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Advanced Convolutional Neural Networks
Keras applications
Keras applications include models for image classification with weights
trained on ImageNet (Xception, VGG16, VGG19, ResNet, ResNetV2, ResNeXt,
InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet, and
NASNet). In addition, there are a few other reference implementations from the
community for object detection and segmentation, sequence learning (see Chapter
8, Recurrent Neural Networks), reinforcement learning (see Chapter 11, Reinforcement
Learning), and GANs (see Chapter 6, Generative Adversarial Networks).
TensorFlow Hub
TensorFlow Hub (https://www.tensorflow.org/hub) is an alternative collection
of pretrained models. However, Hub is not fully integrated with TensorFlow 2.0
as of September 2019, but this issue will surely be fixed with the final version of
2.0 (however, as of July 2019, eager computation is also supported). TensorFlow
Hub includes modules for Text Classification, Sentence Encoding (see Chapter 9,
Autoencoders), Image Classification, Feature Extraction, Image Generation with GANs
(see Chapter 6, Generative Adversarial Networks), and Video Classification. Currently,
both Google and DeepMind contribute to publishing.
See the issue about eager execution at https://github.com/
tensorflow/hub/issues/124 and the issue for full integration
at https://github.com/tensorflow/tensorflow/
issues/25362.
Let's look at an example of using TF.Hub. In this case, we have a simple image
classifier using MobileNetV2:
import matplotlib.pylab as plt
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import PIL.Image as Image
classifier_url ="https://tfhub.dev/google/tf2-preview/mobilenet_v2/
classification/2" #@param {type:"string"}
IMAGE_SHAPE = (224, 224)
# wrap the hub to work with tf.keras
classifier = tf.keras.Sequential([
hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
])
grace_hopper = tf.keras.utils.get_file('image.jpg','https://storage.
googleapis.com/download.tensorflow.org/example_images/grace_hopper.
jpg')
grace_hopper = Image.open(grace_hopper).resize(IMAGE_SHAPE)
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