{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detector de Paneles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargar el modelo ssd7 \n",
    "(https://github.com/pierluigiferrari/ssd_keras#how-to-fine-tune-one-of-the-trained-models-on-your-own-dataset)\n",
    "\n",
    "Training del SSD7 (modelo reducido de SSD). Parámetros en config_7.json y descargar VGG_ILSVRC_16_layers_fc_reduced.h5\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0309 16:47:10.941016 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0309 16:47:10.942156 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "W0309 16:47:10.942741 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "W0309 16:47:10.981798 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "W0309 16:47:10.984804 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "W0309 16:47:10.986444 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0309 16:47:10.994469 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0309 16:47:11.003161 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0309 16:47:11.017030 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4185: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.\n",
      "\n",
      "W0309 16:47:11.062859 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "W0309 16:47:11.134284 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training on: \t{'panel': 1}\n",
      "\n",
      "\n",
      "Loading pretrained weights.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0309 16:47:11.816283 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "W0309 16:47:12.014409 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "W0309 16:47:12.571218 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0309 16:47:12.587135 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test/model-definition/ssd_keras-master/keras_loss_function/keras_ssd_loss.py:95: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "W0309 16:47:12.594385 140565250733888 deprecation.py:323] From /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test/model-definition/ssd_keras-master/keras_loss_function/keras_ssd_loss.py:133: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "W0309 16:47:12.616868 140565250733888 deprecation.py:323] From /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test/model-definition/ssd_keras-master/keras_loss_function/keras_ssd_loss.py:74: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "W0309 16:47:12.643085 140565250733888 deprecation.py:323] From /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test/model-definition/ssd_keras-master/keras_loss_function/keras_ssd_loss.py:166: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "W0309 16:47:13.514157 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "W0309 16:47:13.868388 140565250733888 module_wrapper.py:139] From /home/dlsaavedra/anaconda3/envs/model/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import Adam, SGD\n",
    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TerminateOnNaN, CSVLogger\n",
    "from keras import backend as K\n",
    "from keras.models import load_model\n",
    "from math import ceil\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "import json\n",
    "import xml.etree.cElementTree as ET\n",
    "\n",
    "import sys\n",
    "sys.path += [os.path.abspath('../ssd_keras-master')]\n",
    "\n",
    "from keras_loss_function.keras_ssd_loss import SSDLoss\n",
    "from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes\n",
    "from keras_layers.keras_layer_DecodeDetections import DecodeDetections\n",
    "from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast\n",
    "from keras_layers.keras_layer_L2Normalization import L2Normalization\n",
    "from ssd_encoder_decoder.ssd_input_encoder import SSDInputEncoder\n",
    "from ssd_encoder_decoder.ssd_output_decoder import decode_detections, decode_detections_fast\n",
    "from data_generator.object_detection_2d_data_generator import DataGenerator\n",
    "from data_generator.object_detection_2d_geometric_ops import Resize\n",
    "from data_generator.object_detection_2d_photometric_ops import ConvertTo3Channels\n",
    "from data_generator.data_augmentation_chain_original_ssd import SSDDataAugmentation\n",
    "from data_generator.object_detection_2d_misc_utils import apply_inverse_transforms\n",
    "from eval_utils.average_precision_evaluator import Evaluator\n",
    "from data_generator.data_augmentation_chain_variable_input_size import DataAugmentationVariableInputSize\n",
    "from data_generator.data_augmentation_chain_constant_input_size import DataAugmentationConstantInputSize\n",
    "\n",
    "\n",
    "def makedirs(path):\n",
    "    try:\n",
    "        os.makedirs(path)\n",
    "    except OSError:\n",
    "        if not os.path.isdir(path):\n",
    "            raise\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "K.tensorflow_backend._get_available_gpus()\n",
    "\n",
    "\n",
    "def lr_schedule(epoch):\n",
    "    if epoch < 80:\n",
    "        return 0.001\n",
    "    elif epoch < 100:\n",
    "        return 0.0001\n",
    "    else:\n",
    "        return 0.00001\n",
    "\n",
    "config_path = 'config_7_panel.json'\n",
    "\n",
    "\n",
    "with open(config_path) as config_buffer:\n",
    "    config = json.loads(config_buffer.read())\n",
    "\n",
    "###############################\n",
    "#   Parse the annotations\n",
    "###############################\n",
    "path_imgs_training = config['train']['train_image_folder']\n",
    "path_anns_training = config['train']['train_annot_folder']\n",
    "path_imgs_val =  config['test']['test_image_folder']\n",
    "path_anns_val = config['test']['test_annot_folder']\n",
    "labels = config['model']['labels']\n",
    "categories = {}\n",
    "#categories = {\"Razor\": 1, \"Gun\": 2, \"Knife\": 3, \"Shuriken\": 4} #la categoría 0 es la background\n",
    "for i in range(len(labels)): categories[labels[i]] = i+1\n",
    "print('\\nTraining on: \\t' + str(categories) + '\\n')\n",
    "\n",
    "####################################\n",
    "#   Parameters\n",
    "###################################\n",
    "    #%%\n",
    "img_height = config['model']['input'] # Height of the model input images\n",
    "img_width = config['model']['input'] # Width of the model input images\n",
    "img_channels = 3 # Number of color channels of the model input images\n",
    "mean_color = [123, 117, 104] # The per-channel mean of the images in the dataset. Do not change this value if you're using any of the pre-trained weights.\n",
    "swap_channels = [2, 1, 0] # The color channel order in the original SSD is BGR, so we'll have the model reverse the color channel order of the input images.\n",
    "n_classes = len(labels) # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO\n",
    "scales_pascal = [0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05] # The anchor box scaling factors used in the original SSD300 for the Pascal VOC datasets\n",
    "#scales_coco = [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05] # The anchor box scaling factors used in the original SSD300 for the MS COCO datasets\n",
    "scales = scales_pascal\n",
    "aspect_ratios = [[1.0, 2.0, 0.5],\n",
    "                 [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
    "                 [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
    "                 [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
    "                 [1.0, 2.0, 0.5],\n",
    "                 [1.0, 2.0, 0.5]] # The anchor box aspect ratios used in the original SSD300; the order matters\n",
    "two_boxes_for_ar1 = True\n",
    "steps = [8, 16, 32, 64, 100, 300] # The space between two adjacent anchor box center points for each predictor layer.\n",
    "offsets = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # The offsets of the first anchor box center points from the top and left borders of the image as a fraction of the step size for each predictor layer.\n",
    "clip_boxes = False # Whether or not to clip the anchor boxes to lie entirely within the image boundaries\n",
    "variances = [0.1, 0.1, 0.2, 0.2] # The variances by which the encoded target coordinates are divided as in the original implementation\n",
    "normalize_coords = True\n",
    "\n",
    "K.clear_session() # Clear previous models from memory.\n",
    "\n",
    "\n",
    "model_path = config['train']['saved_weights_name']\n",
    "# 3: Instantiate an optimizer and the SSD loss function and compile the model.\n",
    "#    If you want to follow the original Caffe implementation, use the preset SGD\n",
    "#    optimizer, otherwise I'd recommend the commented-out Adam optimizer.\n",
    "\n",
    "\n",
    "if config['model']['backend'] == 'ssd7':\n",
    "    #weights_path = 'VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
    "    scales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`.\n",
    "    aspect_ratios = [0.5 ,1.0, 2.0] # The list of aspect ratios for the anchor boxes\n",
    "    two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\n",
    "    steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n",
    "    offsets = None\n",
    "\n",
    "if os.path.exists(model_path):\n",
    "    print(\"\\nLoading pretrained weights.\\n\")\n",
    "    # We need to create an SSDLoss object in order to pass that to the model loader.\n",
    "    ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
    "\n",
    "    K.clear_session() # Clear previous models from memory.\n",
    "    model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
    "                                           'L2Normalization': L2Normalization,\n",
    "                                           'compute_loss': ssd_loss.compute_loss})\n",
    "\n",
    "\n",
    "else:\n",
    "    ####################################\n",
    "    #   Build the Keras model.\n",
    "    ###################################\n",
    "\n",
    "    if config['model']['backend'] == 'ssd300':\n",
    "        #weights_path = 'VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.h5'\n",
    "        from models.keras_ssd300 import ssd_300 as ssd\n",
    "\n",
    "        model = ssd_300(image_size=(img_height, img_width, img_channels),\n",
    "                n_classes=n_classes,\n",
    "                mode='training',\n",
    "                l2_regularization=0.0005,\n",
    "                scales=scales,\n",
    "                aspect_ratios_per_layer=aspect_ratios,\n",
    "                two_boxes_for_ar1=two_boxes_for_ar1,\n",
    "                steps=steps,\n",
    "                offsets=offsets,\n",
    "                clip_boxes=clip_boxes,\n",
    "                variances=variances,\n",
    "                normalize_coords=normalize_coords,\n",
    "                subtract_mean=mean_color,\n",
    "                swap_channels=swap_channels)\n",
    "\n",
    "\n",
    "    elif config['model']['backend'] == 'ssd7':\n",
    "        #weights_path = 'VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
    "        from models.keras_ssd7 import build_model as ssd\n",
    "        scales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`.\n",
    "        aspect_ratios = [0.5 ,1.0, 2.0] # The list of aspect ratios for the anchor boxes\n",
    "        two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\n",
    "        steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n",
    "        offsets = None\n",
    "        model = ssd(image_size=(img_height, img_width, img_channels),\n",
    "                n_classes=n_classes,\n",
    "                mode='training',\n",
    "                l2_regularization=0.0005,\n",
    "                scales=scales,\n",
    "                aspect_ratios_global=aspect_ratios,\n",
    "                aspect_ratios_per_layer=None,\n",
    "                two_boxes_for_ar1=two_boxes_for_ar1,\n",
    "                steps=steps,\n",
    "                offsets=offsets,\n",
    "                clip_boxes=clip_boxes,\n",
    "                variances=variances,\n",
    "                normalize_coords=normalize_coords,\n",
    "                subtract_mean=None,\n",
    "                divide_by_stddev=None)\n",
    "\n",
    "    else :\n",
    "        print('Wrong Backend')\n",
    "\n",
    "\n",
    "\n",
    "    print('OK create model')\n",
    "     #sgd = SGD(lr=config['train']['learning_rate'], momentum=0.9, decay=0.0, nesterov=False)\n",
    "\n",
    "    # TODO: Set the path to the weights you want to load. only for ssd300 or ssd512\n",
    "\n",
    "    weights_path = '../ssd_keras-master/VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
    "    print(\"\\nLoading pretrained weights VGG.\\n\")\n",
    "    model.load_weights(weights_path, by_name=True)\n",
    "\n",
    "    # 3: Instantiate an optimizer and the SSD loss function and compile the model.\n",
    "    #    If you want to follow the original Caffe implementation, use the preset SGD\n",
    "    #    optimizer, otherwise I'd recommend the commented-out Adam optimizer.\n",
    "\n",
    "\n",
    "    #adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "    #sgd = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)\n",
    "    optimizer = Adam(lr=config['train']['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "    ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
    "    model.compile(optimizer=optimizer, loss=ssd_loss.compute_loss)\n",
    "\n",
    "    model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instanciar los generadores de datos y entrenamiento del modelo.\n",
    "\n",
    "*Cambio realizado para leer png y jpg. keras-ssd-master/data_generator/object_detection_2d_data_generator.py función parse_xml\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing image set 'train.txt': 100%|██████████| 1/1 [00:00<00:00,  3.02it/s]\n",
      "Processing image set 'test.txt': 100%|██████████| 1/1 [00:00<00:00,  2.48it/s]\n",
      "panel : 69\n",
      "cell : 423\n",
      "Number of images in the training dataset:\t     1\n",
      "Number of images in the validation dataset:\t     1\n",
      "Epoch 1/100\n",
      "\n",
      "Epoch 00001: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 200s 4s/step - loss: 13.2409 - val_loss: 9.9807\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 9.98075, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 2/100\n",
      "\n",
      "Epoch 00002: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 238s 5s/step - loss: 9.8864 - val_loss: 11.1452\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 9.98075\n",
      "Epoch 3/100\n",
      "\n",
      "Epoch 00003: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 226s 5s/step - loss: 8.8060 - val_loss: 8.3006\n",
      "\n",
      "Epoch 00003: val_loss improved from 9.98075 to 8.30060, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 4/100\n",
      "\n",
      "Epoch 00004: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 199s 4s/step - loss: 7.4999 - val_loss: 8.9384\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 8.30060\n",
      "Epoch 5/100\n",
      "\n",
      "Epoch 00005: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 187s 4s/step - loss: 7.4727 - val_loss: 7.9512\n",
      "\n",
      "Epoch 00005: val_loss improved from 8.30060 to 7.95121, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 6/100\n",
      "\n",
      "Epoch 00006: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 213s 4s/step - loss: 6.8813 - val_loss: 11.2544\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 7.95121\n",
      "Epoch 7/100\n",
      "\n",
      "Epoch 00007: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 195s 4s/step - loss: 6.4775 - val_loss: 6.9093\n",
      "\n",
      "Epoch 00007: val_loss improved from 7.95121 to 6.90929, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 8/100\n",
      "\n",
      "Epoch 00008: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 212s 4s/step - loss: 6.9758 - val_loss: 8.6997\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 6.90929\n",
      "Epoch 9/100\n",
      "\n",
      "Epoch 00009: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 199s 4s/step - loss: 6.1539 - val_loss: 10.9586\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 6.90929\n",
      "Epoch 10/100\n",
      "\n",
      "Epoch 00010: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 206s 4s/step - loss: 5.9307 - val_loss: 8.4361\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 6.90929\n",
      "Epoch 11/100\n",
      "\n",
      "Epoch 00011: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 197s 4s/step - loss: 5.3895 - val_loss: 5.9796\n",
      "\n",
      "Epoch 00011: val_loss improved from 6.90929 to 5.97960, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 12/100\n",
      "\n",
      "Epoch 00012: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 184s 4s/step - loss: 5.0889 - val_loss: 5.9283\n",
      "\n",
      "Epoch 00012: val_loss improved from 5.97960 to 5.92832, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 13/100\n",
      "\n",
      "Epoch 00013: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 193s 4s/step - loss: 5.7916 - val_loss: 6.7706\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 5.92832\n",
      "Epoch 14/100\n",
      "\n",
      "Epoch 00014: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 222s 4s/step - loss: 5.3010 - val_loss: 7.8910\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 5.92832\n",
      "Epoch 15/100\n",
      "\n",
      "Epoch 00015: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 179s 4s/step - loss: 4.9873 - val_loss: 6.0389\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 5.92832\n",
      "Epoch 16/100\n",
      "\n",
      "Epoch 00016: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 182s 4s/step - loss: 5.4664 - val_loss: 6.4125\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 5.92832\n",
      "Epoch 17/100\n",
      "\n",
      "Epoch 00017: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 166s 3s/step - loss: 6.0094 - val_loss: 9.2918\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 5.92832\n",
      "Epoch 18/100\n",
      "\n",
      "Epoch 00018: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 181s 4s/step - loss: 5.1737 - val_loss: 7.6806\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 5.92832\n",
      "Epoch 19/100\n",
      "\n",
      "Epoch 00019: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 159s 3s/step - loss: 5.2708 - val_loss: 7.1096\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 5.92832\n",
      "Epoch 20/100\n",
      "\n",
      "Epoch 00020: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 173s 3s/step - loss: 5.4765 - val_loss: 5.4921\n",
      "\n",
      "Epoch 00020: val_loss improved from 5.92832 to 5.49211, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 21/100\n",
      "\n",
      "Epoch 00021: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 170s 3s/step - loss: 4.6517 - val_loss: 6.6033\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 5.49211\n",
      "Epoch 22/100\n",
      "\n",
      "Epoch 00022: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 191s 4s/step - loss: 5.1432 - val_loss: 5.6549\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 5.49211\n",
      "Epoch 23/100\n",
      "\n",
      "Epoch 00023: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 159s 3s/step - loss: 5.4830 - val_loss: 5.8758\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 5.49211\n",
      "Epoch 24/100\n",
      "\n",
      "Epoch 00024: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 150s 3s/step - loss: 5.3366 - val_loss: 5.3871\n",
      "\n",
      "Epoch 00024: val_loss improved from 5.49211 to 5.38706, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 25/100\n",
      "\n",
      "Epoch 00025: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 138s 3s/step - loss: 5.7189 - val_loss: 8.0760\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 5.38706\n",
      "Epoch 26/100\n",
      "\n",
      "Epoch 00026: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 144s 3s/step - loss: 6.0929 - val_loss: 12.6163\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 5.38706\n",
      "Epoch 27/100\n",
      "\n",
      "Epoch 00027: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 147s 3s/step - loss: 5.2239 - val_loss: 9.8536\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 5.38706\n",
      "Epoch 28/100\n",
      "\n",
      "Epoch 00028: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 158s 3s/step - loss: 5.4414 - val_loss: 6.4950\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 5.38706\n",
      "Epoch 29/100\n",
      "\n",
      "Epoch 00029: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 157s 3s/step - loss: 5.4436 - val_loss: 9.0002\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 5.38706\n",
      "Epoch 30/100\n",
      "\n",
      "Epoch 00030: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 162s 3s/step - loss: 4.9780 - val_loss: 4.9993\n",
      "\n",
      "Epoch 00030: val_loss improved from 5.38706 to 4.99925, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 31/100\n",
      "\n",
      "Epoch 00031: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 140s 3s/step - loss: 4.9645 - val_loss: 5.6612\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 4.99925\n",
      "Epoch 32/100\n",
      "\n",
      "Epoch 00032: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 141s 3s/step - loss: 4.5982 - val_loss: 5.2083\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 4.99925\n",
      "Epoch 33/100\n",
      "\n",
      "Epoch 00033: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 143s 3s/step - loss: 4.3101 - val_loss: 6.4808\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 4.99925\n",
      "Epoch 34/100\n",
      "\n",
      "Epoch 00034: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 145s 3s/step - loss: 4.4252 - val_loss: 10.9472\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00034: val_loss did not improve from 4.99925\n",
      "Epoch 35/100\n",
      "\n",
      "Epoch 00035: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 153s 3s/step - loss: 4.4998 - val_loss: 7.1254\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 4.99925\n",
      "Epoch 36/100\n",
      "\n",
      "Epoch 00036: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 153s 3s/step - loss: 4.8952 - val_loss: 7.0446\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 4.99925\n",
      "Epoch 37/100\n",
      "\n",
      "Epoch 00037: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 154s 3s/step - loss: 4.9868 - val_loss: 9.3251\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 4.99925\n",
      "Epoch 38/100\n",
      "\n",
      "Epoch 00038: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 148s 3s/step - loss: 4.8918 - val_loss: 5.1689\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 4.99925\n",
      "Epoch 39/100\n",
      "\n",
      "Epoch 00039: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 143s 3s/step - loss: 4.5572 - val_loss: 4.9839\n",
      "\n",
      "Epoch 00039: val_loss improved from 4.99925 to 4.98394, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 40/100\n",
      "\n",
      "Epoch 00040: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 150s 3s/step - loss: 4.4722 - val_loss: 5.7133\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 4.98394\n",
      "Epoch 41/100\n",
      "\n",
      "Epoch 00041: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 152s 3s/step - loss: 4.9414 - val_loss: 5.5843\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 4.98394\n",
      "Epoch 42/100\n",
      "\n",
      "Epoch 00042: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 148s 3s/step - loss: 4.5857 - val_loss: 5.1884\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 4.98394\n",
      "Epoch 43/100\n",
      "\n",
      "Epoch 00043: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 149s 3s/step - loss: 4.7094 - val_loss: 6.7545\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 4.98394\n",
      "Epoch 44/100\n",
      "\n",
      "Epoch 00044: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 151s 3s/step - loss: 5.0428 - val_loss: 5.2691\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 4.98394\n",
      "Epoch 45/100\n",
      "\n",
      "Epoch 00045: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 146s 3s/step - loss: 4.9842 - val_loss: 6.5112\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 4.98394\n",
      "Epoch 46/100\n",
      "\n",
      "Epoch 00046: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 147s 3s/step - loss: 4.9108 - val_loss: 6.0670\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 4.98394\n",
      "Epoch 47/100\n",
      "\n",
      "Epoch 00047: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 155s 3s/step - loss: 4.6837 - val_loss: 5.8351\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 4.98394\n",
      "Epoch 48/100\n",
      "\n",
      "Epoch 00048: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 149s 3s/step - loss: 5.1042 - val_loss: 5.1778\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 4.98394\n",
      "Epoch 49/100\n",
      "\n",
      "Epoch 00049: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 144s 3s/step - loss: 4.1312 - val_loss: 5.9606\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 4.98394\n",
      "Epoch 50/100\n",
      "\n",
      "Epoch 00050: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 122s 2s/step - loss: 4.5373 - val_loss: 5.4351\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 4.98394\n",
      "Epoch 51/100\n",
      "\n",
      "Epoch 00051: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 135s 3s/step - loss: 4.8955 - val_loss: 6.0315\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 4.98394\n",
      "Epoch 52/100\n",
      "\n",
      "Epoch 00052: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 150s 3s/step - loss: 4.9445 - val_loss: 5.7199\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 4.98394\n",
      "Epoch 53/100\n",
      "\n",
      "Epoch 00053: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 139s 3s/step - loss: 3.9748 - val_loss: 5.5974\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 4.98394\n",
      "Epoch 54/100\n",
      "\n",
      "Epoch 00054: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 153s 3s/step - loss: 4.8783 - val_loss: 8.6056\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 4.98394\n",
      "Epoch 55/100\n",
      "\n",
      "Epoch 00055: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 141s 3s/step - loss: 4.1649 - val_loss: 6.0042\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 4.98394\n",
      "Epoch 56/100\n",
      "\n",
      "Epoch 00056: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 149s 3s/step - loss: 4.8997 - val_loss: 9.1298\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 4.98394\n",
      "Epoch 57/100\n",
      "\n",
      "Epoch 00057: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 151s 3s/step - loss: 4.4433 - val_loss: 7.1151\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 4.98394\n",
      "Epoch 58/100\n",
      "\n",
      "Epoch 00058: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 147s 3s/step - loss: 4.5827 - val_loss: 5.4356\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 4.98394\n",
      "Epoch 59/100\n",
      "\n",
      "Epoch 00059: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 137s 3s/step - loss: 3.9437 - val_loss: 4.7926\n",
      "\n",
      "Epoch 00059: val_loss improved from 4.98394 to 4.79262, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 60/100\n",
      "\n",
      "Epoch 00060: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 125s 3s/step - loss: 4.0939 - val_loss: 5.7098\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 4.79262\n",
      "Epoch 61/100\n",
      "\n",
      "Epoch 00061: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 161s 3s/step - loss: 5.1152 - val_loss: 5.2079\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 4.79262\n",
      "Epoch 62/100\n",
      "\n",
      "Epoch 00062: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 144s 3s/step - loss: 4.2958 - val_loss: 4.9239\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 4.79262\n",
      "Epoch 63/100\n",
      "\n",
      "Epoch 00063: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 141s 3s/step - loss: 3.8241 - val_loss: 4.5443\n",
      "\n",
      "Epoch 00063: val_loss improved from 4.79262 to 4.54430, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 64/100\n",
      "\n",
      "Epoch 00064: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 134s 3s/step - loss: 4.7252 - val_loss: 5.9445\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 4.54430\n",
      "Epoch 65/100\n",
      "\n",
      "Epoch 00065: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 154s 3s/step - loss: 4.4455 - val_loss: 4.8326\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 4.54430\n",
      "Epoch 66/100\n",
      "\n",
      "Epoch 00066: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 145s 3s/step - loss: 4.4054 - val_loss: 5.6441\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 4.54430\n",
      "Epoch 67/100\n",
      "\n",
      "Epoch 00067: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 124s 2s/step - loss: 4.4165 - val_loss: 6.8159\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 4.54430\n",
      "Epoch 68/100\n",
      "\n",
      "Epoch 00068: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 162s 3s/step - loss: 5.0418 - val_loss: 4.8508\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 4.54430\n",
      "Epoch 69/100\n",
      "\n",
      "Epoch 00069: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 140s 3s/step - loss: 4.1512 - val_loss: 5.4053\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 4.54430\n",
      "Epoch 70/100\n",
      "\n",
      "Epoch 00070: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 148s 3s/step - loss: 4.6197 - val_loss: 5.2824\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 4.54430\n",
      "Epoch 71/100\n",
      "\n",
      "Epoch 00071: LearningRateScheduler setting learning rate to 0.001.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 152s 3s/step - loss: 4.2807 - val_loss: 5.5992\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 4.54430\n",
      "Epoch 72/100\n",
      "\n",
      "Epoch 00072: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 143s 3s/step - loss: 4.5368 - val_loss: 6.5207\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 4.54430\n",
      "Epoch 73/100\n",
      "\n",
      "Epoch 00073: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 141s 3s/step - loss: 4.0598 - val_loss: 5.2421\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 4.54430\n",
      "Epoch 74/100\n",
      "\n",
      "Epoch 00074: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 150s 3s/step - loss: 4.4861 - val_loss: 5.4182\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 4.54430\n",
      "Epoch 75/100\n",
      "\n",
      "Epoch 00075: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 144s 3s/step - loss: 4.5263 - val_loss: 4.3774\n",
      "\n",
      "Epoch 00075: val_loss improved from 4.54430 to 4.37742, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 76/100\n",
      "\n",
      "Epoch 00076: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 148s 3s/step - loss: 3.8465 - val_loss: 4.5809\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 4.37742\n",
      "Epoch 77/100\n",
      "\n",
      "Epoch 00077: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 152s 3s/step - loss: 4.0495 - val_loss: 4.9745\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 4.37742\n",
      "Epoch 78/100\n",
      "\n",
      "Epoch 00078: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 152s 3s/step - loss: 4.6009 - val_loss: 13.4989\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 4.37742\n",
      "Epoch 79/100\n",
      "\n",
      "Epoch 00079: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 142s 3s/step - loss: 4.6687 - val_loss: 6.4490\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 4.37742\n",
      "Epoch 80/100\n",
      "\n",
      "Epoch 00080: LearningRateScheduler setting learning rate to 0.001.\n",
      "50/50 [==============================] - 147s 3s/step - loss: 4.5297 - val_loss: 8.0478\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 4.37742\n",
      "Epoch 81/100\n",
      "\n",
      "Epoch 00081: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 141s 3s/step - loss: 4.2662 - val_loss: 5.7929\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 4.37742\n",
      "Epoch 82/100\n",
      "\n",
      "Epoch 00082: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 149s 3s/step - loss: 4.1048 - val_loss: 4.6117\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 4.37742\n",
      "Epoch 83/100\n",
      "\n",
      "Epoch 00083: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 156s 3s/step - loss: 3.9905 - val_loss: 4.5542\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 4.37742\n",
      "Epoch 84/100\n",
      "\n",
      "Epoch 00084: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 155s 3s/step - loss: 4.3129 - val_loss: 4.4676\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 4.37742\n",
      "Epoch 85/100\n",
      "\n",
      "Epoch 00085: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 156s 3s/step - loss: 3.7951 - val_loss: 4.4689\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 4.37742\n",
      "Epoch 86/100\n",
      "\n",
      "Epoch 00086: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 155s 3s/step - loss: 4.3618 - val_loss: 4.4048\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 4.37742\n",
      "Epoch 87/100\n",
      "\n",
      "Epoch 00087: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 156s 3s/step - loss: 4.3538 - val_loss: 4.6832\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 4.37742\n",
      "Epoch 88/100\n",
      "\n",
      "Epoch 00088: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 152s 3s/step - loss: 4.2076 - val_loss: 4.4796\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 4.37742\n",
      "Epoch 89/100\n",
      "\n",
      "Epoch 00089: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 146s 3s/step - loss: 4.1322 - val_loss: 4.5462\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 4.37742\n",
      "Epoch 90/100\n",
      "\n",
      "Epoch 00090: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 157s 3s/step - loss: 4.4995 - val_loss: 4.5660\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 4.37742\n",
      "Epoch 91/100\n",
      "\n",
      "Epoch 00091: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 158s 3s/step - loss: 4.2653 - val_loss: 4.5265\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 4.37742\n",
      "Epoch 92/100\n",
      "\n",
      "Epoch 00092: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 153s 3s/step - loss: 4.3702 - val_loss: 4.5276\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 4.37742\n",
      "Epoch 93/100\n",
      "\n",
      "Epoch 00093: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 153s 3s/step - loss: 3.7340 - val_loss: 4.5439\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 4.37742\n",
      "Epoch 94/100\n",
      "\n",
      "Epoch 00094: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 151s 3s/step - loss: 4.0253 - val_loss: 4.3250\n",
      "\n",
      "Epoch 00094: val_loss improved from 4.37742 to 4.32498, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 95/100\n",
      "\n",
      "Epoch 00095: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 143s 3s/step - loss: 4.0254 - val_loss: 4.6277\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 4.32498\n",
      "Epoch 96/100\n",
      "\n",
      "Epoch 00096: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 148s 3s/step - loss: 3.9857 - val_loss: 4.2953\n",
      "\n",
      "Epoch 00096: val_loss improved from 4.32498 to 4.29533, saving model to experimento_ssd7_panel_cell.h5\n",
      "Epoch 97/100\n",
      "\n",
      "Epoch 00097: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 157s 3s/step - loss: 3.6750 - val_loss: 4.5637\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 4.29533\n",
      "Epoch 98/100\n",
      "\n",
      "Epoch 00098: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 154s 3s/step - loss: 3.7435 - val_loss: 4.3923\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 4.29533\n",
      "Epoch 99/100\n",
      "\n",
      "Epoch 00099: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 162s 3s/step - loss: 4.0930 - val_loss: 4.4010\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 4.29533\n",
      "Epoch 100/100\n",
      "\n",
      "Epoch 00100: LearningRateScheduler setting learning rate to 0.0001.\n",
      "50/50 [==============================] - 134s 3s/step - loss: 3.8983 - val_loss: 4.4451\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 4.29533\n"
     ]
    }
   ],
   "source": [
    "#ENTRENAMIENTO DE MODELO\n",
    "#####################################################################\n",
    "#  Instantiate two `DataGenerator` objects: One for training, one for validation.\n",
    "######################################################################\n",
    "# Optional: If you have enough memory, consider loading the images into memory for the reasons explained above.\n",
    "\n",
    "train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
    "val_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
    "\n",
    "# 2: Parse the image and label lists for the training and validation datasets. This can take a while.\n",
    "\n",
    "\n",
    "\n",
    "# The XML parser needs to now what object class names to look for and in which order to map them to integers.\n",
    "classes = ['background' ] + labels\n",
    "\n",
    "train_dataset.parse_xml(images_dirs= [config['train']['train_image_folder']],\n",
    "                        image_set_filenames=[config['train']['train_image_set_filename']],\n",
    "                        annotations_dirs=[config['train']['train_annot_folder']],\n",
    "                        classes=classes,\n",
    "                        include_classes='all',\n",
    "                        #classes = ['background', 'panel', 'cell'], \n",
    "                        #include_classes=classes,\n",
    "                        exclude_truncated=False,\n",
    "                        exclude_difficult=False,\n",
    "                        ret=False)\n",
    "\n",
    "val_dataset.parse_xml(images_dirs= [config['test']['test_image_folder']],\n",
    "                        image_set_filenames=[config['test']['test_image_set_filename']],\n",
    "                        annotations_dirs=[config['test']['test_annot_folder']],\n",
    "                        classes=classes,\n",
    "                        include_classes='all',\n",
    "                        #classes = ['background', 'panel', 'cell'], \n",
    "                        #include_classes=classes,\n",
    "                        exclude_truncated=False,\n",
    "                        exclude_difficult=False,\n",
    "                        ret=False)\n",
    "\n",
    "#########################\n",
    "# 3: Set the batch size.\n",
    "#########################\n",
    "batch_size = config['train']['batch_size'] # Change the batch size if you like, or if you run into GPU memory issues.\n",
    "\n",
    "##########################\n",
    "# 4: Set the image transformations for pre-processing and data augmentation options.\n",
    "##########################\n",
    "# For the training generator:\n",
    "\n",
    "\n",
    "# For the validation generator:\n",
    "convert_to_3_channels = ConvertTo3Channels()\n",
    "resize = Resize(height=img_height, width=img_width)\n",
    "\n",
    "######################################3\n",
    "# 5: Instantiate an encoder that can encode ground truth labels into the format needed by the SSD loss function.\n",
    "#########################################\n",
    "# The encoder constructor needs the spatial dimensions of the model's predictor layers to create the anchor boxes.\n",
    "if config['model']['backend'] == 'ssd300':\n",
    "    predictor_sizes = [model.get_layer('conv4_3_norm_mbox_conf').output_shape[1:3],\n",
    "                       model.get_layer('fc7_mbox_conf').output_shape[1:3],\n",
    "                       model.get_layer('conv6_2_mbox_conf').output_shape[1:3],\n",
    "                       model.get_layer('conv7_2_mbox_conf').output_shape[1:3],\n",
    "                       model.get_layer('conv8_2_mbox_conf').output_shape[1:3],\n",
    "                       model.get_layer('conv9_2_mbox_conf').output_shape[1:3]]\n",
    "    ssd_input_encoder = SSDInputEncoder(img_height=img_height,\n",
    "                                        img_width=img_width,\n",
    "                                        n_classes=n_classes,\n",
    "                                        predictor_sizes=predictor_sizes,\n",
    "                                        scales=scales,\n",
    "                                        aspect_ratios_per_layer=aspect_ratios,\n",
    "                                        two_boxes_for_ar1=two_boxes_for_ar1,\n",
    "                                        steps=steps,\n",
    "                                        offsets=offsets,\n",
    "                                        clip_boxes=clip_boxes,\n",
    "                                        variances=variances,\n",
    "                                        matching_type='multi',\n",
    "                                        pos_iou_threshold=0.5,\n",
    "                                        neg_iou_limit=0.5,\n",
    "                                        normalize_coords=normalize_coords)\n",
    "\n",
    "elif config['model']['backend'] == 'ssd7':\n",
    "    predictor_sizes = [model.get_layer('classes4').output_shape[1:3],\n",
    "                       model.get_layer('classes5').output_shape[1:3],\n",
    "                       model.get_layer('classes6').output_shape[1:3],\n",
    "                       model.get_layer('classes7').output_shape[1:3]]\n",
    "    ssd_input_encoder = SSDInputEncoder(img_height=img_height,\n",
    "                                img_width=img_width,\n",
    "                                n_classes=n_classes,\n",
    "                                predictor_sizes=predictor_sizes,\n",
    "                                scales=scales,\n",
    "                                aspect_ratios_global=aspect_ratios,\n",
    "                                two_boxes_for_ar1=two_boxes_for_ar1,\n",
    "                                steps=steps,\n",
    "                                offsets=offsets,\n",
    "                                clip_boxes=clip_boxes,\n",
    "                                variances=variances,\n",
    "                                matching_type='multi',\n",
    "                                pos_iou_threshold=0.5,\n",
    "                                neg_iou_limit=0.3,\n",
    "                                normalize_coords=normalize_coords)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "data_augmentation_chain = DataAugmentationVariableInputSize(resize_height = img_height,\n",
    "                                                            resize_width = img_width,\n",
    "                                                            random_brightness=(-48, 48, 0.5),\n",
    "                                                            random_contrast=(0.5, 1.8, 0.5),\n",
    "                                                            random_saturation=(0.5, 1.8, 0.5),\n",
    "                                                            random_hue=(18, 0.5),\n",
    "                                                            random_flip=0.5,\n",
    "                                                            n_trials_max=3,\n",
    "                                                            clip_boxes=True,\n",
    "                                                            overlap_criterion='area',\n",
    "                                                            bounds_box_filter=(0.3, 1.0),\n",
    "                                                            bounds_validator=(0.5, 1.0),\n",
    "                                                            n_boxes_min=1,\n",
    "                                                            background=(0,0,0))\n",
    "#######################\n",
    "# 6: Create the generator handles that will be passed to Keras' `fit_generator()` function.\n",
    "#######################\n",
    "\n",
    "train_generator = train_dataset.generate(batch_size=batch_size,\n",
    "                                         shuffle=True,\n",
    "                                         transformations=  [data_augmentation_chain],\n",
    "                                         label_encoder=ssd_input_encoder,\n",
    "                                         returns={'processed_images',\n",
    "                                                  'encoded_labels'},\n",
    "                                         keep_images_without_gt=False)\n",
    "\n",
    "val_generator = val_dataset.generate(batch_size=batch_size,\n",
    "                                     shuffle=False,\n",
    "                                     transformations=[convert_to_3_channels,\n",
    "                                                      resize],\n",
    "                                     label_encoder=ssd_input_encoder,\n",
    "                                     returns={'processed_images',\n",
    "                                              'encoded_labels'},\n",
    "                                     keep_images_without_gt=False)\n",
    "\n",
    "# Summary instance training\n",
    "category_train_list = []\n",
    "for image_label in train_dataset.labels:\n",
    "    category_train_list += [i[0] for i in train_dataset.labels[0]]\n",
    "summary_category_training = {train_dataset.classes[i]: category_train_list.count(i) for i in list(set(category_train_list))}\n",
    "for i in summary_category_training.keys():\n",
    "     print(i, ': {:.0f}'.format(summary_category_training[i]))\n",
    "\n",
    "\n",
    "\n",
    "# Get the number of samples in the training and validations datasets.\n",
    "train_dataset_size = train_dataset.get_dataset_size()\n",
    "val_dataset_size   = val_dataset.get_dataset_size()\n",
    "\n",
    "print(\"Number of images in the training dataset:\\t{:>6}\".format(train_dataset_size))\n",
    "print(\"Number of images in the validation dataset:\\t{:>6}\".format(val_dataset_size))\n",
    "\n",
    "\n",
    "\n",
    "##########################\n",
    "# Define model callbacks.\n",
    "#########################\n",
    "\n",
    "# TODO: Set the filepath under which you want to save the model.\n",
    "model_checkpoint = ModelCheckpoint(filepath= config['train']['saved_weights_name'],\n",
    "                                   monitor='val_loss',\n",
    "                                   verbose=1,\n",
    "                                   save_best_only=True,\n",
    "                                   save_weights_only=False,\n",
    "                                   mode='auto',\n",
    "                                   period=1)\n",
    "#model_checkpoint.best =\n",
    "\n",
    "csv_logger = CSVLogger(filename='log.csv',\n",
    "                       separator=',',\n",
    "                       append=True)\n",
    "\n",
    "learning_rate_scheduler = LearningRateScheduler(schedule=lr_schedule,\n",
    "                                                verbose=1)\n",
    "\n",
    "terminate_on_nan = TerminateOnNaN()\n",
    "\n",
    "callbacks = [model_checkpoint,\n",
    "             csv_logger,\n",
    "             learning_rate_scheduler,\n",
    "             terminate_on_nan]\n",
    "\n",
    "\n",
    "\n",
    "batch_images, batch_labels = next(train_generator)\n",
    "\n",
    "\n",
    "initial_epoch   = 0\n",
    "final_epoch     = 100 #config['train']['nb_epochs']\n",
    "steps_per_epoch = 50\n",
    "\n",
    "history = model.fit_generator(generator=train_generator,\n",
    "                              steps_per_epoch=steps_per_epoch,\n",
    "                              epochs=final_epoch,\n",
    "                              callbacks=callbacks,\n",
    "                              validation_data=val_generator,\n",
    "                              validation_steps=ceil(val_dataset_size/batch_size),\n",
    "                              initial_epoch=initial_epoch,\n",
    "                              verbose = 1 if config['train']['debug'] else 2)\n",
    "\n",
    "history_path = config['train']['saved_weights_name'].split('.')[0] + '_history'\n",
    "\n",
    "np.save(history_path, history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'loss', 'lr'])\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 30520 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "experimento_ssd7_panel.h5\n"
     ]
    }
   ],
   "source": [
    "#Graficar aprendizaje\n",
    "\n",
    "history_path =config['train']['saved_weights_name'].split('.')[0] + '_history'\n",
    "\n",
    "hist_load = np.load(history_path + '.npy',allow_pickle=True).item()\n",
    "\n",
    "print(hist_load.keys())\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(hist_load['loss'])\n",
    "plt.plot(hist_load['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "print(config['train']['saved_weights_name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluación del Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing image set 'train.txt': 100%|██████████| 1/1 [00:00<00:00, 18.71it/s]\n",
      "Processing image set 'test.txt': 100%|██████████| 1/1 [00:00<00:00, 25.23it/s]\n",
      "Number of images in the evaluation dataset: 1\n",
      "\n",
      "Producing predictions batch-wise: 100%|██████████| 1/1 [00:00<00:00,  1.82it/s]\n",
      "Matching predictions to ground truth, class 1/1.: 100%|██████████| 200/200 [00:00<00:00, 6641.60it/s]\n",
      "Computing precisions and recalls, class 1/1\n",
      "Computing average precision, class 1/1\n",
      "200 instances of class panel with average precision: 0.8982\n",
      "mAP using the weighted average of precisions among classes: 0.8982\n",
      "mAP: 0.8982\n",
      "panel         AP    0.898\n",
      "\n",
      "              mAP   0.898\n"
     ]
    }
   ],
   "source": [
    "\n",
    "config_path = 'config_7_panel.json'\n",
    "\n",
    "with open(config_path) as config_buffer:\n",
    "    config = json.loads(config_buffer.read())\n",
    "\n",
    "    \n",
    "model_mode = 'training'\n",
    "# TODO: Set the path to the `.h5` file of the model to be loaded.\n",
    "model_path = config['train']['saved_weights_name']\n",
    "\n",
    "# We need to create an SSDLoss object in order to pass that to the model loader.\n",
    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
    "\n",
    "K.clear_session() # Clear previous models from memory.\n",
    "\n",
    "model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
    "                                               'L2Normalization': L2Normalization,\n",
    "                                               'DecodeDetections': DecodeDetections,\n",
    "                                               'compute_loss': ssd_loss.compute_loss})\n",
    "\n",
    "\n",
    "    \n",
    "train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
    "val_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
    "\n",
    "# 2: Parse the image and label lists for the training and validation datasets. This can take a while.\n",
    "\n",
    "\n",
    "\n",
    "# The XML parser needs to now what object class names to look for and in which order to map them to integers.\n",
    "classes = ['background' ] + labels\n",
    "\n",
    "train_dataset.parse_xml(images_dirs= [config['train']['train_image_folder']],\n",
    "                        image_set_filenames=[config['train']['train_image_set_filename']],\n",
    "                        annotations_dirs=[config['train']['train_annot_folder']],\n",
    "                        classes=classes,\n",
    "                        include_classes='all',\n",
    "                        #classes = ['background', 'panel', 'cell'], \n",
    "                        #include_classes=classes,\n",
    "                        exclude_truncated=False,\n",
    "                        exclude_difficult=False,\n",
    "                        ret=False)\n",
    "\n",
    "val_dataset.parse_xml(images_dirs= [config['test']['test_image_folder']],\n",
    "                        image_set_filenames=[config['test']['test_image_set_filename']],\n",
    "                        annotations_dirs=[config['test']['test_annot_folder']],\n",
    "                        classes=classes,\n",
    "                        include_classes='all',\n",
    "                        #classes = ['background', 'panel', 'cell'], \n",
    "                        #include_classes=classes,\n",
    "                        exclude_truncated=False,\n",
    "                        exclude_difficult=False,\n",
    "                        ret=False)\n",
    "\n",
    "#########################\n",
    "# 3: Set the batch size.\n",
    "#########################\n",
    "batch_size = config['train']['batch_size'] # Change the batch size if you like, or if you run into GPU memory issues.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "evaluator = Evaluator(model=model,\n",
    "                      n_classes=n_classes,\n",
    "                      data_generator=val_dataset,\n",
    "                      model_mode='training')\n",
    "\n",
    "results = evaluator(img_height=img_height,\n",
    "                    img_width=img_width,\n",
    "                    batch_size=4,\n",
    "                    data_generator_mode='resize',\n",
    "                    round_confidences=False,\n",
    "                    matching_iou_threshold=0.5,\n",
    "                    border_pixels='include',\n",
    "                    sorting_algorithm='quicksort',\n",
    "                    average_precision_mode='sample',\n",
    "                    num_recall_points=11,\n",
    "                    ignore_neutral_boxes=True,\n",
    "                    return_precisions=True,\n",
    "                    return_recalls=True,\n",
    "                    return_average_precisions=True,\n",
    "                    verbose=True)\n",
    "\n",
    "mean_average_precision, average_precisions, precisions, recalls = results\n",
    "total_instances = []\n",
    "precisions = []\n",
    "\n",
    "for i in range(1, len(average_precisions)):\n",
    "    \n",
    "    print('{:.0f} instances of class'.format(len(recalls[i])),\n",
    "          classes[i], 'with average precision: {:.4f}'.format(average_precisions[i]))\n",
    "    total_instances.append(len(recalls[i]))\n",
    "    precisions.append(average_precisions[i])\n",
    "\n",
    "if sum(total_instances) == 0:\n",
    "    \n",
    "    print('No test instances found.')\n",
    "\n",
    "else:\n",
    "\n",
    "    print('mAP using the weighted average of precisions among classes: {:.4f}'.format(sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances)))\n",
    "    print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))\n",
    "\n",
    "    for i in range(1, len(average_precisions)):\n",
    "        print(\"{:<14}{:<6}{}\".format(classes[i], 'AP', round(average_precisions[i], 3)))\n",
    "    print()\n",
    "    print(\"{:<14}{:<6}{}\".format('','mAP', round(mean_average_precision, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargar nuevamente el modelo desde los pesos.\n",
    "Predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training on: \t{'panel': 1}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#############################\n",
    "####Prediction\n",
    "#############################\n",
    "\n",
    "from imageio import imread\n",
    "from keras.preprocessing import image\n",
    "import time\n",
    "\n",
    "def makedirs(path):\n",
    "    try:\n",
    "        os.makedirs(path)\n",
    "    except OSError:\n",
    "        if not os.path.isdir(path):\n",
    "            raise\n",
    "\n",
    "\n",
    "config_path = 'config_7_panel.json'\n",
    "input_path = ['Prueba_1/']#input_path = ['../panel_jpg/Mision_1/', '../panel_jpg/Mision_2/']\n",
    "output_path = 'result_ssd7_panel/'\n",
    "\n",
    "with open(config_path) as config_buffer:\n",
    "    config = json.loads(config_buffer.read())\n",
    "\n",
    "makedirs(output_path)\n",
    "###############################\n",
    "#   Parse the annotations\n",
    "###############################\n",
    "score_threshold = 0.95\n",
    "score_threshold_iou = 0.3\n",
    "labels = config['model']['labels']\n",
    "categories = {}\n",
    "#categories = {\"Razor\": 1, \"Gun\": 2, \"Knife\": 3, \"Shuriken\": 4} #la categoría 0 es la background\n",
    "for i in range(len(labels)): categories[labels[i]] = i+1\n",
    "print('\\nTraining on: \\t' + str(categories) + '\\n')\n",
    "\n",
    "img_height = config['model']['input'] # Height of the model input images\n",
    "img_width = config['model']['input'] # Width of the model input images\n",
    "img_channels = 3 # Number of color channels of the model input images\n",
    "n_classes = len(labels) # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO\n",
    "classes = ['background'] + labels\n",
    "\n",
    "model_mode = 'training'\n",
    "# TODO: Set the path to the `.h5` file of the model to be loaded.\n",
    "model_path = config['train']['saved_weights_name']\n",
    "\n",
    "# We need to create an SSDLoss object in order to pass that to the model loader.\n",
    "ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
    "\n",
    "K.clear_session() # Clear previous models from memory.\n",
    "\n",
    "model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
    "                                               'L2Normalization': L2Normalization,\n",
    "                                               'DecodeDetections': DecodeDetections,\n",
    "                                               'compute_loss': ssd_loss.compute_loss})\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 246084 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 211096 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 5596 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 421264 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 392708 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 6112 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 397952 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 377084 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 6120 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 312084 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 302276 characters]\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 6716 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiempo Total: 0.602\n",
      "Tiempo promedio por imagen: 0.151\n",
      "OK\n"
     ]
    }
   ],
   "source": [
    "image_paths = []\n",
    "for inp in input_path:\n",
    "    if os.path.isdir(inp):\n",
    "        for inp_file in os.listdir(inp):\n",
    "            image_paths += [inp + inp_file]\n",
    "    else:\n",
    "        image_paths += [inp]\n",
    "\n",
    "image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]\n",
    "times = []\n",
    "\n",
    "\n",
    "\n",
    "for img_path in image_paths:\n",
    "    \n",
    "    boxes_area_score = {}\n",
    "    orig_images = [] # Store the images here.\n",
    "    input_images = [] # Store resized versions of the images here.\n",
    "    #print(img_path)\n",
    "\n",
    "    # preprocess image for network\n",
    "    orig_images.append(imread(img_path))\n",
    "    img = image.load_img(img_path, target_size=(img_height, img_width))\n",
    "    img = image.img_to_array(img)\n",
    "    input_images.append(img)\n",
    "    input_images = np.array(input_images)\n",
    "    # process image\n",
    "    start = time.time()\n",
    "    y_pred = model.predict(input_images)\n",
    "    y_pred_decoded = decode_detections(y_pred,\n",
    "                               confidence_thresh=score_threshold,\n",
    "                               iou_threshold=score_threshold_iou,\n",
    "                               top_k=200,\n",
    "                               normalize_coords=True,\n",
    "                               img_height=img_height,\n",
    "                               img_width=img_width)\n",
    "\n",
    "\n",
    "    #print(\"processing time: \", time.time() - start)\n",
    "    times.append(time.time() - start)\n",
    "    # correct for image scale\n",
    "\n",
    "    # visualize detections\n",
    "    # Set the colors for the bounding boxes\n",
    "    colors = plt.cm.brg(np.linspace(0, 1, 21)).tolist()\n",
    "\n",
    "    plt.figure(figsize=(20,12))\n",
    "    plt.imshow(orig_images[0],cmap = 'gray')\n",
    "\n",
    "    current_axis = plt.gca()\n",
    "    #print(y_pred)\n",
    "    for num,box in enumerate(y_pred_decoded[0]):\n",
    "        # Transform the predicted bounding boxes for the 300x300 image to the original image dimensions.\n",
    "\n",
    "        xmin = box[2] * orig_images[0].shape[1] / img_width\n",
    "        ymin = box[3] * orig_images[0].shape[0] / img_height\n",
    "        xmax = box[4] * orig_images[0].shape[1] / img_width\n",
    "        ymax = box[5] * orig_images[0].shape[0] / img_height\n",
    "        \n",
    "        color = colors[int(box[0])]\n",
    "        label = '{}: {:.2f}'.format(classes[int(box[0])], box[1])\n",
    "        current_axis.add_patch(plt.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, color=color, fill=False, linewidth=2))\n",
    "        current_axis.text(xmin, ymin, label, size='x-large', color='white', bbox={'facecolor':color, 'alpha':1.0})\n",
    "        \n",
    "        I = orig_images[0].copy()\n",
    "        score = np.sum(I[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / ((ymax-ymin)*(xmax-xmin))\n",
    "        \n",
    "        boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score}\n",
    "       \n",
    "    \n",
    "    ### Detect panel disconnect\n",
    "    mean = np.mean([boxes_area_score[i]['score'] for i in boxes_area_score])\n",
    "    sd  = np.std([boxes_area_score[i]['score'] for i in boxes_area_score])\n",
    "    normal = ([boxes_area_score[i]['score'] for i in boxes_area_score] - mean)/sd\n",
    "    \n",
    "    for key in boxes_area_score:\n",
    "        color = colors[2]\n",
    "        \n",
    "        #boxes_area_score[key]['score_norm'] = (boxes_area_score[key]['score'] - mean) / sd\n",
    "        z_score = (boxes_area_score[key]['score'] - mean) / sd      \n",
    "        ### Escribe el z-score\n",
    "        current_axis.text((boxes_area_score[key]['xmin'] + boxes_area_score[key]['xmax'])/2,\n",
    "                          (boxes_area_score[key]['ymin'] + boxes_area_score[key]['ymax'])/2,\n",
    "                           '%.2f' % z_score, size='x-large',\n",
    "                          color='white', bbox={'facecolor':color, 'alpha':1.0})\n",
    "        ### Escribe si es muy iluminado o no\n",
    "        #if (boxes_area_score[key]['score'] - mean) / sd < -2:\n",
    "         #   current_axis.text((boxes_area_score[key]['xmin'] + boxes_area_score[key]['xmax'])/2, (boxes_area_score[key]['ymin'] + boxes_area_score[key]['ymax'])/2, 'muy oscuro', size='x-large', color='white', bbox={'facecolor':color, 'alpha':1.0})\n",
    "        #if (boxes_area_score[key]['score'] - mean) / sd > 2:\n",
    "         #   current_axis.text((boxes_area_score[key]['xmin'] + boxes_area_score[key]['xmax'])/2, (boxes_area_score[key]['ymin'] + boxes_area_score[key]['ymax'])/2, 'muy iluminado', size='x-large', color='white', bbox={'facecolor':color, 'alpha':1.0})\n",
    "        \n",
    "        \n",
    "    #plt.figure(figsize=(15, 15))\n",
    "    #plt.axis('off')\n",
    "    save_path = output_path + img_path.split('/')[-1]\n",
    "    #plt.savefig(save_path)\n",
    "    #plt.close()\n",
    "    plt.figure(figsize=(20,12))\n",
    "    plt.imshow(orig_images[0],cmap = 'gray')\n",
    "    \n",
    "    plt.figure()\n",
    "    mean = np.mean([boxes_area_score[i]['score'] for i in boxes_area_score])\n",
    "    sd  = np.std([boxes_area_score[i]['score'] for i in boxes_area_score])\n",
    "    normal = ([boxes_area_score[i]['score'] for i in boxes_area_score] - mean)/sd\n",
    "    _ = plt.hist(normal, bins='auto')  # arguments are passed to np.histogram\n",
    "    plt.title(\"Histogram with 'auto' bins\")\n",
    "    plt.show()\n",
    "    \n",
    "file = open(output_path + 'time.txt','w')\n",
    "\n",
    "file.write('Tiempo promedio:' + str(np.mean(times)))\n",
    "\n",
    "file.close()\n",
    "print('Tiempo Total: {:.3f}'.format(np.sum(times)))\n",
    "print('Tiempo promedio por imagen: {:.3f}'.format(np.mean(times)))\n",
    "print('OK')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-0.28'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#boxes_area_score['0']\n",
    "\n",
    "#a = 1.233333\n",
    "#'%.2f' % a\n",
    "#for i in boxes_area_score:\n",
    " #   print(boxes_area_score[i]['score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 7928 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#boxes_area_score\n",
    "mean = np.mean([boxes_area_score[i]['score'] for i in boxes_area_score])\n",
    "sd  = np.std([boxes_area_score[i]['score'] for i in boxes_area_score])\n",
    "normal = ([boxes_area_score[i]['score'] for i in boxes_area_score] - mean)/sd\n",
    "_ = plt.hist(normal, bins='auto')  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram with 'auto' bins\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in boxes_area_score:\n",
    "    boxes_area_score[key]['score_norm'] = (boxes_area_score[key]['score'] - mean) / sd \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "panel : 69\n",
      "cell : 423\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Summary instance training\n",
    "category_train_list = []\n",
    "for image_label in train_dataset.labels:\n",
    "    category_train_list += [i[0] for i in train_dataset.labels[0]]\n",
    "summary_category_training = {train_dataset.classes[i]: category_train_list.count(i) for i in list(set(category_train_list))}\n",
    "for i in summary_category_training.keys():\n",
    "     print(i, ': {:.0f}'.format(summary_category_training[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 400, 400, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "identity_layer (Lambda)         (None, 400, 400, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 400, 400, 32) 2432        identity_layer[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn1 (BatchNormalization)        (None, 400, 400, 32) 128         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu1 (ELU)                      (None, 400, 400, 32) 0           bn1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "pool1 (MaxPooling2D)            (None, 200, 200, 32) 0           elu1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 200, 200, 48) 13872       pool1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn2 (BatchNormalization)        (None, 200, 200, 48) 192         conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu2 (ELU)                      (None, 200, 200, 48) 0           bn2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "pool2 (MaxPooling2D)            (None, 100, 100, 48) 0           elu2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 100, 100, 64) 27712       pool2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn3 (BatchNormalization)        (None, 100, 100, 64) 256         conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu3 (ELU)                      (None, 100, 100, 64) 0           bn3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "pool3 (MaxPooling2D)            (None, 50, 50, 64)   0           elu3[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv4 (Conv2D)                  (None, 50, 50, 64)   36928       pool3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn4 (BatchNormalization)        (None, 50, 50, 64)   256         conv4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu4 (ELU)                      (None, 50, 50, 64)   0           bn4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "pool4 (MaxPooling2D)            (None, 25, 25, 64)   0           elu4[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv5 (Conv2D)                  (None, 25, 25, 48)   27696       pool4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn5 (BatchNormalization)        (None, 25, 25, 48)   192         conv5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu5 (ELU)                      (None, 25, 25, 48)   0           bn5[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "pool5 (MaxPooling2D)            (None, 12, 12, 48)   0           elu5[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv6 (Conv2D)                  (None, 12, 12, 48)   20784       pool5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn6 (BatchNormalization)        (None, 12, 12, 48)   192         conv6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu6 (ELU)                      (None, 12, 12, 48)   0           bn6[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "pool6 (MaxPooling2D)            (None, 6, 6, 48)     0           elu6[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "conv7 (Conv2D)                  (None, 6, 6, 32)     13856       pool6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bn7 (BatchNormalization)        (None, 6, 6, 32)     128         conv7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "elu7 (ELU)                      (None, 6, 6, 32)     0           bn7[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "classes4 (Conv2D)               (None, 50, 50, 12)   6924        elu4[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "classes5 (Conv2D)               (None, 25, 25, 12)   5196        elu5[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "classes6 (Conv2D)               (None, 12, 12, 12)   5196        elu6[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "classes7 (Conv2D)               (None, 6, 6, 12)     3468        elu7[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "boxes4 (Conv2D)                 (None, 50, 50, 16)   9232        elu4[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "boxes5 (Conv2D)                 (None, 25, 25, 16)   6928        elu5[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "boxes6 (Conv2D)                 (None, 12, 12, 16)   6928        elu6[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "boxes7 (Conv2D)                 (None, 6, 6, 16)     4624        elu7[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "classes4_reshape (Reshape)      (None, 10000, 3)     0           classes4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classes5_reshape (Reshape)      (None, 2500, 3)      0           classes5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classes6_reshape (Reshape)      (None, 576, 3)       0           classes6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classes7_reshape (Reshape)      (None, 144, 3)       0           classes7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "anchors4 (AnchorBoxes)          (None, 50, 50, 4, 8) 0           boxes4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "anchors5 (AnchorBoxes)          (None, 25, 25, 4, 8) 0           boxes5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "anchors6 (AnchorBoxes)          (None, 12, 12, 4, 8) 0           boxes6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "anchors7 (AnchorBoxes)          (None, 6, 6, 4, 8)   0           boxes7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "classes_concat (Concatenate)    (None, 13220, 3)     0           classes4_reshape[0][0]           \n",
      "                                                                 classes5_reshape[0][0]           \n",
      "                                                                 classes6_reshape[0][0]           \n",
      "                                                                 classes7_reshape[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "boxes4_reshape (Reshape)        (None, 10000, 4)     0           boxes4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "boxes5_reshape (Reshape)        (None, 2500, 4)      0           boxes5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "boxes6_reshape (Reshape)        (None, 576, 4)       0           boxes6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "boxes7_reshape (Reshape)        (None, 144, 4)       0           boxes7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "anchors4_reshape (Reshape)      (None, 10000, 8)     0           anchors4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "anchors5_reshape (Reshape)      (None, 2500, 8)      0           anchors5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "anchors6_reshape (Reshape)      (None, 576, 8)       0           anchors6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "anchors7_reshape (Reshape)      (None, 144, 8)       0           anchors7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classes_softmax (Activation)    (None, 13220, 3)     0           classes_concat[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "boxes_concat (Concatenate)      (None, 13220, 4)     0           boxes4_reshape[0][0]             \n",
      "                                                                 boxes5_reshape[0][0]             \n",
      "                                                                 boxes6_reshape[0][0]             \n",
      "                                                                 boxes7_reshape[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "anchors_concat (Concatenate)    (None, 13220, 8)     0           anchors4_reshape[0][0]           \n",
      "                                                                 anchors5_reshape[0][0]           \n",
      "                                                                 anchors6_reshape[0][0]           \n",
      "                                                                 anchors7_reshape[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Concatenate)       (None, 13220, 15)    0           classes_softmax[0][0]            \n",
      "                                                                 boxes_concat[0][0]               \n",
      "                                                                 anchors_concat[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 193,120\n",
      "Trainable params: 192,448\n",
      "Non-trainable params: 672\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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