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Photovoltaic_Fault_Detector/.ipynb_checkpoints/Panel_Detector-checkpoint.ipynb

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:tada: initial commit
2020-01-16 10:51:32 -03:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Detector de Celulas"
]
},
{
"cell_type": "code",
"execution_count": 1,
"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": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training on: \t{'panel': 1}\n",
"\n",
"OK create model\n",
"\n",
"Loading pretrained weights VGG.\n",
"\n",
"__________________________________________________________________________________________________\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, 8) 4616 elu4[0][0] \n",
"__________________________________________________________________________________________________\n",
"classes5 (Conv2D) (None, 25, 25, 8) 3464 elu5[0][0] \n",
"__________________________________________________________________________________________________\n",
"classes6 (Conv2D) (None, 12, 12, 8) 3464 elu6[0][0] \n",
"__________________________________________________________________________________________________\n",
"classes7 (Conv2D) (None, 6, 6, 8) 2312 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, 2) 0 classes4[0][0] \n",
"__________________________________________________________________________________________________\n",
"classes5_reshape (Reshape) (None, 2500, 2) 0 classes5[0][0] \n",
"__________________________________________________________________________________________________\n",
"classes6_reshape (Reshape) (None, 576, 2) 0 classes6[0][0] \n",
"__________________________________________________________________________________________________\n",
"classes7_reshape (Reshape) (None, 144, 2) 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, 2) 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, 2) 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, 14) 0 classes_softmax[0][0] \n",
" boxes_concat[0][0] \n",
" anchors_concat[0][0] \n",
"==================================================================================================\n",
"Total params: 186,192\n",
"Trainable params: 185,520\n",
"Non-trainable params: 672\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",
"makedirs(path_anns)\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": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Processing image set 'train.txt': 0%| | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
"\n",
"Processing image set 'train.txt': 100%|██████████| 1/1 [00:00<00:00, 18.17it/s]\u001b[A\u001b[A\n",
"\n",
"Processing image set 'test.txt': 0%| | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
"\n",
"Processing image set 'test.txt': 100%|██████████| 1/1 [00:00<00:00, 18.46it/s]\u001b[A\u001b[Apanel : 66\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 [==============================] - 32s 639ms/step - loss: 9.5767 - val_loss: 14.7488\n",
"\n",
"Epoch 00001: val_loss improved from inf to 14.74878, saving model to experimento_ssd7_panel.h5\n",
"Epoch 2/100\n",
"\n",
"Epoch 00002: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 36s 722ms/step - loss: 6.3343 - val_loss: 13.6317\n",
"\n",
"Epoch 00002: val_loss improved from 14.74878 to 13.63168, saving model to experimento_ssd7_panel.h5\n",
"Epoch 3/100\n",
"\n",
"Epoch 00003: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 36s 721ms/step - loss: 5.6142 - val_loss: 8.7417\n",
"\n",
"Epoch 00003: val_loss improved from 13.63168 to 8.74172, saving model to experimento_ssd7_panel.h5\n",
"Epoch 4/100\n",
"\n",
"Epoch 00004: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 33s 661ms/step - loss: 5.0502 - val_loss: 8.8821\n",
"\n",
"Epoch 00004: val_loss did not improve from 8.74172\n",
"Epoch 5/100\n",
"\n",
"Epoch 00005: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 34s 671ms/step - loss: 4.9250 - val_loss: 10.0443\n",
"\n",
"Epoch 00005: val_loss did not improve from 8.74172\n",
"Epoch 6/100\n",
"\n",
"Epoch 00006: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 31s 613ms/step - loss: 4.6507 - val_loss: 6.0406\n",
"\n",
"Epoch 00006: val_loss improved from 8.74172 to 6.04056, saving model to experimento_ssd7_panel.h5\n",
"Epoch 7/100\n",
"\n",
"Epoch 00007: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 609ms/step - loss: 3.9971 - val_loss: 5.6637\n",
"\n",
"Epoch 00007: val_loss improved from 6.04056 to 5.66370, saving model to experimento_ssd7_panel.h5\n",
"Epoch 8/100\n",
"\n",
"Epoch 00008: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 608ms/step - loss: 4.0542 - val_loss: 3.1067\n",
"\n",
"Epoch 00008: val_loss improved from 5.66370 to 3.10669, saving model to experimento_ssd7_panel.h5\n",
"Epoch 9/100\n",
"\n",
"Epoch 00009: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 606ms/step - loss: 3.7937 - val_loss: 4.1299\n",
"\n",
"Epoch 00009: val_loss did not improve from 3.10669\n",
"Epoch 10/100\n",
"\n",
"Epoch 00010: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 602ms/step - loss: 3.5755 - val_loss: 3.2800\n",
"\n",
"Epoch 00010: val_loss did not improve from 3.10669\n",
"Epoch 11/100\n",
"\n",
"Epoch 00011: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 604ms/step - loss: 4.1572 - val_loss: 3.9973\n",
"\n",
"Epoch 00011: val_loss did not improve from 3.10669\n",
"Epoch 12/100\n",
"\n",
"Epoch 00012: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 596ms/step - loss: 4.0369 - val_loss: 3.4753\n",
"\n",
"Epoch 00012: val_loss did not improve from 3.10669\n",
"Epoch 13/100\n",
"\n",
"Epoch 00013: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 605ms/step - loss: 3.7626 - val_loss: 4.3096\n",
"\n",
"Epoch 00013: val_loss did not improve from 3.10669\n",
"Epoch 14/100\n",
"\n",
"Epoch 00014: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 610ms/step - loss: 3.4655 - val_loss: 4.3969\n",
"\n",
"Epoch 00014: val_loss did not improve from 3.10669\n",
"Epoch 15/100\n",
"\n",
"Epoch 00015: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 36s 724ms/step - loss: 3.6985 - val_loss: 8.0128\n",
"\n",
"Epoch 00015: val_loss did not improve from 3.10669\n",
"Epoch 16/100\n",
"\n",
"Epoch 00016: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 31s 613ms/step - loss: 3.4447 - val_loss: 3.4261\n",
"\n",
"Epoch 00016: val_loss did not improve from 3.10669\n",
"Epoch 17/100\n",
"\n",
"Epoch 00017: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 35s 704ms/step - loss: 3.3104 - val_loss: 3.4646\n",
"\n",
"Epoch 00017: val_loss did not improve from 3.10669\n",
"Epoch 18/100\n",
"\n",
"Epoch 00018: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 34s 686ms/step - loss: 3.1926 - val_loss: 5.6268\n",
"\n",
"Epoch 00018: val_loss did not improve from 3.10669\n",
"Epoch 19/100\n",
"\n",
"Epoch 00019: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 34s 682ms/step - loss: 2.7941 - val_loss: 3.3249\n",
"\n",
"Epoch 00019: val_loss did not improve from 3.10669\n",
"Epoch 20/100\n",
"\n",
"Epoch 00020: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 31s 614ms/step - loss: 3.5081 - val_loss: 9.3332\n",
"\n",
"Epoch 00020: val_loss did not improve from 3.10669\n",
"Epoch 21/100\n",
"\n",
"Epoch 00021: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 599ms/step - loss: 2.8098 - val_loss: 2.2246\n",
"\n",
"Epoch 00021: val_loss improved from 3.10669 to 2.22460, saving model to experimento_ssd7_panel.h5\n",
"Epoch 22/100\n",
"\n",
"Epoch 00022: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 31s 611ms/step - loss: 2.9235 - val_loss: 2.5262\n",
"\n",
"Epoch 00022: val_loss did not improve from 2.22460\n",
"Epoch 23/100\n",
"\n",
"Epoch 00023: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 595ms/step - loss: 2.9251 - val_loss: 4.8879\n",
"\n",
"Epoch 00023: val_loss did not improve from 2.22460\n",
"Epoch 24/100\n",
"\n",
"Epoch 00024: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 36s 729ms/step - loss: 2.7105 - val_loss: 1.8794\n",
"\n",
"Epoch 00024: val_loss improved from 2.22460 to 1.87940, saving model to experimento_ssd7_panel.h5\n",
"Epoch 25/100\n",
"\n",
"Epoch 00025: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 52s 1s/step - loss: 2.8781 - val_loss: 3.6699\n",
"\n",
"Epoch 00025: val_loss did not improve from 1.87940\n",
"Epoch 26/100\n",
"\n",
"Epoch 00026: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 35s 693ms/step - loss: 2.7714 - val_loss: 3.5053\n",
"\n",
"Epoch 00026: val_loss did not improve from 1.87940\n",
"Epoch 27/100\n",
"\n",
"Epoch 00027: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 39s 783ms/step - loss: 3.1689 - val_loss: 2.1802\n",
"\n",
"Epoch 00027: val_loss did not improve from 1.87940\n",
"Epoch 28/100\n",
"\n",
"Epoch 00028: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 32s 637ms/step - loss: 2.9556 - val_loss: 6.3200\n",
"\n",
"Epoch 00028: val_loss did not improve from 1.87940\n",
"Epoch 29/100\n",
"\n",
"Epoch 00029: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 583ms/step - loss: 3.1600 - val_loss: 1.7858\n",
"\n",
"Epoch 00029: val_loss improved from 1.87940 to 1.78582, saving model to experimento_ssd7_panel.h5\n",
"Epoch 30/100\n",
"\n",
"Epoch 00030: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 591ms/step - loss: 2.6865 - val_loss: 3.3138\n",
"\n",
"Epoch 00030: val_loss did not improve from 1.78582\n",
"Epoch 31/100\n",
"\n",
"Epoch 00031: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 583ms/step - loss: 3.1641 - val_loss: 3.0732\n",
"\n",
"Epoch 00031: val_loss did not improve from 1.78582\n",
"Epoch 32/100\n",
"\n",
"Epoch 00032: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 585ms/step - loss: 2.6921 - val_loss: 6.5412\n",
"\n",
"Epoch 00032: val_loss did not improve from 1.78582\n",
"Epoch 33/100\n",
"\n",
"Epoch 00033: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 593ms/step - loss: 2.3164 - val_loss: 2.8038\n",
"\n",
"Epoch 00033: val_loss did not improve from 1.78582\n",
"Epoch 34/100\n",
"\n",
"Epoch 00034: LearningRateScheduler setting learning rate to 0.001.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"50/50 [==============================] - 30s 595ms/step - loss: 2.2648 - val_loss: 2.2688\n",
"\n",
"Epoch 00034: val_loss did not improve from 1.78582\n",
"Epoch 35/100\n",
"\n",
"Epoch 00035: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 585ms/step - loss: 3.1685 - val_loss: 4.1838\n",
"\n",
"Epoch 00035: val_loss did not improve from 1.78582\n",
"Epoch 36/100\n",
"\n",
"Epoch 00036: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 590ms/step - loss: 2.8008 - val_loss: 3.2680\n",
"\n",
"Epoch 00036: val_loss did not improve from 1.78582\n",
"Epoch 37/100\n",
"\n",
"Epoch 00037: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 590ms/step - loss: 2.5555 - val_loss: 2.4116\n",
"\n",
"Epoch 00037: val_loss did not improve from 1.78582\n",
"Epoch 38/100\n",
"\n",
"Epoch 00038: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 2.1759 - val_loss: 2.2294\n",
"\n",
"Epoch 00038: val_loss did not improve from 1.78582\n",
"Epoch 39/100\n",
"\n",
"Epoch 00039: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 575ms/step - loss: 2.4307 - val_loss: 2.0943\n",
"\n",
"Epoch 00039: val_loss did not improve from 1.78582\n",
"Epoch 40/100\n",
"\n",
"Epoch 00040: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 577ms/step - loss: 2.1298 - val_loss: 1.6592\n",
"\n",
"Epoch 00040: val_loss improved from 1.78582 to 1.65918, saving model to experimento_ssd7_panel.h5\n",
"Epoch 41/100\n",
"\n",
"Epoch 00041: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 576ms/step - loss: 2.6209 - val_loss: 1.5406\n",
"\n",
"Epoch 00041: val_loss improved from 1.65918 to 1.54055, saving model to experimento_ssd7_panel.h5\n",
"Epoch 42/100\n",
"\n",
"Epoch 00042: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 574ms/step - loss: 2.1339 - val_loss: 1.5206\n",
"\n",
"Epoch 00042: val_loss improved from 1.54055 to 1.52056, saving model to experimento_ssd7_panel.h5\n",
"Epoch 43/100\n",
"\n",
"Epoch 00043: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 586ms/step - loss: 2.6588 - val_loss: 4.4331\n",
"\n",
"Epoch 00043: val_loss did not improve from 1.52056\n",
"Epoch 44/100\n",
"\n",
"Epoch 00044: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 589ms/step - loss: 2.5057 - val_loss: 1.3420\n",
"\n",
"Epoch 00044: val_loss improved from 1.52056 to 1.34204, saving model to experimento_ssd7_panel.h5\n",
"Epoch 45/100\n",
"\n",
"Epoch 00045: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 585ms/step - loss: 2.4487 - val_loss: 1.7022\n",
"\n",
"Epoch 00045: val_loss did not improve from 1.34204\n",
"Epoch 46/100\n",
"\n",
"Epoch 00046: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 588ms/step - loss: 3.2258 - val_loss: 2.5862\n",
"\n",
"Epoch 00046: val_loss did not improve from 1.34204\n",
"Epoch 47/100\n",
"\n",
"Epoch 00047: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 587ms/step - loss: 2.6757 - val_loss: 2.9214\n",
"\n",
"Epoch 00047: val_loss did not improve from 1.34204\n",
"Epoch 48/100\n",
"\n",
"Epoch 00048: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 578ms/step - loss: 2.5212 - val_loss: 2.6395\n",
"\n",
"Epoch 00048: val_loss did not improve from 1.34204\n",
"Epoch 49/100\n",
"\n",
"Epoch 00049: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 576ms/step - loss: 2.3494 - val_loss: 2.3526\n",
"\n",
"Epoch 00049: val_loss did not improve from 1.34204\n",
"Epoch 50/100\n",
"\n",
"Epoch 00050: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 575ms/step - loss: 2.3571 - val_loss: 3.9680\n",
"\n",
"Epoch 00050: val_loss did not improve from 1.34204\n",
"Epoch 51/100\n",
"\n",
"Epoch 00051: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 584ms/step - loss: 2.6289 - val_loss: 1.7524\n",
"\n",
"Epoch 00051: val_loss did not improve from 1.34204\n",
"Epoch 52/100\n",
"\n",
"Epoch 00052: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 586ms/step - loss: 2.1134 - val_loss: 1.5759\n",
"\n",
"Epoch 00052: val_loss did not improve from 1.34204\n",
"Epoch 53/100\n",
"\n",
"Epoch 00053: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 586ms/step - loss: 2.2294 - val_loss: 5.2589\n",
"\n",
"Epoch 00053: val_loss did not improve from 1.34204\n",
"Epoch 54/100\n",
"\n",
"Epoch 00054: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 583ms/step - loss: 2.3119 - val_loss: 2.6193\n",
"\n",
"Epoch 00054: val_loss did not improve from 1.34204\n",
"Epoch 55/100\n",
"\n",
"Epoch 00055: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 588ms/step - loss: 2.5177 - val_loss: 2.9167\n",
"\n",
"Epoch 00055: val_loss did not improve from 1.34204\n",
"Epoch 56/100\n",
"\n",
"Epoch 00056: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 2.2165 - val_loss: 2.2745\n",
"\n",
"Epoch 00056: val_loss did not improve from 1.34204\n",
"Epoch 57/100\n",
"\n",
"Epoch 00057: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 578ms/step - loss: 1.9686 - val_loss: 1.4099\n",
"\n",
"Epoch 00057: val_loss did not improve from 1.34204\n",
"Epoch 58/100\n",
"\n",
"Epoch 00058: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 2.4100 - val_loss: 6.2453\n",
"\n",
"Epoch 00058: val_loss did not improve from 1.34204\n",
"Epoch 59/100\n",
"\n",
"Epoch 00059: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 587ms/step - loss: 2.3643 - val_loss: 2.6864\n",
"\n",
"Epoch 00059: val_loss did not improve from 1.34204\n",
"Epoch 60/100\n",
"\n",
"Epoch 00060: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 584ms/step - loss: 1.9538 - val_loss: 2.4815\n",
"\n",
"Epoch 00060: val_loss did not improve from 1.34204\n",
"Epoch 61/100\n",
"\n",
"Epoch 00061: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 572ms/step - loss: 2.2638 - val_loss: 1.3604\n",
"\n",
"Epoch 00061: val_loss did not improve from 1.34204\n",
"Epoch 62/100\n",
"\n",
"Epoch 00062: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 581ms/step - loss: 1.9277 - val_loss: 1.4886\n",
"\n",
"Epoch 00062: val_loss did not improve from 1.34204\n",
"Epoch 63/100\n",
"\n",
"Epoch 00063: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 583ms/step - loss: 2.2858 - val_loss: 11.1304\n",
"\n",
"Epoch 00063: val_loss did not improve from 1.34204\n",
"Epoch 64/100\n",
"\n",
"Epoch 00064: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 581ms/step - loss: 2.8886 - val_loss: 5.2787\n",
"\n",
"Epoch 00064: val_loss did not improve from 1.34204\n",
"Epoch 65/100\n",
"\n",
"Epoch 00065: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 598ms/step - loss: 2.4776 - val_loss: 3.4754\n",
"\n",
"Epoch 00065: val_loss did not improve from 1.34204\n",
"Epoch 66/100\n",
"\n",
"Epoch 00066: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 584ms/step - loss: 2.2199 - val_loss: 1.6823\n",
"\n",
"Epoch 00066: val_loss did not improve from 1.34204\n",
"Epoch 67/100\n",
"\n",
"Epoch 00067: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 2.4033 - val_loss: 1.7285\n",
"\n",
"Epoch 00067: val_loss did not improve from 1.34204\n",
"Epoch 68/100\n",
"\n",
"Epoch 00068: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 577ms/step - loss: 2.5946 - val_loss: 1.3581\n",
"\n",
"Epoch 00068: val_loss did not improve from 1.34204\n",
"Epoch 69/100\n",
"\n",
"Epoch 00069: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 587ms/step - loss: 2.2140 - val_loss: 1.8901\n",
"\n",
"Epoch 00069: val_loss did not improve from 1.34204\n",
"Epoch 70/100\n",
"\n",
"Epoch 00070: LearningRateScheduler setting learning rate to 0.001.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"50/50 [==============================] - 29s 583ms/step - loss: 1.6617 - val_loss: 1.5495\n",
"\n",
"Epoch 00070: val_loss did not improve from 1.34204\n",
"Epoch 71/100\n",
"\n",
"Epoch 00071: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 574ms/step - loss: 2.0889 - val_loss: 2.9760\n",
"\n",
"Epoch 00071: val_loss did not improve from 1.34204\n",
"Epoch 72/100\n",
"\n",
"Epoch 00072: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 588ms/step - loss: 2.1289 - val_loss: 3.2035\n",
"\n",
"Epoch 00072: val_loss did not improve from 1.34204\n",
"Epoch 73/100\n",
"\n",
"Epoch 00073: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 571ms/step - loss: 2.4053 - val_loss: 3.2075\n",
"\n",
"Epoch 00073: val_loss did not improve from 1.34204\n",
"Epoch 74/100\n",
"\n",
"Epoch 00074: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 584ms/step - loss: 2.4194 - val_loss: 4.1101\n",
"\n",
"Epoch 00074: val_loss did not improve from 1.34204\n",
"Epoch 75/100\n",
"\n",
"Epoch 00075: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 583ms/step - loss: 2.3311 - val_loss: 4.0916\n",
"\n",
"Epoch 00075: val_loss did not improve from 1.34204\n",
"Epoch 76/100\n",
"\n",
"Epoch 00076: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 586ms/step - loss: 2.1583 - val_loss: 10.7875\n",
"\n",
"Epoch 00076: val_loss did not improve from 1.34204\n",
"Epoch 77/100\n",
"\n",
"Epoch 00077: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 572ms/step - loss: 2.3412 - val_loss: 3.5870\n",
"\n",
"Epoch 00077: val_loss did not improve from 1.34204\n",
"Epoch 78/100\n",
"\n",
"Epoch 00078: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 2.1332 - val_loss: 1.3347\n",
"\n",
"Epoch 00078: val_loss improved from 1.34204 to 1.33472, saving model to experimento_ssd7_panel.h5\n",
"Epoch 79/100\n",
"\n",
"Epoch 00079: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 30s 590ms/step - loss: 2.1183 - val_loss: 1.6549\n",
"\n",
"Epoch 00079: val_loss did not improve from 1.33472\n",
"Epoch 80/100\n",
"\n",
"Epoch 00080: LearningRateScheduler setting learning rate to 0.001.\n",
"50/50 [==============================] - 29s 572ms/step - loss: 2.0257 - val_loss: 2.5943\n",
"\n",
"Epoch 00080: val_loss did not improve from 1.33472\n",
"Epoch 81/100\n",
"\n",
"Epoch 00081: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 588ms/step - loss: 2.1342 - val_loss: 1.5209\n",
"\n",
"Epoch 00081: val_loss did not improve from 1.33472\n",
"Epoch 82/100\n",
"\n",
"Epoch 00082: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 581ms/step - loss: 1.8549 - val_loss: 1.5491\n",
"\n",
"Epoch 00082: val_loss did not improve from 1.33472\n",
"Epoch 83/100\n",
"\n",
"Epoch 00083: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 1.6819 - val_loss: 1.3131\n",
"\n",
"Epoch 00083: val_loss improved from 1.33472 to 1.31315, saving model to experimento_ssd7_panel.h5\n",
"Epoch 84/100\n",
"\n",
"Epoch 00084: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 581ms/step - loss: 1.6949 - val_loss: 1.3913\n",
"\n",
"Epoch 00084: val_loss did not improve from 1.31315\n",
"Epoch 85/100\n",
"\n",
"Epoch 00085: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 577ms/step - loss: 1.6462 - val_loss: 1.1784\n",
"\n",
"Epoch 00085: val_loss improved from 1.31315 to 1.17837, saving model to experimento_ssd7_panel.h5\n",
"Epoch 86/100\n",
"\n",
"Epoch 00086: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 30s 591ms/step - loss: 2.0633 - val_loss: 1.4053\n",
"\n",
"Epoch 00086: val_loss did not improve from 1.17837\n",
"Epoch 87/100\n",
"\n",
"Epoch 00087: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 581ms/step - loss: 2.2056 - val_loss: 1.3353\n",
"\n",
"Epoch 00087: val_loss did not improve from 1.17837\n",
"Epoch 88/100\n",
"\n",
"Epoch 00088: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 587ms/step - loss: 1.5451 - val_loss: 1.3712\n",
"\n",
"Epoch 00088: val_loss did not improve from 1.17837\n",
"Epoch 89/100\n",
"\n",
"Epoch 00089: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 587ms/step - loss: 1.8745 - val_loss: 1.1776\n",
"\n",
"Epoch 00089: val_loss improved from 1.17837 to 1.17762, saving model to experimento_ssd7_panel.h5\n",
"Epoch 90/100\n",
"\n",
"Epoch 00090: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 579ms/step - loss: 1.4659 - val_loss: 1.3136\n",
"\n",
"Epoch 00090: val_loss did not improve from 1.17762\n",
"Epoch 91/100\n",
"\n",
"Epoch 00091: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 582ms/step - loss: 1.7581 - val_loss: 1.4674\n",
"\n",
"Epoch 00091: val_loss did not improve from 1.17762\n",
"Epoch 92/100\n",
"\n",
"Epoch 00092: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 584ms/step - loss: 1.8052 - val_loss: 1.3722\n",
"\n",
"Epoch 00092: val_loss did not improve from 1.17762\n",
"Epoch 93/100\n",
"\n",
"Epoch 00093: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 587ms/step - loss: 1.6640 - val_loss: 1.4513\n",
"\n",
"Epoch 00093: val_loss did not improve from 1.17762\n",
"Epoch 94/100\n",
"\n",
"Epoch 00094: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 577ms/step - loss: 1.9951 - val_loss: 1.2249\n",
"\n",
"Epoch 00094: val_loss did not improve from 1.17762\n",
"Epoch 95/100\n",
"\n",
"Epoch 00095: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 585ms/step - loss: 1.6637 - val_loss: 1.3945\n",
"\n",
"Epoch 00095: val_loss did not improve from 1.17762\n",
"Epoch 96/100\n",
"\n",
"Epoch 00096: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 576ms/step - loss: 1.3763 - val_loss: 1.4558\n",
"\n",
"Epoch 00096: val_loss did not improve from 1.17762\n",
"Epoch 97/100\n",
"\n",
"Epoch 00097: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 580ms/step - loss: 2.1172 - val_loss: 1.4779\n",
"\n",
"Epoch 00097: val_loss did not improve from 1.17762\n",
"Epoch 98/100\n",
"\n",
"Epoch 00098: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 573ms/step - loss: 1.8102 - val_loss: 1.5256\n",
"\n",
"Epoch 00098: val_loss did not improve from 1.17762\n",
"Epoch 99/100\n",
"\n",
"Epoch 00099: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 577ms/step - loss: 1.7073 - val_loss: 1.4527\n",
"\n",
"Epoch 00099: val_loss did not improve from 1.17762\n",
"Epoch 100/100\n",
"\n",
"Epoch 00100: LearningRateScheduler setting learning rate to 0.0001.\n",
"50/50 [==============================] - 29s 571ms/step - loss: 1.5982 - val_loss: 1.5814\n",
"\n",
"Epoch 00100: val_loss did not improve from 1.17762\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",
" 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",
" 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": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['val_loss', 'loss', 'lr'])\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Graficar aprendizaje\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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluación del Modelo"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of images in the evaluation dataset: 1\n",
"\n",
"\n",
"\n",
" 0%| | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
"\n",
"Producing predictions batch-wise: 0%| | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
"\n",
"Producing predictions batch-wise: 100%|██████████| 1/1 [00:00<00:00, 1.37it/s]\u001b[A\u001b[A\n",
"\n",
" 0%| | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
"\n",
"Matching predictions to ground truth, class 1/1.: 0%| | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
"\n",
"Matching predictions to ground truth, class 1/1.: 100%|██████████| 200/200 [00:00<00:00, 4758.36it/s]\u001b[A\u001b[AComputing precisions and recalls, class 1/1\n",
"Computing average precision, class 1/1\n",
"200 instances of class panel with average precision: 0.9048\n",
"mAP using the weighted average of precisions among classes: 0.9048\n",
"mAP: 0.9048\n",
"panel AP 0.905\n",
"\n",
" mAP 0.905\n"
]
}
],
"source": [
"\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": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training on: \t{'panel': 1}\n",
"\n",
"Tiempo Total: 1.293\n",
"Tiempo promedio por imagen: 0.259\n",
"OK\n"
]
}
],
"source": [
"from imageio import imread\n",
"from keras.preprocessing import image\n",
"import time\n",
"\n",
"config_path = 'config_7_panel.json'\n",
"input_path = 'panel/Mision_2/'\n",
"output_path = 'result_ssd7_panel_2/'\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 = 0.3\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",
"image_paths = []\n",
"\n",
"if os.path.isdir(input_path):\n",
" for inp_file in os.listdir(input_path):\n",
" image_paths += [input_path + inp_file]\n",
"else:\n",
" image_paths += [input_path]\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",
"for img_path in image_paths:\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,\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 box in 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",
" #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",
" \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')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing image set 'training_small.txt': 100%|██████████| 45/45 [00:01<00:00, 27.96it/s]\n",
"trophozoite : 135\n",
"red blood cell : 3195\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": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"45"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"\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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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