vgg16_pretrained.py

import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
import matplotlib.pyplot as plt
import numpy as np
import cv2

# prebuild model with pre-trained weights on imagenet
model = VGG16(weights='imagenet', include_top=True)
model.compile(optimizer='sgd', loss='categorical_crossentropy')

# resize into VGG16 trained images' format
im = cv2.resize(cv2.imread('steam-locomotive.jpg'), (224, 224))
im = np.expand_dims(im, axis=0)
im.astype(np.float32)

# predict
out = model.predict(im)
index = np.argmax(out)
print(index)

#plt.plot(out.ravel())
#plt.show()