Transfer learning

Last updated on 2024-11-19 | Edit this page

Estimated time: 50 minutes

Overview

Questions

  • How do I apply a pre-trained model to my data?

Objectives

  • Adapt a state-of-the-art pre-trained network to your own dataset

What is transfer learning?

Instead of training a model from scratch, with transfer learning you make use of models that are trained on another machine learning task. The pre-trained network captures generic knowledge during pre-training and will only be ‘fine-tuned’ to the specifics of your dataset.

An example: Let’s say that you want to train a model to classify images of different dog breeds. You could make use of a pre-trained network that learned how to classify images of dogs and cats. The pre-trained network will not know anything about different dog breeds, but it will have captured some general knowledge of, on a high-level, what dogs look like, and on a low-level all the different features (eyes, ears, paws, fur) that make up an image of a dog. Further training this model on your dog breed dataset is a much easier task than training from scratch, because the model can use the general knowledge captured in the pre-trained network.

In this episode we will learn how use Keras to adapt a state-of-the-art pre-trained model to the Dollar Street Dataset.

1. Formulate / Outline the problem and 2. Identify inputs and outputs

Just like in the previous episode, we use the Dollar Street 10 dataset. The goal is to predict one out of 10 classes for a given image.

We load the data in the same way as the previous episode:

PYTHON 

import pathlib
import numpy as np

DATA_FOLDER = pathlib.Path('data/dataset_dollarstreet/') # change to location where you stored the data
train_images = np.load(DATA_FOLDER / 'train_images.npy')
val_images = np.load(DATA_FOLDER / 'test_images.npy')
train_labels = np.load(DATA_FOLDER / 'train_labels.npy')
val_labels = np.load(DATA_FOLDER / 'test_labels.npy')

3. Prepare the data

We prepare the data as before, scaling the values between 0 and 1.

PYTHON 

train_images = train_images / 255.0
val_images = val_images / 255.0

4. Choose a pre-trained model or start building architecture from scratch

Let’s define our model input layer using the shape of our training images:

PYTHON 

# input tensor
inputs = keras.Input(train_images.shape[1:])

Our images are 64 x 64 pixels, whereas the pre-trained model that we will use was trained on images of 160 x 160 pixels. To deal with this, we add an upscale layer that resizes the images to 160 x 160 pixels during training and prediction.

PYTHON 

# upscale layer
method = tf.image.ResizeMethod.BILINEAR
upscale = keras.layers.Lambda(
  lambda x: tf.image.resize_with_pad(x, 160, 160, method=method))(inputs)

From the keras.applications module we use the DenseNet121 architecture. This architecture was proposed by the paper: Densely Connected Convolutional Networks (CVPR 2017). It is trained on the Imagenet dataset, which contains 14,197,122 annotated images according to the WordNet hierarchy with over 20,000 classes.

We will have a look at the architecture later, for now it is enough to know that it is a convolutional neural network with 121 layers that was designed to work well on image classification tasks.

Let’s configure the DenseNet121:

PYTHON 

base_model = keras.applications.DenseNet121(include_top=False,
                                            pooling='max',
                                            weights='imagenet',
                                            input_tensor=upscale,
                                            input_shape=(160,160,3),
                                            )

By setting include_top to False we exclude the fully connected layer at the top of the network. This layer was used to predict the Imagenet classes, but will be of no use for our Dollar Street dataset.

We add pooling='max' so that max pooling is applied to the output of the DenseNet121 network.

By setting weights='imagenet' we use the weights that resulted from training this network on the Imagenet data.

We connect the network to the upscale layer that we defined before.

Only train a ‘head’ network

Instead of fine-tuning all the weights of the DenseNet121 network using our dataset, we choose to freeze all these weights and only train a so-called ‘head network’ that sits on top of the pre-trained network. You can see the DenseNet121 network as extracting a meaningful feature representation from our image. The head network will then be trained to decide on which of the 10 Dollar Street dataset classes the image belongs.

We will turn of the trainable property of the base model:

PYTHON 

base_model.trainable = False

Let’s define our ‘head’ network:

PYTHON 

out = base_model.output
out = keras.layers.Flatten()(out)
out = keras.layers.BatchNormalization()(out)
out = keras.layers.Dense(50, activation='relu')(out)
out = keras.layers.Dropout(0.5)(out)
out = keras.layers.Dense(10)(out)

Finally we define our model:

PYTHON 

model = keras.models.Model(inputs=inputs, outputs=out)

Inspect the DenseNet121 network

Have a look at the network architecture with model.summary(). It is indeed a deep network, so expect a long summary!

1.Trainable parameters

How many parameters are there? How many of them are trainable?

Why is this and how does it effect the time it takes to train the model?

2. Head and base

Can you see in the model summary which part is the base network and which part is the head network?

3. Max pooling

Which layer is added because we provided pooling='max' as argument for DenseNet121()?

1. Trainable parameters

Total number of parameters: 7093360, out of which only 53808 are trainable.

The 53808 trainable parameters are the weights of the head network. All other parameters are ‘frozen’ because we set base_model.trainable=False. Because only a small proportion of the parameters have to be updated at each training step, this will greatly speed up training time.

2. Head and base

The head network starts at the flatten layer, 5 layers before the final layer.

3. Max pooling

The max_pool layer right before the flatten layer is added because we provided pooling='max'.

1. Compile the model

Compile the model: - Use the adam optimizer - Use the SparseCategoricalCrossentropy loss with from_logits=True. - Use ‘accuracy’ as a metric.

2. Train the model

Train the model on the training dataset: - Use a batch size of 32 - Train for 30 epochs, but use an earlystopper with a patience of 5 - Pass the validation dataset as validation data so we can monitor performance on the validation data during training - Store the result of training in a variable called history - Training can take a while, it is a much larger model than what we have seen so far.

3. Inspect the results

Plot the training history and evaluate the trained model. What do you think of the results?

4. (Optional) Try out other pre-trained neural networks

Train and evaluate another pre-trained model from https://keras.io/api/applications/. How does it compare to DenseNet121?

1. Compile the model

PYTHON 

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

2. Train the model

Define the early stopper:

PYTHON 

early_stopper = keras.callbacks.EarlyStopping(monitor='val_accuracy',
                              patience=5)

Train the model:

PYTHON 

history = model.fit(x=train_images,
                    y=train_labels,
                    batch_size=32,
                    epochs=30,
                    callbacks=[early_stopper],
                    validation_data=(val_images, val_labels))

3. Inspect the results

PYTHON 

def plot_history(history, metrics):
    """
    Plot the training history

    Args:
        history (keras History object that is returned by model.fit())
        metrics(str, list): Metric or a list of metrics to plot
    """
    history_df = pd.DataFrame.from_dict(history.history)
    sns.lineplot(data=history_df[metrics])
    plt.xlabel("epochs")
    plt.ylabel("metric")

plot_history(history, ['accuracy', 'val_accuracy'])

Training history for training the pre-trained-model. The training accuracy slowly raises from 0.2 to 0.9 in 20 epochs. The validation accuracy starts higher at 0.25, but reaches a plateau around 0.64 The final validation accuracy reaches 64%, this is a huge improvement over 30% accuracy we reached with the simple convolutional neural network that we build from scratch in the previous episode.

Concluding: The power of transfer learning

In many domains, large networks are available that have been trained on vast amounts of data, such as in computer vision and natural language processing. Using transfer learning, you can benefit from the knowledge that was captured from another machine learning task. In many fields, transfer learning will outperform models trained from scratch, especially if your dataset is small or of poor quality.

Key Points

  • Large pre-trained models capture generic knowledge about a domain
  • Use the keras.applications module to easily use pre-trained models for your own datasets