Understanding Transfer Learning In Machine Learning

Introduction

Often in machine learning, one might not have sufficient data to train a deep learning model effectively. Alternatively, it isn't uncommon to come across a problem so extensive that training from scratch is computationally impractical. That's where Transfer Learning comes to the rescue!

Transfer learning, in a nutshell, is a technique where a pre-trained model is adapted for a different but related problem. You transfer the learnings from a previous model and further train the model to make accurate predictions for a new task.

Why Transfer Learning?

There are two major reasons we use transfer learning:

Lack of Data: Deep Learning models require large amounts of data. However, collecting and labeling that data might be a challenging task.
Save Training Time: Training deep learning models can take days, even on multiple GPUs. Transfer learning, due to the inherent advantage of reusing a pre-trained model, speeds up the process considerably.

Use case: Image Classification using Transfer Learning

Let's walk through a code snippet in Python, using the TensorFlow package, where we utilize transfer learning for image classification.

import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras import layers

# Load pre-trained base model
base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3),
                                               include_top=False,
                                               weights='imagenet')

# Don't train existing layers
for layer in base_model.layers:
    layer.trainable = False

# Include your custom layers
x = layers.GlobalAveragePooling2D()(base_model.output)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dense(1024, activation='relu')(x)
predictions = layers.Dense(10, activation='softmax')(x)

# Create a new model
model = Model(inputs=base_model.input, outputs=predictions)

# Train the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

You can replace 'MobileNetV2' with any pre-trained model. Also, this is an example for 10 classes, you can replace 10 with number of classes you have.

Conclusion

Transfer learning is a smart way to leverage the power of neural networks for tasks where data or computational resources are limited. By transferring the knowledge gained from training on millions of images, the quality of the model's predictions can be significantly improved.

Stay tuned for more exciting topics in Machine Learning!