Deep Learning Tutorial 27: Transfer Learning with Tensorflow, Keras & Python

In this tutorial, we will be discussing Transfer Learning in deep learning using TensorFlow, Keras, and Python. Transfer learning is a technique in machine learning where a model trained on one task is re-used as a starting point for a model on a different task. This is particularly useful when you have a small dataset for your target task, as it allows you to leverage the knowledge learned from a larger dataset on a related task.

Transfer Learning can drastically reduce the amount of time and data needed to train a model, as it allows you to take advantage of pre-trained models that have already been trained on massive datasets like ImageNet.

We will be using Keras, which is a high-level neural networks API that runs on top of TensorFlow, a popular deep learning framework developed by Google. We will also be using Python programming language for coding.

Here are the steps we will be following in this tutorial:

1. Importing necessary libraries:

   • We will start by importing the necessary libraries like TensorFlow, Keras, and numpy.

2. Loading and preprocessing the data:

   • Next, we will load the dataset that we will be working with. For this tutorial, we will be using the CIFAR-10 dataset, which contains 60,000 32×32 color images in 10 different classes.
   • We will preprocess the images by normalizing the pixel values and resizing the images to the required input shape of the pre-trained model.

3. Creating the base model:

   • We will choose a pre-trained model as our base model. In this tutorial, we will be using the VGG16 model, which has been pre-trained on the ImageNet dataset.
   • We will load the model using the keras.applications module, freeze the layers of the pre-trained model, and add a few additional layers on top of it.

4. Fine-tuning the model:

   • We will train the model on the CIFAR-10 dataset using transfer learning. We will unfreeze some of the layers in the pre-trained model and fine-tune them along with the additional layers that we added.
5. Evaluating the model:
   • Finally, we will evaluate the performance of the model on the test dataset and visualize the results using confusion matrix and classification report.

Now, let’s get started with the code:

# Step 1: Importing necessary libraries
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

# Step 2: Loading and preprocessing the data
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Step 3: Creating the base model
base_model = keras.applications.VGG16(input_shape=(32, 32, 3), include_top=False, weights='imagenet')
base_model.trainable = False

model = keras.Sequential([
    base_model,
    layers.GlobalAveragePooling2D(),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Step 4: Fine-tuning the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))

# Step 5: Evaluating the model
model.evaluate(x_test, y_test)

In this code snippet, we first import the necessary libraries and load the CIFAR-10 dataset. We then create the base model using the VGG16 pre-trained model and add additional layers on top of it. We freeze the layers of the base model and train the model on the CIFAR-10 dataset. Finally, we evaluate the model on the test dataset.

Transfer learning is a powerful technique in deep learning that can help you build accurate and reliable models even with limited data. By leveraging pre-trained models and fine-tuning them on your dataset, you can achieve state-of-the-art results with minimal effort. Practice this tutorial and experiment with different pre-trained models and datasets to further enhance your understanding of transfer learning.