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Convolutional Neural Networks
If we run the code we get 820 as a result, which is the image net code for "steaming
train". Equally important, all the other classes have very weak support, as shown in
the following figure:
To conclude this section, note that VGG16 is only one of the modules that is prebuilt
in tf.Keras. A full list of pretrained models is available online (https://www.
tensorflow.org/api_docs/python/tf/keras/applications).
Recycling prebuilt deep learning models
for extracting features
One very simple idea is to use VGG16, and more generally DCNN, for feature
extraction. This code implements the idea by extracting features from a specific layer.
Note that we need to switch to the functional API since the sequential model only
accepts layers:
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras import models
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np
import cv2
# prebuild model with pre-trained weights on imagenet
base_model = VGG16(weights='imagenet', include_top=True)
print (base_model)
for i, layer in enumerate(base_model.layers):
print (i, layer.name, layer.output_shape)
# extract features from block4_pool block
model = models.Model(inputs=base_model.input,
outputs=base_model.get_layer('block4_pool').output)
[ 136 ]