pdfcoffee

Convolutional Neural Networks
Recognizing cats with a VGG16 Net
Now let us test the image of a cat:
Note that we are going to use predefined weights:
import cv2
im = cv2.resize(cv2.imread('cat.jpg'), (224, 224).astype(np.float32))
#im = im.transpose((2,0,1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
model = VGG_16('/Users/antonio/.keras/models/vgg16_weights_tf_dim_
ordering_tf_kernels.h5')
model.summary()
model.compile(optimizer='sgd', loss='categorical_crossentropy')
out = model.predict(im)
print(np.argmax(out))
When the code is executed, the class 285 is returned, which corresponds (https://
gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) to "Egyptian cat":
Figure 19: Image recognition results using a VGG16 Net
Impressive isn't it? Our VGG-16 network can successfully recognize images of cats!
A first important step for deep learning. It is only five years since the paper in [4],
but that was a game-changing moment.
[ 134 ]