from keras.models import load_model
from keras.preprocessing import image
import numpy as np

model = load_model('Xception_299x299_1x4k.hdf5')



img = image.load_img("spa.jpg", target_size=(256, 256))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = np.divide(x, 255)

scores = model.predict(x)

print(np.argmax(scores))

4c9987b0139bf2e55c06d15d45fc0bb708a2179f
<mask token>

<mask token>
print(np.argmax(scores))

<mask token>
model = load_model('Xception_299x299_1x4k.hdf5')
img = image.load_img('spa.jpg', target_size=(256, 256))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = np.divide(x, 255)
scores = model.predict(x)
print(np.argmax(scores))

from keras.models import load_model
from keras.preprocessing import image
import numpy as np
model = load_model('Xception_299x299_1x4k.hdf5')
img = image.load_img('spa.jpg', target_size=(256, 256))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = np.divide(x, 255)
scores = model.predict(x)
print(np.argmax(scores))

from keras.models import load_model
from keras.preprocessing import image
import numpy as np

model = load_model('Xception_299x299_1x4k.hdf5')



img = image.load_img("spa.jpg", target_size=(256, 256))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = np.divide(x, 255)

scores = model.predict(x)

print(np.argmax(scores))
