Transfer learning is a popular technique in machine learning that allows us to leverage pre-trained models to improve the performance of our models on new tasks. This tutorial will guide you through the basics of transfer learning, using a pre-trained model for image classification as an example.

What is Transfer Learning?

Transfer learning is the process of using a pre-trained model as a starting point for a new model. The pre-trained model has already learned a lot of features from a large dataset, which can be useful for new tasks that have similar features.

Why Use Transfer Learning?

  • Time Efficiency: Using a pre-trained model can save a lot of time and computational resources compared to training a model from scratch.
  • Better Performance: Pre-trained models often have better performance on new tasks, especially when the new dataset is small.

Steps for Transfer Learning

  1. Choose a Pre-trained Model: Select a pre-trained model that has been trained on a large dataset. For image classification, popular choices include VGG16, ResNet, and Inception.
  2. Modify the Model: Adjust the last few layers of the pre-trained model to match the number of classes in your new task.
  3. Train the Model: Train the modified model on your new dataset.

Example: Image Classification

Let's say you want to classify images of animals into different categories. You can use a pre-trained model like VGG16 for this task.

  1. Load the Pre-trained Model: Load the VGG16 model and remove the last layer.
from keras.applications import VGG16
model = VGG16(weights='imagenet', include_top=False)
  1. Add New Layers: Add new layers to the model to match the number of classes in your dataset.
from keras.layers import Dense, Flatten
from keras.models import Model

x = Flatten()(model.output)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=model.input, outputs=predictions)
  1. Compile and Train the Model: Compile and train the model on your dataset.
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=10)

Further Reading

For more information on transfer learning, you can refer to the following resources:

Transfer Learning Diagram