Using Transfer Learning in Keras and Tensorflow

Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. This technique is particularly useful when you have limited data for your specific task, as you can leverage the pre-trained model’s knowledge to improve performance.

In this tutorial, we will cover how to perform transfer learning using Keras and Tensorflow. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. TensorFlow is an open-source machine learning library developed by Google.

Step 1: Install Keras and TensorFlow

Before you can start working with Keras and TensorFlow, you need to install them. You can install them using pip by running the following commands:

pip install keras
pip install tensorflow

Step 2: Load the Pre-trained Model

In transfer learning, we often use pre-trained models that have been trained on large datasets, such as ImageNet. These pre-trained models have already learned to recognize a wide variety of features in images and can be fine-tuned for specific tasks.

Keras provides a range of pre-trained models that can be easily loaded using just a few lines of code. For this tutorial, we will use the VGG16 model, which is a deep convolutional neural network with 16 layers.

from keras.applications import VGG16

base_model = VGG16(weights='imagenet', include_top=False)

In this code snippet, we load the VGG16 model with pre-trained weights from the ImageNet dataset, but exclude the top layer (the fully connected layers). This allows us to use the features learned by the VGG16 model without the final classification layers.

Step 3: Add Additional Layers

To adapt the pre-trained model to our specific task, we need to add additional layers on top of the pre-trained model. These additional layers will be trained on our specific data. In this tutorial, we will be using a simple example of adding a few fully connected layers on top of the VGG16 model.

from keras.models import Model
from keras.layers import Flatten, Dense

x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(2, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

In this code snippet, we add a Flatten layer to convert the 3D features from the pre-trained model into 1D features, followed by a Dense layer with 1024 units and a ReLU activation function. Finally, we add a Dense layer with 2 units (assuming a binary classification task) and a softmax activation function.

We also set all the layers in the pre-trained model to be non-trainable, so only the layers we added on top will be trained on our specific task.

Step 4: Compile and Train the Model

Now that we have our model architecture defined, we need to compile and train the model on our specific dataset. You can compile the model using the following code:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Next, you can train the model on your dataset using the fit method:

model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(val_data, val_labels))

In this code snippet, train_data and train_labels are your training data and labels, val_data and val_labels are your validation data and labels, and epochs and batch_size specify the number of training epochs and batch size, respectively.

Step 5: Fine-tune the Model (Optional)

If you have a larger dataset or your specific task is significantly different from the pre-trained task, you may want to fine-tune the pre-trained model by unfreezing some of the layers and re-training them on your dataset.

To fine-tune the model, you can set some of the layers in the pre-trained model to be trainable and recompile the model:

for layer in model.layers[:15]:
    layer.trainable = True

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

You can then continue training the model on your dataset to fine-tune the model on your specific task.

Conclusion

Transfer learning is a powerful technique that can help improve the performance of deep learning models, especially when you have limited data for your specific task. In this tutorial, we covered how to perform transfer learning using Keras and TensorFlow, including loading a pre-trained model, adding additional layers, compiling and training the model, and fine-tuning the model if necessary. By following these steps, you can quickly apply transfer learning to your own deep learning projects.