Perro o Gato con Tensorflow

Introduction

Train:

La carpeta del entrenamiento contiene 25,000 imágenes de perros y gatos. Cada imagen en esta carpeta tiene la etiqueta como parte del nombre de archivo.

Test:

La carpeta de prueba contiene 12,500 imágenes, nombradas de acuerdo con una identificación numérica. Para cada imagen en el conjunto de prueba, debe predecir la probabilidad de que la imagen sea un perro (1 = perro, 0 = gato).

Cargando librerías

In [1]:
import os, random
import cv2

import numpy as np
import pandas as pd

import seaborn as sns
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

from tqdm import tqdm
from random import shuffle 
from IPython.display import SVG

from keras.preprocessing.image import ImageDataGenerator

from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from keras import layers, models, optimizers
from tensorflow.python.keras.applications import ResNet50
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D

%matplotlib inline 
Using TensorFlow backend.

Parámetros

In [2]:
TEST_SIZE = 0.3 
RANDOM_STATE = 2018
BATCH_SIZE = 64 #16
NO_EPOCHS = 10
NUM_CLASSES = 2
numero_entrenamiento = 0
numero_validacion = 0
PATH = 'C:/Users/Jesus/Desktop/mineria/'
TRAIN_FOLDER = PATH+'train/'
TEST_FOLDER =  PATH+'test/'
IMG_SIZE = 224

img_width = 150
img_height = 150

Test y Train

In [3]:
train_images_dogs_cats = [TRAIN_FOLDER+i for i in os.listdir(TRAIN_FOLDER)] # use this for full dataset
test_images_dogs_cats = [TEST_FOLDER+i for i in os.listdir(TEST_FOLDER)]
In [4]:
train_images_dogs_cats
Out[4]:
['C:/Users/Jesus/Desktop/mineria/train/cat.0.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.1.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.100.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.1000.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10000.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10001.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10002.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10003.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10004.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10005.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10006.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10007.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10008.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10009.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.1001.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10010.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10011.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10012.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10013.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10014.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10015.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10016.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10017.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10018.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10019.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.1002.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10020.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10021.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10022.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10023.jpg',
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 'C:/Users/Jesus/Desktop/mineria/train/cat.10028.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10029.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.1003.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10030.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10031.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10032.jpg',
 'C:/Users/Jesus/Desktop/mineria/train/cat.10033.jpg',
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Image to Vector

Ahora las imágenes tienen que estar representadas en números.

Para esto, usando la biblioteca openCV lee y redimensiona la imagen.

Generar etiquetas para el conjunto de aprendizaje supervisado.

A continuación se muestra la función de ayuda para hacerlo.

In [5]:
def preparar_data(lista_imagenes):
    
    x = [] # array de imagenes redimensionadas
    y = [] # array de labels
    
    for image in lista_imagenes:
        x.append(cv2.resize(cv2.imread(image), (img_width,img_height), interpolation=cv2.INTER_CUBIC))
    
    for i in lista_imagenes:
        if 'dog' in i:
            y.append(1)
        elif 'cat' in i:
            y.append(0)
        #else:
            #print('no esta presente un archivo de perros y gatos')
            
    return x, y

Preparación de datos

In [6]:
X, Y = preparar_data(train_images_dogs_cats)

X_train, X_evaluation, Y_train, Y_evaluation = train_test_split(X,Y, test_size=0.3, random_state=1)
In [7]:
numero_entrenamiento = len(X_train)
numero_validacion = len(X_evaluation)
print("entrenamiento ", numero_entrenamiento)
print("validación ", numero_validacion)
entrenamiento  17500
validación  7500

Creación del modelo

In [8]:
model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), input_shape=(img_width, img_height, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))

model.add(layers.Conv2D(32, (3, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))

model.add(layers.Conv2D(64, (3, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))

model.add(layers.Flatten())
model.add(layers.Dense(64))
model.add(layers.Activation('relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1))
model.add(layers.Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       
_________________________________________________________________
activation_1 (Activation)    (None, 148, 148, 32)      0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 72, 72, 32)        9248      
_________________________________________________________________
activation_2 (Activation)    (None, 72, 72, 32)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 34, 34, 64)        18496     
_________________________________________________________________
activation_3 (Activation)    (None, 34, 34, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 18496)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                1183808   
_________________________________________________________________
activation_4 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_5 (Activation)    (None, 1)                 0         
=================================================================
Total params: 1,212,513
Trainable params: 1,212,513
Non-trainable params: 0
_________________________________________________________________

Control del entrenamiento y la validación

In [9]:
entrenamiento_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

validacion_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

Preprocesamiento del entrenamiento

In [15]:
train_generator = entrenamiento_datagen.flow(np.array(X_train), Y_train, batch_size=BATCH_SIZE)
validation_generator = validacion_datagen.flow(np.array(X_evaluation), Y_evaluation, batch_size=BATCH_SIZE)

Entrenamiento

In [19]:
history = model.fit_generator(
    train_generator, 
    steps_per_epoch=numero_entrenamiento // BATCH_SIZE,
    epochs=2,
    validation_data=validation_generator,
    validation_steps=numero_validacion // BATCH_SIZE
)
Epoch 1/2
204/273 [=====================>........] - ETA: 18:04 - loss: 0.5259 - acc: 0.70 - ETA: 15:43 - loss: 0.5255 - acc: 0.69 - ETA: 15:34 - loss: 0.5473 - acc: 0.71 - ETA: 15:37 - loss: 0.6208 - acc: 0.66 - ETA: 15:01 - loss: 0.6087 - acc: 0.67 - ETA: 14:34 - loss: 0.6262 - acc: 0.65 - ETA: 14:16 - loss: 0.6250 - acc: 0.66 - ETA: 14:01 - loss: 0.6317 - acc: 0.64 - ETA: 13:52 - loss: 0.6374 - acc: 0.63 - ETA: 13:44 - loss: 0.6455 - acc: 0.63 - ETA: 13:30 - loss: 0.6474 - acc: 0.62 - ETA: 13:11 - loss: 0.6380 - acc: 0.64 - ETA: 13:01 - loss: 0.6404 - acc: 0.64 - ETA: 12:52 - loss: 0.6390 - acc: 0.64 - ETA: 12:39 - loss: 0.6384 - acc: 0.63 - ETA: 12:35 - loss: 0.6354 - acc: 0.63 - ETA: 12:27 - loss: 0.6345 - acc: 0.64 - ETA: 12:21 - loss: 0.6313 - acc: 0.65 - ETA: 12:11 - loss: 0.6369 - acc: 0.65 - ETA: 12:06 - loss: 0.6343 - acc: 0.65 - ETA: 12:01 - loss: 0.6302 - acc: 0.65 - ETA: 11:57 - loss: 0.6257 - acc: 0.65 - ETA: 11:51 - loss: 0.6266 - acc: 0.65 - ETA: 11:49 - loss: 0.6245 - acc: 0.65 - ETA: 11:45 - loss: 0.6226 - acc: 0.65 - ETA: 11:40 - loss: 0.6247 - acc: 0.65 - ETA: 11:39 - loss: 0.6237 - acc: 0.65 - ETA: 11:37 - loss: 0.6239 - acc: 0.65 - ETA: 11:31 - loss: 0.6268 - acc: 0.65 - ETA: 11:28 - loss: 0.6251 - acc: 0.65 - ETA: 11:24 - loss: 0.6233 - acc: 0.65 - ETA: 11:20 - loss: 0.6248 - acc: 0.65 - ETA: 11:16 - loss: 0.6256 - acc: 0.65 - ETA: 11:13 - loss: 0.6264 - acc: 0.65 - ETA: 11:09 - loss: 0.6257 - acc: 0.65 - ETA: 11:05 - loss: 0.6262 - acc: 0.65 - ETA: 11:00 - loss: 0.6240 - acc: 0.65 - ETA: 10:56 - loss: 0.6246 - acc: 0.65 - ETA: 10:53 - loss: 0.6226 - acc: 0.65 - ETA: 10:49 - loss: 0.6243 - acc: 0.65 - ETA: 10:45 - loss: 0.6305 - acc: 0.65 - ETA: 10:42 - loss: 0.6296 - acc: 0.65 - ETA: 10:38 - loss: 0.6297 - acc: 0.65 - ETA: 10:35 - loss: 0.6278 - acc: 0.65 - ETA: 10:32 - loss: 0.6261 - acc: 0.66 - ETA: 10:29 - loss: 0.6278 - acc: 0.66 - ETA: 10:25 - loss: 0.6278 - acc: 0.66 - ETA: 10:21 - loss: 0.6283 - acc: 0.66 - ETA: 10:18 - loss: 0.6280 - acc: 0.66 - ETA: 10:16 - loss: 0.6282 - acc: 0.66 - ETA: 10:14 - loss: 0.6265 - acc: 0.66 - ETA: 10:12 - loss: 0.6270 - acc: 0.66 - ETA: 10:08 - loss: 0.6247 - acc: 0.66 - ETA: 10:05 - loss: 0.6246 - acc: 0.66 - ETA: 10:01 - loss: 0.6231 - acc: 0.66 - ETA: 9:57 - loss: 0.6251 - acc: 0.6657 - ETA: 9:55 - loss: 0.6261 - acc: 0.664 - ETA: 9:52 - loss: 0.6285 - acc: 0.663 - ETA: 9:49 - loss: 0.6292 - acc: 0.663 - ETA: 9:45 - loss: 0.6276 - acc: 0.664 - ETA: 9:42 - loss: 0.6283 - acc: 0.663 - ETA: 9:39 - loss: 0.6278 - acc: 0.663 - ETA: 9:37 - loss: 0.6287 - acc: 0.662 - ETA: 9:33 - loss: 0.6284 - acc: 0.662 - ETA: 9:31 - loss: 0.6272 - acc: 0.664 - ETA: 9:28 - loss: 0.6265 - acc: 0.665 - ETA: 9:25 - loss: 0.6272 - acc: 0.665 - ETA: 9:22 - loss: 0.6258 - acc: 0.665 - ETA: 9:19 - loss: 0.6255 - acc: 0.666 - ETA: 9:16 - loss: 0.6247 - acc: 0.667 - ETA: 9:13 - loss: 0.6232 - acc: 0.668 - ETA: 9:10 - loss: 0.6241 - acc: 0.668 - ETA: 9:07 - loss: 0.6245 - acc: 0.668 - ETA: 9:04 - loss: 0.6249 - acc: 0.668 - ETA: 9:03 - loss: 0.6256 - acc: 0.668 - ETA: 9:00 - loss: 0.6250 - acc: 0.669 - ETA: 8:57 - loss: 0.6258 - acc: 0.668 - ETA: 8:54 - loss: 0.6256 - acc: 0.667 - ETA: 8:51 - loss: 0.6258 - acc: 0.667 - ETA: 8:49 - loss: 0.6260 - acc: 0.667 - ETA: 8:48 - loss: 0.6246 - acc: 0.668 - ETA: 8:45 - loss: 0.6254 - acc: 0.668 - ETA: 8:42 - loss: 0.6268 - acc: 0.667 - ETA: 8:39 - loss: 0.6255 - acc: 0.668 - ETA: 8:36 - loss: 0.6249 - acc: 0.669 - ETA: 8:33 - loss: 0.6228 - acc: 0.671 - ETA: 8:31 - loss: 0.6243 - acc: 0.670 - ETA: 8:28 - loss: 0.6252 - acc: 0.670 - ETA: 8:24 - loss: 0.6260 - acc: 0.669 - ETA: 8:22 - loss: 0.6260 - acc: 0.669 - ETA: 8:19 - loss: 0.6251 - acc: 0.669 - ETA: 8:16 - loss: 0.6244 - acc: 0.669 - ETA: 8:13 - loss: 0.6245 - acc: 0.669 - ETA: 8:10 - loss: 0.6244 - acc: 0.669 - ETA: 8:07 - loss: 0.6242 - acc: 0.670 - ETA: 8:04 - loss: 0.6244 - acc: 0.669 - ETA: 8:01 - loss: 0.6244 - acc: 0.669 - ETA: 7:58 - loss: 0.6236 - acc: 0.670 - ETA: 7:56 - loss: 0.6233 - acc: 0.671 - ETA: 7:53 - loss: 0.6231 - acc: 0.670 - ETA: 7:51 - loss: 0.6238 - acc: 0.670 - ETA: 7:48 - loss: 0.6255 - acc: 0.670 - ETA: 7:45 - loss: 0.6252 - acc: 0.670 - ETA: 7:42 - loss: 0.6249 - acc: 0.670 - ETA: 7:39 - loss: 0.6261 - acc: 0.670 - ETA: 7:36 - loss: 0.6253 - acc: 0.671 - ETA: 7:34 - loss: 0.6257 - acc: 0.670 - ETA: 7:31 - loss: 0.6254 - acc: 0.671 - ETA: 7:28 - loss: 0.6252 - acc: 0.670 - ETA: 7:25 - loss: 0.6264 - acc: 0.669 - ETA: 7:22 - loss: 0.6278 - acc: 0.668 - ETA: 7:20 - loss: 0.6279 - acc: 0.668 - ETA: 7:17 - loss: 0.6276 - acc: 0.669 - ETA: 7:14 - loss: 0.6280 - acc: 0.668 - ETA: 7:11 - loss: 0.6273 - acc: 0.668 - ETA: 7:08 - loss: 0.6276 - acc: 0.668 - ETA: 7:05 - loss: 0.6273 - acc: 0.668 - ETA: 7:03 - loss: 0.6264 - acc: 0.668 - ETA: 7:00 - loss: 0.6270 - acc: 0.668 - ETA: 6:57 - loss: 0.6275 - acc: 0.668 - ETA: 6:54 - loss: 0.6268 - acc: 0.668 - ETA: 6:52 - loss: 0.6256 - acc: 0.669 - ETA: 6:49 - loss: 0.6253 - acc: 0.670 - ETA: 6:46 - loss: 0.6252 - acc: 0.670 - ETA: 6:43 - loss: 0.6244 - acc: 0.670 - ETA: 6:41 - loss: 0.6241 - acc: 0.670 - ETA: 6:38 - loss: 0.6241 - acc: 0.670 - ETA: 6:35 - loss: 0.6238 - acc: 0.670 - ETA: 6:33 - loss: 0.6243 - acc: 0.669 - ETA: 6:30 - loss: 0.6257 - acc: 0.670 - ETA: 6:27 - loss: 0.6249 - acc: 0.670 - ETA: 6:24 - loss: 0.6267 - acc: 0.670 - ETA: 6:21 - loss: 0.6262 - acc: 0.671 - ETA: 6:19 - loss: 0.6261 - acc: 0.671 - ETA: 6:16 - loss: 0.6263 - acc: 0.671 - ETA: 6:13 - loss: 0.6259 - acc: 0.671 - ETA: 6:10 - loss: 0.6262 - acc: 0.670 - ETA: 6:08 - loss: 0.6253 - acc: 0.671 - ETA: 6:05 - loss: 0.6254 - acc: 0.671 - ETA: 6:02 - loss: 0.6246 - acc: 0.672 - ETA: 5:59 - loss: 0.6254 - acc: 0.671 - ETA: 5:57 - loss: 0.6253 - acc: 0.671 - ETA: 5:54 - loss: 0.6253 - acc: 0.671 - ETA: 5:51 - loss: 0.6250 - acc: 0.671 - ETA: 5:49 - loss: 0.6257 - acc: 0.671 - ETA: 5:46 - loss: 0.6254 - acc: 0.671 - ETA: 5:44 - loss: 0.6252 - acc: 0.671 - ETA: 5:41 - loss: 0.6248 - acc: 0.671 - ETA: 5:39 - loss: 0.6245 - acc: 0.672 - ETA: 5:36 - loss: 0.6252 - acc: 0.672 - ETA: 5:34 - loss: 0.6253 - acc: 0.672 - ETA: 5:32 - loss: 0.6257 - acc: 0.671 - ETA: 5:29 - loss: 0.6256 - acc: 0.671 - ETA: 5:27 - loss: 0.6252 - acc: 0.672 - ETA: 5:25 - loss: 0.6248 - acc: 0.672 - ETA: 5:23 - loss: 0.6250 - acc: 0.672 - ETA: 5:20 - loss: 0.6241 - acc: 0.672 - ETA: 5:17 - loss: 0.6240 - acc: 0.672 - ETA: 5:15 - loss: 0.6244 - acc: 0.672 - ETA: 5:12 - loss: 0.6244 - acc: 0.672 - ETA: 5:10 - loss: 0.6253 - acc: 0.672 - ETA: 5:07 - loss: 0.6258 - acc: 0.672 - ETA: 5:04 - loss: 0.6256 - acc: 0.671 - ETA: 5:01 - loss: 0.6254 - acc: 0.672 - ETA: 4:59 - loss: 0.6256 - acc: 0.672 - ETA: 4:56 - loss: 0.6249 - acc: 0.672 - ETA: 4:53 - loss: 0.6251 - acc: 0.672 - ETA: 4:51 - loss: 0.6254 - acc: 0.672 - ETA: 4:48 - loss: 0.6249 - acc: 0.672 - ETA: 4:45 - loss: 0.6249 - acc: 0.672 - ETA: 4:43 - loss: 0.6247 - acc: 0.672 - ETA: 4:40 - loss: 0.6247 - acc: 0.672 - ETA: 4:37 - loss: 0.6250 - acc: 0.672 - ETA: 4:35 - loss: 0.6261 - acc: 0.672 - ETA: 4:32 - loss: 0.6256 - acc: 0.673 - ETA: 4:30 - loss: 0.6252 - acc: 0.673 - ETA: 4:27 - loss: 0.6251 - acc: 0.673 - ETA: 4:24 - loss: 0.6258 - acc: 0.672 - ETA: 4:21 - loss: 0.6258 - acc: 0.673 - ETA: 4:18 - loss: 0.6255 - acc: 0.673 - ETA: 4:16 - loss: 0.6250 - acc: 0.673 - ETA: 4:13 - loss: 0.6251 - acc: 0.673 - ETA: 4:11 - loss: 0.6247 - acc: 0.674 - ETA: 4:08 - loss: 0.6242 - acc: 0.674 - ETA: 4:05 - loss: 0.6237 - acc: 0.674 - ETA: 4:03 - loss: 0.6235 - acc: 0.674 - ETA: 4:00 - loss: 0.6241 - acc: 0.674 - ETA: 3:57 - loss: 0.6236 - acc: 0.675 - ETA: 3:54 - loss: 0.6235 - acc: 0.675 - ETA: 3:52 - loss: 0.6231 - acc: 0.675 - ETA: 3:49 - loss: 0.6230 - acc: 0.675 - ETA: 3:46 - loss: 0.6235 - acc: 0.674 - ETA: 3:43 - loss: 0.6236 - acc: 0.674 - ETA: 3:41 - loss: 0.6233 - acc: 0.675 - ETA: 3:38 - loss: 0.6260 - acc: 0.674 - ETA: 3:35 - loss: 0.6274 - acc: 0.674 - ETA: 3:32 - loss: 0.6276 - acc: 0.674 - ETA: 3:29 - loss: 0.6274 - acc: 0.674 - ETA: 3:27 - loss: 0.6276 - acc: 0.674 - ETA: 3:24 - loss: 0.6280 - acc: 0.674 - ETA: 3:21 - loss: 0.6281 - acc: 0.673 - ETA: 3:18 - loss: 0.6285 - acc: 0.673 - ETA: 3:15 - loss: 0.6290 - acc: 0.673 - ETA: 3:12 - loss: 0.6288 - acc: 0.6733273/273 [==============================] - ETA: 3:10 - loss: 0.6289 - acc: 0.673 - ETA: 3:07 - loss: 0.6289 - acc: 0.673 - ETA: 3:04 - loss: 0.6286 - acc: 0.673 - ETA: 3:01 - loss: 0.6284 - acc: 0.673 - ETA: 2:58 - loss: 0.6277 - acc: 0.673 - ETA: 2:56 - loss: 0.6272 - acc: 0.674 - ETA: 2:53 - loss: 0.6274 - acc: 0.674 - ETA: 2:50 - loss: 0.6274 - acc: 0.674 - ETA: 2:47 - loss: 0.6274 - acc: 0.674 - ETA: 2:44 - loss: 0.6278 - acc: 0.673 - ETA: 2:42 - loss: 0.6280 - acc: 0.673 - ETA: 2:39 - loss: 0.6273 - acc: 0.674 - ETA: 2:36 - loss: 0.6276 - acc: 0.674 - ETA: 2:33 - loss: 0.6276 - acc: 0.674 - ETA: 2:30 - loss: 0.6277 - acc: 0.674 - ETA: 2:28 - loss: 0.6278 - acc: 0.673 - ETA: 2:25 - loss: 0.6277 - acc: 0.673 - ETA: 2:22 - loss: 0.6279 - acc: 0.674 - ETA: 2:19 - loss: 0.6278 - acc: 0.674 - ETA: 2:16 - loss: 0.6278 - acc: 0.673 - ETA: 2:13 - loss: 0.6275 - acc: 0.673 - ETA: 2:11 - loss: 0.6274 - acc: 0.673 - ETA: 2:08 - loss: 0.6278 - acc: 0.674 - ETA: 2:05 - loss: 0.6275 - acc: 0.674 - ETA: 2:02 - loss: 0.6276 - acc: 0.674 - ETA: 1:59 - loss: 0.6278 - acc: 0.674 - ETA: 1:57 - loss: 0.6278 - acc: 0.674 - ETA: 1:54 - loss: 0.6278 - acc: 0.674 - ETA: 1:51 - loss: 0.6278 - acc: 0.674 - ETA: 1:48 - loss: 0.6277 - acc: 0.675 - ETA: 1:45 - loss: 0.6278 - acc: 0.674 - ETA: 1:42 - loss: 0.6278 - acc: 0.674 - ETA: 1:40 - loss: 0.6277 - acc: 0.674 - ETA: 1:37 - loss: 0.6283 - acc: 0.674 - ETA: 1:34 - loss: 0.6286 - acc: 0.673 - ETA: 1:31 - loss: 0.6284 - acc: 0.673 - ETA: 1:29 - loss: 0.6282 - acc: 0.673 - ETA: 1:26 - loss: 0.6280 - acc: 0.674 - ETA: 1:23 - loss: 0.6278 - acc: 0.674 - ETA: 1:20 - loss: 0.6281 - acc: 0.674 - ETA: 1:17 - loss: 0.6304 - acc: 0.673 - ETA: 1:15 - loss: 0.6314 - acc: 0.672 - ETA: 1:12 - loss: 0.6315 - acc: 0.672 - ETA: 1:09 - loss: 0.6317 - acc: 0.672 - ETA: 1:06 - loss: 0.6317 - acc: 0.672 - ETA: 1:03 - loss: 0.6314 - acc: 0.672 - ETA: 1:01 - loss: 0.6314 - acc: 0.671 - ETA: 58s - loss: 0.6326 - acc: 0.671 - ETA: 55s - loss: 0.6328 - acc: 0.67 - ETA: 52s - loss: 0.6329 - acc: 0.67 - ETA: 50s - loss: 0.6327 - acc: 0.67 - ETA: 47s - loss: 0.6324 - acc: 0.67 - ETA: 44s - loss: 0.6322 - acc: 0.67 - ETA: 41s - loss: 0.6324 - acc: 0.67 - ETA: 38s - loss: 0.6324 - acc: 0.67 - ETA: 36s - loss: 0.6320 - acc: 0.67 - ETA: 33s - loss: 0.6318 - acc: 0.67 - ETA: 30s - loss: 0.6311 - acc: 0.67 - ETA: 27s - loss: 0.6311 - acc: 0.67 - ETA: 24s - loss: 0.6313 - acc: 0.67 - ETA: 22s - loss: 0.6314 - acc: 0.67 - ETA: 19s - loss: 0.6310 - acc: 0.67 - ETA: 16s - loss: 0.6309 - acc: 0.67 - ETA: 13s - loss: 0.6308 - acc: 0.67 - ETA: 11s - loss: 0.6305 - acc: 0.67 - ETA: 8s - loss: 0.6304 - acc: 0.6723 - ETA: 5s - loss: 0.6301 - acc: 0.672 - ETA: 2s - loss: 0.6298 - acc: 0.672 - 893s 3s/step - loss: 0.6295 - acc: 0.6725 - val_loss: 0.6113 - val_acc: 0.6677
Epoch 2/2
204/273 [=====================>........] - ETA: 14:09 - loss: 0.5956 - acc: 0.67 - ETA: 14:06 - loss: 0.6038 - acc: 0.66 - ETA: 13:59 - loss: 0.5941 - acc: 0.67 - ETA: 13:55 - loss: 0.5695 - acc: 0.69 - ETA: 14:01 - loss: 0.5622 - acc: 0.70 - ETA: 13:43 - loss: 0.6455 - acc: 0.69 - ETA: 13:22 - loss: 0.6448 - acc: 0.69 - ETA: 13:06 - loss: 0.6559 - acc: 0.68 - ETA: 13:01 - loss: 0.6470 - acc: 0.67 - ETA: 12:52 - loss: 0.6394 - acc: 0.68 - ETA: 12:48 - loss: 0.6409 - acc: 0.67 - ETA: 12:36 - loss: 0.6421 - acc: 0.67 - ETA: 12:29 - loss: 0.6374 - acc: 0.66 - ETA: 12:24 - loss: 0.6374 - acc: 0.65 - ETA: 12:21 - loss: 0.6409 - acc: 0.66 - ETA: 12:12 - loss: 0.6404 - acc: 0.66 - ETA: 12:12 - loss: 0.6371 - acc: 0.66 - ETA: 12:07 - loss: 0.6305 - acc: 0.66 - ETA: 12:02 - loss: 0.6359 - acc: 0.66 - ETA: 11:56 - loss: 0.6485 - acc: 0.66 - ETA: 11:51 - loss: 0.6522 - acc: 0.66 - ETA: 11:46 - loss: 0.6525 - acc: 0.65 - ETA: 11:48 - loss: 0.6522 - acc: 0.65 - ETA: 11:44 - loss: 0.6495 - acc: 0.65 - ETA: 11:40 - loss: 0.6478 - acc: 0.66 - ETA: 11:34 - loss: 0.6479 - acc: 0.66 - ETA: 11:31 - loss: 0.6442 - acc: 0.66 - ETA: 11:29 - loss: 0.6430 - acc: 0.66 - ETA: 11:27 - loss: 0.6429 - acc: 0.66 - ETA: 11:26 - loss: 0.6424 - acc: 0.66 - ETA: 11:24 - loss: 0.6461 - acc: 0.66 - ETA: 11:22 - loss: 0.6460 - acc: 0.66 - ETA: 11:19 - loss: 0.6450 - acc: 0.66 - ETA: 11:18 - loss: 0.6427 - acc: 0.66 - ETA: 11:14 - loss: 0.6423 - acc: 0.66 - ETA: 11:10 - loss: 0.6439 - acc: 0.65 - ETA: 11:07 - loss: 0.6444 - acc: 0.66 - ETA: 11:04 - loss: 0.6451 - acc: 0.66 - ETA: 11:02 - loss: 0.6446 - acc: 0.66 - ETA: 10:59 - loss: 0.6440 - acc: 0.65 - ETA: 10:56 - loss: 0.6419 - acc: 0.66 - ETA: 10:52 - loss: 0.6416 - acc: 0.66 - ETA: 10:48 - loss: 0.6416 - acc: 0.66 - ETA: 10:44 - loss: 0.6407 - acc: 0.65 - ETA: 10:41 - loss: 0.6395 - acc: 0.66 - ETA: 10:40 - loss: 0.6376 - acc: 0.66 - ETA: 10:36 - loss: 0.6375 - acc: 0.66 - ETA: 10:33 - loss: 0.6379 - acc: 0.66 - ETA: 10:28 - loss: 0.6356 - acc: 0.66 - ETA: 10:25 - loss: 0.6348 - acc: 0.66 - ETA: 10:22 - loss: 0.6315 - acc: 0.66 - ETA: 10:19 - loss: 0.6294 - acc: 0.66 - ETA: 10:16 - loss: 0.6289 - acc: 0.66 - ETA: 10:12 - loss: 0.6270 - acc: 0.66 - ETA: 10:09 - loss: 0.6362 - acc: 0.66 - ETA: 10:06 - loss: 0.6358 - acc: 0.66 - ETA: 10:02 - loss: 0.6358 - acc: 0.66 - ETA: 10:00 - loss: 0.6350 - acc: 0.66 - ETA: 9:57 - loss: 0.6343 - acc: 0.6655 - ETA: 9:54 - loss: 0.6337 - acc: 0.666 - ETA: 9:51 - loss: 0.6339 - acc: 0.667 - ETA: 9:49 - loss: 0.6350 - acc: 0.665 - ETA: 9:47 - loss: 0.6345 - acc: 0.664 - ETA: 9:44 - loss: 0.6335 - acc: 0.664 - ETA: 9:41 - loss: 0.6333 - acc: 0.664 - ETA: 9:38 - loss: 0.6326 - acc: 0.664 - ETA: 9:35 - loss: 0.6337 - acc: 0.664 - ETA: 9:33 - loss: 0.6338 - acc: 0.664 - ETA: 9:31 - loss: 0.6342 - acc: 0.664 - ETA: 9:28 - loss: 0.6333 - acc: 0.665 - ETA: 9:25 - loss: 0.6332 - acc: 0.665 - ETA: 9:22 - loss: 0.6329 - acc: 0.665 - ETA: 9:20 - loss: 0.6324 - acc: 0.665 - ETA: 9:18 - loss: 0.6365 - acc: 0.662 - ETA: 9:15 - loss: 0.6351 - acc: 0.663 - ETA: 9:12 - loss: 0.6350 - acc: 0.663 - ETA: 9:09 - loss: 0.6351 - acc: 0.663 - ETA: 9:07 - loss: 0.6333 - acc: 0.664 - ETA: 9:03 - loss: 0.6333 - acc: 0.665 - ETA: 9:01 - loss: 0.6338 - acc: 0.664 - ETA: 8:58 - loss: 0.6328 - acc: 0.664 - ETA: 8:56 - loss: 0.6332 - acc: 0.664 - ETA: 8:52 - loss: 0.6343 - acc: 0.663 - ETA: 8:49 - loss: 0.6342 - acc: 0.662 - ETA: 8:46 - loss: 0.6343 - acc: 0.662 - ETA: 8:44 - loss: 0.6331 - acc: 0.663 - ETA: 8:41 - loss: 0.6339 - acc: 0.664 - ETA: 8:38 - loss: 0.6340 - acc: 0.663 - ETA: 8:35 - loss: 0.6332 - acc: 0.663 - ETA: 8:32 - loss: 0.6321 - acc: 0.664 - ETA: 8:29 - loss: 0.6313 - acc: 0.665 - ETA: 8:26 - loss: 0.6295 - acc: 0.666 - ETA: 8:23 - loss: 0.6300 - acc: 0.666 - ETA: 8:20 - loss: 0.6302 - acc: 0.666 - ETA: 8:17 - loss: 0.6303 - acc: 0.666 - ETA: 8:15 - loss: 0.6304 - acc: 0.667 - ETA: 8:12 - loss: 0.6307 - acc: 0.667 - ETA: 8:09 - loss: 0.6289 - acc: 0.668 - ETA: 8:06 - loss: 0.6285 - acc: 0.668 - ETA: 8:03 - loss: 0.6295 - acc: 0.668 - ETA: 8:00 - loss: 0.6300 - acc: 0.668 - ETA: 7:57 - loss: 0.6301 - acc: 0.669 - ETA: 7:54 - loss: 0.6302 - acc: 0.669 - ETA: 7:52 - loss: 0.6299 - acc: 0.668 - ETA: 7:49 - loss: 0.6295 - acc: 0.668 - ETA: 7:46 - loss: 0.6298 - acc: 0.667 - ETA: 7:43 - loss: 0.6312 - acc: 0.666 - ETA: 7:40 - loss: 0.6299 - acc: 0.667 - ETA: 7:38 - loss: 0.6294 - acc: 0.667 - ETA: 7:35 - loss: 0.6286 - acc: 0.667 - ETA: 7:32 - loss: 0.6296 - acc: 0.666 - ETA: 7:29 - loss: 0.6303 - acc: 0.665 - ETA: 7:26 - loss: 0.6297 - acc: 0.666 - ETA: 7:23 - loss: 0.6301 - acc: 0.666 - ETA: 7:21 - loss: 0.6310 - acc: 0.666 - ETA: 7:18 - loss: 0.6312 - acc: 0.665 - ETA: 7:15 - loss: 0.6309 - acc: 0.665 - ETA: 7:12 - loss: 0.6309 - acc: 0.665 - ETA: 7:09 - loss: 0.6313 - acc: 0.664 - ETA: 7:07 - loss: 0.6321 - acc: 0.664 - ETA: 7:04 - loss: 0.6322 - acc: 0.664 - ETA: 7:01 - loss: 0.6318 - acc: 0.664 - ETA: 6:59 - loss: 0.6327 - acc: 0.663 - ETA: 6:56 - loss: 0.6324 - acc: 0.662 - ETA: 6:53 - loss: 0.6325 - acc: 0.662 - ETA: 6:50 - loss: 0.6341 - acc: 0.662 - ETA: 6:48 - loss: 0.6363 - acc: 0.661 - ETA: 6:45 - loss: 0.6363 - acc: 0.660 - ETA: 6:42 - loss: 0.6362 - acc: 0.660 - ETA: 6:39 - loss: 0.6360 - acc: 0.660 - ETA: 6:36 - loss: 0.6353 - acc: 0.661 - ETA: 6:33 - loss: 0.6348 - acc: 0.661 - ETA: 6:31 - loss: 0.6336 - acc: 0.663 - ETA: 6:28 - loss: 0.6337 - acc: 0.663 - ETA: 6:25 - loss: 0.6369 - acc: 0.662 - ETA: 6:22 - loss: 0.6416 - acc: 0.660 - ETA: 6:19 - loss: 0.6417 - acc: 0.660 - ETA: 6:16 - loss: 0.6418 - acc: 0.659 - ETA: 6:14 - loss: 0.6415 - acc: 0.660 - ETA: 6:10 - loss: 0.6415 - acc: 0.660 - ETA: 6:08 - loss: 0.6416 - acc: 0.660 - ETA: 6:05 - loss: 0.6408 - acc: 0.660 - ETA: 6:02 - loss: 0.6406 - acc: 0.660 - ETA: 5:59 - loss: 0.6411 - acc: 0.660 - ETA: 5:56 - loss: 0.6409 - acc: 0.661 - ETA: 5:53 - loss: 0.6406 - acc: 0.661 - ETA: 5:51 - loss: 0.6399 - acc: 0.661 - ETA: 5:48 - loss: 0.6394 - acc: 0.662 - ETA: 5:45 - loss: 0.6396 - acc: 0.661 - ETA: 5:42 - loss: 0.6391 - acc: 0.662 - ETA: 5:40 - loss: 0.6394 - acc: 0.661 - ETA: 5:37 - loss: 0.6396 - acc: 0.661 - ETA: 5:34 - loss: 0.6396 - acc: 0.661 - ETA: 5:31 - loss: 0.6400 - acc: 0.661 - ETA: 5:29 - loss: 0.6395 - acc: 0.661 - ETA: 5:26 - loss: 0.6402 - acc: 0.661 - ETA: 5:23 - loss: 0.6401 - acc: 0.661 - ETA: 5:20 - loss: 0.6396 - acc: 0.661 - ETA: 5:18 - loss: 0.6394 - acc: 0.661 - ETA: 5:15 - loss: 0.6396 - acc: 0.660 - ETA: 5:12 - loss: 0.6395 - acc: 0.660 - ETA: 5:09 - loss: 0.6395 - acc: 0.660 - ETA: 5:06 - loss: 0.6393 - acc: 0.660 - ETA: 5:04 - loss: 0.6393 - acc: 0.660 - ETA: 5:01 - loss: 0.6397 - acc: 0.660 - ETA: 4:58 - loss: 0.6399 - acc: 0.659 - ETA: 4:55 - loss: 0.6394 - acc: 0.660 - ETA: 4:52 - loss: 0.6395 - acc: 0.660 - ETA: 4:49 - loss: 0.6397 - acc: 0.660 - ETA: 4:47 - loss: 0.6401 - acc: 0.659 - ETA: 4:44 - loss: 0.6402 - acc: 0.659 - ETA: 4:41 - loss: 0.6399 - acc: 0.659 - ETA: 4:38 - loss: 0.6401 - acc: 0.659 - ETA: 4:35 - loss: 0.6401 - acc: 0.659 - ETA: 4:33 - loss: 0.6400 - acc: 0.659 - ETA: 4:30 - loss: 0.6399 - acc: 0.659 - ETA: 4:27 - loss: 0.6398 - acc: 0.659 - ETA: 4:24 - loss: 0.6394 - acc: 0.659 - ETA: 4:22 - loss: 0.6391 - acc: 0.659 - ETA: 4:18 - loss: 0.6390 - acc: 0.659 - ETA: 4:16 - loss: 0.6404 - acc: 0.658 - ETA: 4:13 - loss: 0.6410 - acc: 0.658 - ETA: 4:10 - loss: 0.6412 - acc: 0.658 - ETA: 4:07 - loss: 0.6413 - acc: 0.658 - ETA: 4:05 - loss: 0.6410 - acc: 0.658 - ETA: 4:02 - loss: 0.6405 - acc: 0.658 - ETA: 3:59 - loss: 0.6402 - acc: 0.658 - ETA: 3:56 - loss: 0.6401 - acc: 0.659 - ETA: 3:54 - loss: 0.6401 - acc: 0.658 - ETA: 3:51 - loss: 0.6406 - acc: 0.658 - ETA: 3:48 - loss: 0.6410 - acc: 0.657 - ETA: 3:45 - loss: 0.6406 - acc: 0.658 - ETA: 3:43 - loss: 0.6401 - acc: 0.658 - ETA: 3:40 - loss: 0.6400 - acc: 0.658 - ETA: 3:37 - loss: 0.6402 - acc: 0.658 - ETA: 3:34 - loss: 0.6400 - acc: 0.658 - ETA: 3:31 - loss: 0.6401 - acc: 0.658 - ETA: 3:29 - loss: 0.6396 - acc: 0.658 - ETA: 3:26 - loss: 0.6394 - acc: 0.659 - ETA: 3:23 - loss: 0.6391 - acc: 0.659 - ETA: 3:20 - loss: 0.6391 - acc: 0.659 - ETA: 3:18 - loss: 0.6393 - acc: 0.658 - ETA: 3:15 - loss: 0.6395 - acc: 0.658 - ETA: 3:12 - loss: 0.6391 - acc: 0.6588273/273 [==============================] - ETA: 3:09 - loss: 0.6387 - acc: 0.658 - ETA: 3:07 - loss: 0.6384 - acc: 0.659 - ETA: 3:04 - loss: 0.6381 - acc: 0.659 - ETA: 3:01 - loss: 0.6384 - acc: 0.659 - ETA: 2:58 - loss: 0.6385 - acc: 0.659 - ETA: 2:56 - loss: 0.6386 - acc: 0.658 - ETA: 2:53 - loss: 0.6380 - acc: 0.659 - ETA: 2:50 - loss: 0.6395 - acc: 0.659 - ETA: 2:47 - loss: 0.6398 - acc: 0.659 - ETA: 2:44 - loss: 0.6397 - acc: 0.659 - ETA: 2:42 - loss: 0.6396 - acc: 0.659 - ETA: 2:39 - loss: 0.6394 - acc: 0.659 - ETA: 2:36 - loss: 0.6390 - acc: 0.659 - ETA: 2:33 - loss: 0.6389 - acc: 0.659 - ETA: 2:30 - loss: 0.6389 - acc: 0.659 - ETA: 2:28 - loss: 0.6391 - acc: 0.659 - ETA: 2:25 - loss: 0.6391 - acc: 0.659 - ETA: 2:22 - loss: 0.6388 - acc: 0.659 - ETA: 2:19 - loss: 0.6386 - acc: 0.659 - ETA: 2:16 - loss: 0.6385 - acc: 0.659 - ETA: 2:13 - loss: 0.6387 - acc: 0.659 - ETA: 2:11 - loss: 0.6389 - acc: 0.658 - ETA: 2:08 - loss: 0.6394 - acc: 0.658 - ETA: 2:05 - loss: 0.6392 - acc: 0.658 - ETA: 2:03 - loss: 0.6392 - acc: 0.658 - ETA: 2:00 - loss: 0.6396 - acc: 0.658 - ETA: 1:57 - loss: 0.6396 - acc: 0.658 - ETA: 1:54 - loss: 0.6396 - acc: 0.658 - ETA: 1:52 - loss: 0.6392 - acc: 0.658 - ETA: 1:49 - loss: 0.6387 - acc: 0.658 - ETA: 1:46 - loss: 0.6384 - acc: 0.658 - ETA: 1:43 - loss: 0.6384 - acc: 0.659 - ETA: 1:40 - loss: 0.6383 - acc: 0.659 - ETA: 1:38 - loss: 0.6378 - acc: 0.659 - ETA: 1:35 - loss: 0.6372 - acc: 0.659 - ETA: 1:32 - loss: 0.6369 - acc: 0.659 - ETA: 1:29 - loss: 0.6376 - acc: 0.659 - ETA: 1:27 - loss: 0.6374 - acc: 0.659 - ETA: 1:24 - loss: 0.6373 - acc: 0.660 - ETA: 1:21 - loss: 0.6376 - acc: 0.659 - ETA: 1:18 - loss: 0.6375 - acc: 0.659 - ETA: 1:15 - loss: 0.6374 - acc: 0.659 - ETA: 1:13 - loss: 0.6372 - acc: 0.659 - ETA: 1:10 - loss: 0.6372 - acc: 0.660 - ETA: 1:07 - loss: 0.6375 - acc: 0.659 - ETA: 1:04 - loss: 0.6377 - acc: 0.659 - ETA: 1:01 - loss: 0.6381 - acc: 0.658 - ETA: 59s - loss: 0.6382 - acc: 0.658 - ETA: 56s - loss: 0.6392 - acc: 0.65 - ETA: 53s - loss: 0.6394 - acc: 0.65 - ETA: 50s - loss: 0.6393 - acc: 0.65 - ETA: 47s - loss: 0.6390 - acc: 0.65 - ETA: 45s - loss: 0.6385 - acc: 0.65 - ETA: 42s - loss: 0.6383 - acc: 0.65 - ETA: 39s - loss: 0.6383 - acc: 0.65 - ETA: 36s - loss: 0.6382 - acc: 0.65 - ETA: 33s - loss: 0.6382 - acc: 0.65 - ETA: 31s - loss: 0.6380 - acc: 0.65 - ETA: 28s - loss: 0.6380 - acc: 0.65 - ETA: 25s - loss: 0.6412 - acc: 0.65 - ETA: 22s - loss: 0.6415 - acc: 0.65 - ETA: 19s - loss: 0.6415 - acc: 0.65 - ETA: 16s - loss: 0.6416 - acc: 0.65 - ETA: 14s - loss: 0.6416 - acc: 0.65 - ETA: 11s - loss: 0.6418 - acc: 0.65 - ETA: 8s - loss: 0.6417 - acc: 0.6584 - ETA: 5s - loss: 0.6418 - acc: 0.658 - ETA: 2s - loss: 0.6419 - acc: 0.657 - 915s 3s/step - loss: 0.6420 - acc: 0.6575 - val_loss: 0.6175 - val_acc: 0.6580

Guardar modelo

In [20]:
model.save_weights('model_wieghts.h5')
model.save('model_keras.h5')

Preparar data para prededir

In [11]:
X_test, Y_test = preparar_data(test_images_dogs_cats) #Y_test en este caso será []

configuración

In [21]:
test_datagen = ImageDataGenerator(rescale=1. / 255)

predicción

In [ ]:
test_generator = validacion_datagen.flow(np.array(X_test), batch_size=BATCH_SIZE)
prediction_probabilities = model.predict_generator(test_generator, verbose=1)