daniellerch

import os
import glob
import random
import numpy as np

from sklearn.metrics import accuracy_score

from keras.utils import np_utils
from keras.models import Sequential
from keras.preprocessing import image

daniellerch

import sys
import numpy as np
from PIL import Image
from scipy import misc, ndimage
from keras import Model
from keras import Sequential
from keras.layers import Activation
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import ZeroPadding2D

daniellerch

from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
from keras.models import Model
from keras.utils import np_utils
from keras.preprocessing import image
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import preprocess_input, decode_predictions
from keras.layers import Dense, GlobalAveragePooling2D
from sklearn.model_selection import train_test_split

daniellerch

from keras.applications.inception_resnet_v2 import InceptionResNetV2             
from keras.preprocessing import image                                            
from keras.applications.inception_resnet_v2 import preprocess_input, decode_predictions
import numpy as np                                                               
                                                                                 
model = InceptionResNetV2(weights='imagenet')                                    
                                                                                 
img_path = 'aguila.jpg'                                                          
img = image.load_img(img_path, target_size=(224, 224))                           

daniellerch

from keras.datasets import mnist                                                 
from keras.models import Model                                                   
from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D    
from keras.utils import np_utils                                                 
                                                                                 
(x_train, y_train), (x_test, y_test) = mnist.load_data()                         
                                                                                 
x_train = x_train.reshape(x_train.shape+(1,))                                    
x_test = x_test.reshape(x_test.shape+(1,))