20.11. Building Neural Networks with Keras and TensorFlow: Fine-tuning and Transfer of Learning
As we venture into the field of Machine Learning and especially Deep Learning, we are faced with challenges that go beyond simply training models from scratch. Two powerful techniques that emerge as solutions to these challenges are fine-tuning and learning transfer. Using frameworks such as Keras and TensorFlow, these techniques can be implemented effectively, speeding up the development process and improving the performance of models in specific tasks.
What is Learning Transfer?
Transfer learning is a technique where a model developed for one task is reused as a starting point for a model in a second task. It is especially useful in Deep Learning, where neural networks trained on large data sets can be adapted to solve related problems with less available data.
What is Fine-tuning?
Fine-tuning is a fine-tuning process where we adjust the weights of a pre-trained neural network for a new task. Instead of starting training from random weights, we start with weights from a model that has already been trained on a large and generally related dataset.
Why use these techniques?
These techniques are particularly advantageous when we have a limited dataset to train our model. Neural networks, especially deep ones, require large amounts of data to learn generalizable features. By using a pre-trained model, we can take advantage of features learned from a larger dataset and apply them to our specific problem, saving time and computational resources.
Transfer Learning with Keras and TensorFlow
Keras and TensorFlow make it easy to implement transfer learning. We can easily load pre-trained models available in Keras, such as VGG16, ResNet50, InceptionV3, among others. These models were trained on ImageNet, a vast dataset with millions of images and thousands of classes.
from keras.applications import VGG16
# Load the VGG16 model with pre-trained weights in ImageNet
# include_top=False means we will not include the final dense layers
base_model = VGG16(weights='imagenet', include_top=False)
With the base model loaded, we can add new layers on top of it to adapt to our specific task.
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
# Add new layers on top of the base model
x = base_model.output
x = GlobalAveragePooling2D() (x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
# Define the final model
model = Model(inputs=base_model.input, outputs=predictions)
Fine-tuning with Keras and TensorFlow
For fine-tuning, we freeze the initial layers of the base model and train only the upper layers that we just added. After a few epochs, we can start to unfreeze some of the last layers of the base model and continue training for fine-tuning.
# Freezes all layers of the base model
for layer in base_model.layers:
layer.trainable = False
# Compile the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
# First train only the top layers that have been added
model.fit(...)
# After initial training, fine-tuning of some layers of the base model begins
for layer in base_model.layers[:100]:
layer.trainable = True
# Recompile the model for the changes to take effect
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy')
# Continue training
model.fit(...)
It is important to recompile the model after changing the trainable attribute, as this affects the training process.
Final Considerations
Fine-tuning and transfer learning are powerful techniques that can make a big difference in the performance of Deep Learning models, especially when dealing with smaller data sets. With Keras and TensorFlow, these techniques can be applied relatively simply, allowing researchers and developers to leverage pre-trained models and adapt them to their specific needs.
It is important to remember that the success of these techniques depends on how related the original and final tasks are. Furthermore, the choice of which layers to freeze or unfreeze during fine-tuning must be made carefully, considering the specific architecture of the model and the nature of the problem at hand.
In summary, Keras and TensorFlow offer the toolsnecessary to implement transfer learning and fine-tuning efficiently, which can significantly accelerate the development of Deep Learning models and improve their results.
