXy6NubuOxXS3gxr5jqGl3LFwlmFmxjsbOzs11+fr7XZUgz5BWV8tiiAlbsOERG1/bcNy6Trw7tQUSEeV2aSNgwsxXOueymttMduXLecjO6MP9bF/HcbdlER0bwby+u5Lqn/85Hmw/oBi+RIKPQlxZhZlw5uDtv3fNlnrhpBIeOV3HbH5cx7fd5rN5Z7nV5IuKj0zvSKiqra3gpr5jfvF9I6fFTTBjSg/vHX0D/bglelyYSkvw9vaPQl1Z1rLKaP/59G89+VMSJU9VMHp3KPVdeQMpZJn0TkXOj0JegUnb8FE8vKeSFT3eAwW1j+vCdr/QnqX2M16WJhASFvgSlkvIKZi/ezKsrd9EuJoq7xmZw5yXptI9V506R86HQl6C2Zd9RfvW3Ahat30fXDjHc/ZX+3JybRmxUZNMvFpF/oNCXNmFl8SEee2cTS4vKSO0cz71XXcDErBQiNcZfpFk0Tl/ahFFpnXnpm2P48x05dIqP5t6X13DNUx/z3sZ9GuMv0goU+uI5M2PsBcm8fvcl/HbaSE5W1XDnn/K58ZlPWbatzOvyREKKQl+CRkSEce3wXiy+91J+/vWhFJed4Kbffco/P7+MjXuOeF2eSEjQOX0JWhWnavjv/93Of31QyNHKaiaO6MW9V2WS1qWd16WJBB1dyJWQcfhEFc98tJXnP9lGdY1jWm4ad1/en24JauIicppCX0LOviMneeq9LcxdvpOYyAjuvCSduy7NoGNctNeliXhOoS8ha9vB4zyxeDOvr9lNYrtovnNZP267qC9x0RrjL+FLoS8h77OSw8xaVMCHmw/Qo2Mc068cwOTRqURFanyChB+N05eQNzSlE3+6I4eXvjmGnolxzFywjnGzP+KtdXs0xl/kLBT60uZd1K8LC759Mc/+02gizfjOnJVMfPoTPik86HVpIkFHoS8hwcwYN6QH70wfy6zJwyk9dopbnsvj1ufyWKMmLiJn6Jy+hKSTVTXMySvm6SWFlB0/xdXDenDfuEz6JXfwujSRVqELuSLA0ZNVPPfxNp77uIiT1bXcODqVe64cQM9OauIioUWhL1LPwWOVPL2kkDlLi8Hg9ov78u1L+9FZTVwkRCj0RRqxs+wEs9/dwoJVu+gQE8W/XprBHZek0y5GTVykbVPoi3yBgr11TVwWb9hH1w6xfPeK/ky9MI2YKI1tkLZJ4/RFvkBmjwR+f1s2r377IjK6tudHf13PFU98wGurSqitDa4DIZGWpNCXsDa6TxLz/nUMz//zhXSIjWb6vNVc/dTHLNm0Xzd4SUjS6R0Rn9pax+trd/P43zZTXHaCnL5JPDAhk+y+Sby2qoRZiwrYXV5Br8R4ZozPZNLIFK9LFjlD5/RFztGp6lrm5e/kqfe2cOBoJUN6JVC4/ziV1bVntomPjuQX1w9T8EvQ0Dl9kXMUExXBP43pw4czLmPG+Ew27D76ucAHqKiqYdaiAo8qFDl3Cn2Rs2gXE8W/faX/WdfvLq8IYDUiLUOhL9KEXomN371rBo+9s4mdZScCXJHIuVPoizRhxvhM4hs0aImJjGBQz4488+FWxs5awu3PL2Pxhn1U19Se5beIBAfdhijShNMXaxsbvbO7vIJ5y3cyd3kx3/xzPj06xjE1pzdTL0yjRyf18JXgo9E7Ii2guqaW9zbtZ05eMR9tPkBkhHHFwG5My01j7IBkIiLM6xIlxLXo6B0zm2BmBWZWaGYzG1k/1sxWmlm1mU1usO4bZrbF9/MN//8VRNqOqMgIxg/pwZ/vyOGjGV/hrrEZrNhxiNufX86lv1rCf35QyIGjlV6XKdL0kb6ZRQKbgauAXcBy4Gbn3IZ62/QFOgL3Awudc/N9y5OAfCAbcMAKYLRz7tDZ3k9H+hIqTlXXsmj9Xubk7WBpURnRkXWNXm7JTeOijC6Y6ehfWo6/R/r+nNPPAQqdc0W+XzwXmAicCX3n3HbfuoZXscYDi51zZb71i4EJwEt+vK9ImxYTFcHXRvTiayN6Ubj/GC8tK2b+il28uXYPGcntmZaTxuTRqSS20/TOEjj+nN5JAXbWe77Lt8wf5/NakZDRv1sHfnjtYPIeuoLHbxxBYnw0P3tzIzmPvMe981azYkeZ5vqRgAiK0TtmdhdwF0BaWprH1Yi0nrjoSG4YncoNo1PZuOcIL+YV85dVJSxYVcLAHgnckpvGxJEpdIyL9rpUCVH+HOmXAL3rPU/1LfOHX691zj3rnMt2zmUnJyf7+atF2rZBPTvy00lDyXvoCn5x/TCiIo0f/nU9uT9/j5mvrmXdrsNelyghyJ8j/eXAADNLpy6wpwLT/Pz9i4BHzKyz7/k44MFmVykSwtrHRnFzTho356Sxdlc5c5YW89fVu5m7fCfDUzsxLSeN67J6qbuXtAi/xumb2dXAbCAS+KNz7udm9jCQ75xbaGYXAn8BOgMngb3OuSG+194BPOT7VT93zj3/Re+l0TsicORkFa+tKmHO0mIK9h0lITaKr49KYVpuGgN7dPS6PAlCmlpZJAQ451ix4xBz8op5c90eTlXXMrpPZ27JTePqYT2JazA9hIQvhb5IiDl0/BSvrtzFnLxith08TmK7aCaPSuXm3DT6JXfwujzxmEJfJEQ55/h0aylz8opZtH4v1bWOizK6cMuYNMYN7qHm7mGqJW/OEpEgYmZc3L8rF/fvyv6jJ3klfxcvLSvm7hdX0bVDDDdl9+bmnDR6J7XzulQJQjrSFwkBNbWOj7Yc4MW8Yt7buA8HjB2QzC25aVw+sBtRkTr6D3U6vSMSpvYcrmDusrrpnvcdqTwz3fOUC3vTs1PjDWGk7VPoi4S56ppa3j893fOWAxhwxaDu3KLpnkOSzumLhLmoyAjGDenBuCE9KC49wUvLi3l5+U4Wb9hHaud4bs5J46bs3iQnxHpdqgSQjvRFwsip6lr+tmEvc5YW82lRqaZ7DiE6vSMiX6j+dM+HK6rI6Nqeablp3DAqlc7tNd1zW6PQFxG/nKyq4a11e5iTV8yKHYeIiYrg2mE9mZabxug+nXX030Yo9EWk2TbtrZvuecHKEo5VVpPZPYFbxqQxSdM9Bz2Fvoics+OV1by+Zjdz8opZV3KY+OhIJmb1YlpuGsNTE70uTxqh0BeRFrF2Vzkv5tVN91xRVcOwlE7ckqvpnoONQl9EWpSmew5uCn0RaRXOOVYWH2LO0mLeOMt0z6+tKmHWogJ2l1fQKzGeGeMzmTRS7bFbk0JfRFrd6emeX8wrpsg33fOI1E4sLSqjsrr2zHbx0ZH84vphCv5WpNAXkYBxzvFpUd10z2+u3dPoNimJ8Xwy8/IAVxY+/A19Tb0nIufNzLi4X1eenjaKs43qLymv4IWlO9iy7yjBdrAZTnTpXURaVK/EeErKK/5heYTBD1/7DIAu7WPISU8iJz2J3PQuDOyRoAngAkShLyItasb4TB5csI6Kqpozy+KjI3nk60MZ1aczeUVl5G0rI29bKW9/theATvHRXNg3iTEZdV8Eg3t2VA+AVqLQF5EWdfpi7dlG7/Tp0p6bLuwN1J3yySsqJa+ojGXby3h34z4AOsRGkd23M7npXcjNSGJYSiei9SXQInQhV0SCxr4jJ+v+CigqJW9bGYX7jwF1fymM7tOZ3PQkcjO6MKJ3J2KjIj2uNrho9I6ItHkHj1WyfFvd6aClRaVs2nsUgNioCEamJZKT3oUx6UmMTOtMfEx4fwko9EUk5JSfOMUy35fAsm1lrN99mFoH0ZHGiNREcjOSyEnvQnafzrSPDa+z1wp9EQl5R05WsWL7IZZuq7susK7kMDW1jsgIY2hKJ8akJ5GbkUR236SQnyVUoS8iYed4ZTUriw/5RgiVsmbnYU7V1BJhMKhnxzMXhnP6JoVcoxiFvoiEvZNVNawqLifP95fAyuJDZ6aHGNgj4cx9AjnpSW2+V7BCX0SkgcrqGtbuOnxmdNCKHYc4carufoJ+ye3JzehSN0IovQs9OsV5XG3zKPRFRJpQVVPLZyWHzwwTzd9+iKOV1QD06dLuzBdAbkYSqZ3beVztF1Poi4g0U02tY+OeIyz1/SWwbFsZhyuqgLoJ43J9F4Zz07vQp0u7oOofrNAXETlPtbWOgn1HySsqZdn2MvKKyig9fgqA7h1jyUmvOx00JiOJfskdPP0SUOiLiLQw5xxbDxxj6en5g4pK2X+0EoCuHWI+d2E4s3tgJ5HzN/TD6+4FEZHzYGb075ZA/24J3DqmD845dpSeODM6KG9bGW+tq5tELrFd3SRydX8JdGFQz45EBsFMogp9EZFzZGb07dqevl3bM+XCNAB2lp3w3TVcd11g8Ya6SeQSTk8i5xshNLTeJHKBbC/pV+ib2QTgSSASeM4592iD9bHAn4HRQCkwxTm33cz6AhuBAt+mS51z32qZ0kVEgk/vpHb0TmrHDaNTAdh7+CR520pZWlTGsm2lLCk4AEC7mLpJ5BJio3h3035O+e4fKCmv4MEF6wBaJfibDH0ziwSeBq4CdgHLzWyhc25Dvc3uBA455/qb2VTgl8AU37qtzrmsFq5bRKRN6NEpjolZKUzMqgvwA0cr/+8vgaIyPt538B9eU1FVw6xFBd6EPpADFDrnigDMbC4wEagf+hOB//A9ng/81oJpLJOISJBITojlmuE9uWZ4TwDSZ75JY8NpdjfSfawl+NOVIAXYWe/5Lt+yRrdxzlUDh4EuvnXpZrbKzD40sy+fZ70iIiGlV2J8s5afr9ZuRbMHSHPOjQTuBV40s44NNzKzu8ws38zyDxw40MoliYgEjxnjM4mP/nwvgPjoSGaMz2yV9/Mn9EuA3vWep/qWNbqNmUUBnYBS51ylc64UwDm3AtgKXNDwDZxzzzrnsp1z2cnJyc3/txARaaMmjUzhF9cPIyUxHqPuzt9fXD/M09E7y4EBZpZOXbhPBaY12GYh8A3gU2Ay8L5zzplZMlDmnKsxswxgAFDUYtWLiISASSNTWi3kG2oy9J1z1WZ2N7CIuiGbf3TOrTezh4F859xC4A/AC2ZWCJRR98UAMBZ42MyqgFrgW865stb4FxERkaZpGgYRkRDg7zQMrX0hV0REgohCX0QkjCj0RUTCSNCd0zezA8CO8/gVXYF/vK/Ze6qreVRX86iu5gnFuvo455oc8x50oX++zCzfn4sZgaa6mkd1NY/qap5wrkund0REwohCX0QkjIRi6D/rdQFnobqaR3U1j+pqnrCtK+TO6YuIyNmF4pG+iIicRZsMfTObYGYFZlZoZjMbWR9rZvN86/N8bRuDoa7bzeyAma32/fxLgOr6o5ntN7PPzrLezOwpX91rzWxUkNR1mZkdrre/fhSgunqb2RIz22Bm683snka2Cfg+87OugO8zM4szs2VmtsZX108a2Sbgn0k/6/LkM+l770hfr5E3GlnXevvLOdemfqib9G0rkAHEAGuAwQ22+Q7wjO/xVGBekNR1O/BbD/bZWGAU8NlZ1l8NvA0YMAbIC5K6LgPe8GB/9QRG+R4nAJsb+W8Z8H1StJdsAAAC2ElEQVTmZ10B32e+fdDB9zgayAPGNNjGi8+kP3V58pn0vfe9wIuN/fdqzf3VFo/0z7RvdM6dAk63b6xvIvAn3+P5wBUBaN/oT12ecM59RN3sp2czEfizq7MUSDSznkFQlyecc3uccyt9j48CG/nHbnEB32d+1hVwvn1wzPc02vfT8GJhwD+TftblCTNLBa4BnjvLJq22v9pi6J9v+0Yv6wK4wXc6YL6Z9W5kvRf8rd0LF/n+PH/bzIYE+s19f1aPpO4osT5P99kX1AUe7DPfqYrVwH5gsXPurPsrgJ9Jf+oCbz6Ts4EHqJtyvjGttr/aYui3Za8DfZ1zw4HF/N83uTRuJXW3lo8AfgO8Fsg3N7MOwKvAdOfckUC+9xdpoi5P9plzrsY5l0VdZ70cMxsaiPdtih91BfwzaWbXAvtdXTfBgGuLoX/O7Ru9rss5V+qcq/Q9fQ4Y3co1+cuffRpwzrkjp/88d869BUSbWddAvLeZRVMXrHOccwsa2cSTfdZUXV7uM997lgNLgAkNVnnxmWyyLo8+k18CrjOz7dSdBr7czP6nwTattr/aYuifad9oZjHUXeRY2GCb0+0boV77Rq/ranDO9zrqzskGg4XAbb4RKWOAw865PV4XZWY9Tp/HNLMc6v5/bfWg8L3nH4CNzrknzrJZwPeZP3V5sc/MLNnMEn2P44GrgE0NNgv4Z9Kfurz4TDrnHnTOpTrn+lKXE+87525tsFmr7S9/euQGFXd+7Ru9ruu7ZnYdUO2r6/bWrgvAzF6iblRHVzPbBfyYuotaOOeeAd6ibjRKIXAC+OcgqWsy8G0zqwYqgKkB+PKGuiOxfwLW+c4HAzwEpNWrzYt95k9dXuyznsCfzCySui+Zl51zb3j9mfSzLk8+k40J1P7SHbkiImGkLZ7eERGRc6TQFxEJIwp9EZEwotAXEQkjCn0RkTCi0BcRCSMKfRGRMKLQFxEJI/8fpOvlSpBCUtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(log.history['loss'], '-o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usando el modelo completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para obtener la clase de una predicción, tenemos que identificar el indice del array\n",
    "# con el valor más alto, para esto usamos la función \"np.argmax\", ej:\n",
    "np.argmax([0, 0, 1, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para identificar el label que pertene a una prediccion usamos el diccionario\n",
    "# creado en el \"train_generator\"\n",
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'daisy', 1: 'dandelion', 2: 'roses', 3: 'sunflowers', 4: 'tulips'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pero necesitamos invertir el diccionario\n",
    "lbl_dict = {k:i for i,k in train_generator.class_indices.items()}\n",
    "lbl_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'roses'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbl_dict[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_class(path):\n",
    "    # Cargar la imagen del path\n",
    "    img = Image.open(path)\n",
    "    \n",
    "    # Mostrar la imagen\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')\n",
    "    \n",
    "    # Cambiar el tamaño de la imagen\n",
    "    img_resized = img.resize((224, 224))\n",
    "    \n",
    "    # Cambiar a formato numpy y preprocesar\n",
    "    x = np.asarray(img_resized, np.float32)[None]\n",
    "    x = preprocess_input(x)\n",
    "    \n",
    "    # Obtener predicciones\n",
    "    y = final_model.predict(x)\n",
    "    \n",
    "    # Decodear predicciones\n",
    "    preds = lbl_dict[np.argmax(y)]\n",
    "    \n",
    "    return preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./flower_photos/tulips/14094114202_4c1d7f1116.jpg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'tulips'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Escoger una imagene al azar\n",
    "random_path = path + np.random.choice(train_generator.filenames)\n",
    "print(random_path)\n",
    "\n",
    "# Obtener la clasificación de la imagen\n",
    "get_class(random_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}