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Transfer Learning Using Pretrained ConvNets

View source on GitHub

Note: This is an archived TF1 notebook. These are configured to run in TF2's compatbility mode but will run in TF1 as well. To use TF1 in Colab, use the magic.

In this tutorial we will discuss how to classify cats vs dogs images by using transfer learning from a pre-trained network. This will allows us to get higher accuracies than we saw by training our network from scratch.

A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. We can either use the pretrained model as it is or transfer learning using the pretrained ConvNets. The intuition behind transfer learning is that if this model trained on a large and general enough dataset, this model will effectively serve as a generic model of the visual world. We can leverage these learned feature maps without having to train a large model on a large dataset by using these models as the basis of our own model specific to our task. There are 2 scenarios of transfer learning using a pretrained model:

Feature Extraction - use the representations of learned by a previous network to extract meaningful features from new samples. We simply add a new classifier, which will be trained from scratch, on top of the pretrained model so that we can repurpose the feature maps learned previously for our dataset. Do we use the entire pretrained model or just the convolutional base? - We use the feature extraction portion of these pretrained ConvNets (convolutional base) since they are likely to be generic features and learned concepts over a picture. However, the classification part of the pretrained model is often specific to original classification task, and subsequently specific to the set of classes on which the model was trained.
Fine-Tuning - unfreezing a few of the top layers of a frozen model base used for feature extraction, and jointly training both the newly added classifier layers as well as the last layers of the frozen model. This allows us to "fine tune" the higher order feature representations in addition to our final classifier in order to make them more relevant for the specific task involved.

We will follow the general machine learning workflow:

Examine and understand data
Build an input pipeline - using Keras ImageDataGenerator as we did in the image classification tutorial
Compose our model
- Load in our pretrained model (and pretrained weights)
- Stack our classification layers on top
Train our model
Evaluate model

We will see an example of using the pre-trained ConvNet as the feature extraction and then fine-tune to train the last few layers of the base model.

Audience: This post is geared towards beginners with some Keras API and ML background. To get the most out of this post, you should have some basic ML background, know what CNNs are, and be familiar with the Keras Sequential API.

Time Estimated: 30 minutes



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import os

import tensorflow.compat.v1 as tf

from tensorflow import keras
print("TensorFlow version is ", tf.__version__)

import numpy as np

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

Data preprocessing

Download data - cats_and_dogs_filtered.zip

We will download a filtered version of Kaggle's Dogs vs Cats dataset. Then store the downloaded zip file to the "/tmp/" directory.



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zip_file = tf.keras.utils.get_file(origin="https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip",
                                   fname="cats_and_dogs_filtered.zip", extract=True)
base_dir, _ = os.path.splitext(zip_file)

Prepare training and validation cats and dogs datasets

Create the training and validation directories for cats datasets and dog datasets.



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train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')

# Directory with our training cat pictures
train_cats_dir = os.path.join(train_dir, 'cats')
print ('Total training cat images:', len(os.listdir(train_cats_dir)))

# Directory with our training dog pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')
print ('Total training dog images:', len(os.listdir(train_dogs_dir)))

# Directory with our validation cat pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')
print ('Total validation cat images:', len(os.listdir(validation_cats_dir)))

# Directory with our validation dog pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
print ('Total validation dog images:', len(os.listdir(validation_dogs_dir)))

Create Image Data Generator with Image Augmentation

We will use ImageDataGenerator to rescale the images.

To create the train generator, specify where the train dataset directory, image size, batch size and binary classification mode.

The validation generator is created the same way.



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image_size = 160 # All images will be resized to 160x160
batch_size = 32

# Rescale all images by 1./255 and apply image augmentation
train_datagen = keras.preprocessing.image.ImageDataGenerator(
                rescale=1./255)

validation_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)

# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
                train_dir,  # Source directory for the training images
                target_size=(image_size, image_size),
                batch_size=batch_size,
                # Since we use binary_crossentropy loss, we need binary labels
                class_mode='binary')

# Flow validation images in batches of 20 using test_datagen generator
validation_generator = validation_datagen.flow_from_directory(
                validation_dir, # Source directory for the validation images
                target_size=(image_size, image_size),
                batch_size=batch_size,
                class_mode='binary')

Create the base model from the pre-trained ConvNets

We will create the base model from the MobileNet V2 model developed at Google, and pre-trained on the ImageNet dataset, a large dataset of 1.4M images and 1000 classes of web images. This is a powerful model. Let's see what the features that it has learned can do for our cat vs. dog problem.

First, we need to pick which intermediate layer of MobileNet V2 we will use for feature extraction. A common practice is to use the output of the very last layer before the flatten operation, the so-called "bottleneck layer". The reasoning here is that the following fully-connected layers will be too specialized to the task the network was trained on, and thus the features learned by these layers won't be very useful for a new task. The bottleneck features, however, retain much generality.

Let's instantiate an MobileNet V2 model pre-loaded with weights trained on ImageNet. By specifying the include_top=False argument, we load a network that doesn't include the classification layers at the top, which is ideal for feature extraction.



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IMG_SHAPE = (image_size, image_size, 3)

# Create the base model from the pre-trained model MobileNet V2
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
                                               include_top=False,
                                               weights='imagenet')

Feature extraction

We will freeze the convolutional base created from the previous step and use that as a feature extractor, add a classifier on top of it and train the top-level classifier.

Freeze the convolutional base

It's important to freeze the convolutional based before we compile and train the model. By freezing (or setting layer.trainable = False), we prevent the weights in these layers from being updated during training.



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base_model.trainable = False



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# Let's take a look at the base model architecture
base_model.summary()

Add a classification head

Now let's add a few layers on top of the base model:



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model = tf.keras.Sequential([
  base_model,
  keras.layers.GlobalAveragePooling2D(),
  keras.layers.Dense(1, activation='sigmoid')
])

Compile the model

You must compile the model before training it.



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model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
              loss='binary_crossentropy',
              metrics=['accuracy'])



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model.summary()

These 1.2K trainable parameters are divided among 2 TensorFlow Variable objects, the weights and biases of the two dense layers:



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len(model.trainable_variables)

Train the model

After training for 10 epochs, we are able to get ~94% accuracy.

If you have more time, train it to convergence (50 epochs, ~96% accuracy)



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epochs = 10
steps_per_epoch = train_generator.n // batch_size
validation_steps = validation_generator.n // batch_size

history = model.fit_generator(train_generator,
                              steps_per_epoch = steps_per_epoch,
                              epochs=epochs,
                              workers=4,
                              validation_data=validation_generator,
                              validation_steps=validation_steps)

Learning curves

Let's take a look at the learning curves of the training and validation accuracy / loss, when using the MobileNet V2 base model as a fixed feature extractor.

If you train to convergence (epochs=50) the resulting graph should look like this:



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acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.ylabel('Accuracy')
plt.ylim([min(plt.ylim()),1])
plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.ylabel('Cross Entropy')
plt.ylim([0,max(plt.ylim())])
plt.title('Training and Validation Loss')
plt.show()

Fine tuning

In our feature extraction experiment, we were only training a few layers on top of an MobileNet V2 base model. The weights of the pre-trained network were not updated during training. One way to increase performance even further is to "fine-tune" the weights of the top layers of the pre-trained model alongside the training of the top-level classifier. The training process will force the weights to be tuned from generic features maps to features associated specifically to our dataset.

Note: this should only be attempted after you have trained the top-level classifier with the pre-trained model set to non-trainable. If you add a randomly initialized classifier on top of a pre-trained model and attempt to train all layers jointly, the magnitude of the gradient updates will be too large (due to the random weights from the classifier) and your pre-trained model will just forget everything it has learned.

Additionally, the reasoning behind fine-tuning the top layers of the pre-trained model rather than all layers of the pre-trained model is the following: in a ConvNet, the higher up a layer is, the more specialized it is. The first few layers in a ConvNet learned very simple and generic features, which generalize to almost all types of images. But as you go higher up, the features are increasingly more specific to the dataset that the model was trained on. The goal of fine-tuning is to adapt these specialized features to work with the new dataset.

Un-freeze the top layers of the model

All we need to do is unfreeze the base_model, and set the bottom layers be un-trainable. Then, recompile the model (necessary for these changes to take effect), and resume training.



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base_model.trainable = True



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# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine tune from this layer onwards
fine_tune_at = 100

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
  layer.trainable =  False

Compile the model

Compile the model using a much-lower training rate.



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model.compile(optimizer = tf.keras.optimizers.RMSprop(lr=2e-5),
              loss='binary_crossentropy',
              metrics=['accuracy'])



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model.summary()



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len(model.trainable_variables)

Continue Train the model

If you trained to convergence earlier, this will get you a few percent more accuracy.



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history_fine = model.fit_generator(train_generator,
                                   steps_per_epoch = steps_per_epoch,
                                   epochs=epochs,
                                   workers=4,
                                   validation_data=validation_generator,
                                   validation_steps=validation_steps)

Learning curves

Let's take a look at the learning curves of the training and validation accuracy / loss, when fine tuning the last few layers of the MobileNet V2 base model, as well as the classifier on top of it. Note the validation loss much higher than the training loss which means there maybe some overfitting.

Note: the training dataset is fairly small, and is similar to the original datasets that MobileNet V2 was trained on, so fine-tuning may result in overfitting.

If you train to convergence (epochs=50) the resulting graph should look like this:



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acc += history_fine.history['accuracy']
val_acc += history_fine.history['val_accuracy']

loss += history_fine.history['loss']
val_loss += history_fine.history['val_loss']



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plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.ylim([0.9, 1])
plt.plot([epochs-1,epochs-1], plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.ylim([0, 0.2])
plt.plot([epochs-1,epochs-1], plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()



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acc



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loss

Key takeaways

In summary here is what we covered in this tutorial on how to do transfer learning using a pre-trained model to improve accuracy:

Using a pre-trained model for feature extraction - when working with a small dataset, it is common to leverage the features learned by a model trained on a larger dataset in the same domain. This is done by instantiating the pre-trained model and adding a fully connected classifier on top. The pre-trained model is "frozen" and only the weights of the classifier are updated during training. In this case, the convolutional base extracts all the features associated with each image and we train a classifier that determines, given these set of features to which class it belongs.
Fine-tuning a pre-trained model - to further improve performance, one might want to repurpose the top-level layers of the pre-trained models to the new dataset via fine-tuning. In this case, we tune our weights such that we learn highly specified and high level features specific to our dataset. This only make sense when the training dataset is large and very similar to the original dataset that the pre-trained model was trained on.