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Chapter 5
Let's compute the number of training, validation, and testing examples,
then compute the initial accuracy given by the pretrained MobileNetV2:
num_train, num_val, num_test = (
metadata.splits['train'].num_examples*weight/10
for weight in SPLIT_WEIGHTS
)
initial_epochs = 10
steps_per_epoch = round(num_train)//BATCH_SIZE
validation_steps = 4
loss0,accuracy0 = model.evaluate(validation_batches, steps =
validation_steps)
We get an initial accuracy of 50%.
We can now fine-tune the composed network by training for a few iterations
and optimizing the non-frozen layers:
history = model.fit(train_batches,
epochs=initial_epochs,
validation_data=validation_batches)
Thanks to the transfer learning, our network reaches a very high accuracy of 98%
by using Google MobileNetV2 trained on ImageNet. Transfer learning speeds up
training by reusing existing pretrained image classification models and retraining
only the top layer of the network to determine the class that each image belongs to:
Figure 19: Model accuracy using transfer learning
In this section we learned how to use a pretrained model. The next section will
explain where we can find a repository with many models.
Application Zoos with tf.keras and
TensorFlow Hub
One of the nice things about transfer learning is that it is possible to reuse pretrained
networks to save time and resources. There are many collections of ready-to-use
networks out there, but the following two are the most used.
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