pdfcoffee

Since we saved the model and the weights, we do not need to train each time:
import numpy as np
import scipy.misc
from tf.keras.models import model_from_json
from tf.keras.optimizers import SGD
# load model
model_architecture = 'cifar10_architecture.json'
model_weights = 'cifar10_weights.h5'
model = model_from_json(open(model_architecture).read())
model.load_weights(model_weights)
Chapter 4
# load images
img_names = ['cat-standing.jpg', 'dog.jpg']
imgs = [np.transpose(scipy.misc.imresize(scipy.misc.imread(img_name),
(32, 32)), (2, 0, 1)).astype('float32')
for img_name in img_names]
imgs = np.array(imgs) / 255
# train
optim = SGD()
model.compile(loss='categorical_crossentropy', optimizer=optim,
metrics=['accuracy'])
# predict
predictions = model.predict_classes(imgs)
print(predictions)
Now let us get the prediction for a cat and for a dog:
We get categories 3 (cat) and 5 (dog) as output as expected. We successfully created a
CNN to classify CIFAR-10 images. Next, we will look at VGG-16: a breakthrough in
deep learning.
[ 131 ]