Principle:Tensorflow Tfjs Task Head Construction

Metadata

Field	Value
Principle Name	Tensorflow Tfjs Task Head Construction
Library	TensorFlow.js
Domains	Transfer_Learning, Neural_Networks
Type	Principle
Implemented By	Implementation:Tensorflow_Tfjs_Tf_Model_Functional
Source	TensorFlow.js
Last Updated	2026-02-10 00:00 GMT

Overview

Task Head Construction is the process of building a new, task-specific neural network component that is attached on top of a pretrained base model's feature representations. The task head maps the high-dimensional feature output from the base model to the target task's output space (e.g., class probabilities for classification, continuous values for regression). This is the component of the transfer learning model that is trained from scratch on the target dataset.

Description

In transfer learning, the pretrained base model serves as a feature extractor whose outputs are rich, high-level representations. The task head (also called the classification head, prediction head, or top layers) is a small neural network that takes these feature representations as input and produces the final task-specific output.

The task head is constructed using the Functional API, which allows building models with arbitrary graph topologies. Unlike the Sequential API (which only supports linear stacks of layers), the Functional API enables:

• Graph branching and merging -- Multiple inputs, multiple outputs, shared layers.
• Layer reuse -- Connecting pretrained layers to new layers in a single computation graph.
• Explicit tensor flow -- Each layer's apply() method takes a SymbolicTensor and returns a new SymbolicTensor, making the data flow explicit.

Typical Task Head Architecture

A standard task head for image classification consists of:

Layer	Purpose	Typical Configuration
Flatten	Converts multi-dimensional feature maps (e.g., [batch, 7, 7, 256]) to a 1D vector ([batch, 12544])	No parameters; pure reshape
Dense (hidden)	Learns task-specific nonlinear combinations of features	64-512 units, ReLU activation
Dropout	Regularization to prevent overfitting on small target datasets	Rate 0.2-0.5
Dense (output)	Maps to the target task's output space	n_classes units with softmax (classification) or 1 unit with linear (regression)

Alternative architectures may use GlobalAveragePooling2D instead of Flatten (reduces parameters and spatial information loss), multiple hidden Dense layers, or BatchNormalization between Dense layers.

Theoretical Basis

Why a Separate Task Head?

The pretrained model's original output layer is specific to the source task (e.g., 1,000 ImageNet classes). For a new task with a different number of classes or a different output type (regression vs. classification), this original output is inappropriate. The task head provides:

1. Dimensional adaptation -- The output dimensionality matches the target task (e.g., 5 classes instead of 1,000).
2. Activation adaptation -- The output activation matches the task type (softmax for multi-class, sigmoid for binary, linear for regression).
3. Feature combination -- Hidden layers in the task head learn to combine the base model's features in ways that are optimal for the new task.

The Functional API Paradigm

The Functional API constructs models by chaining layer apply() calls on SymbolicTensors:

1. Start with the base model's input SymbolicTensor (baseModel.input).
2. The feature extraction layer's output SymbolicTensor (featureLayer.output) serves as the connection point.
3. New layers are applied sequentially: layer.apply(previousOutput) returns a new SymbolicTensor.
4. The final model is created with tf.model({inputs, outputs}), specifying the start and end of the computation graph.

This paradigm enables the construction of models that span both the pretrained base and the new task head as a single, unified computation graph.

Regularization in the Task Head

Since the task head is trained from scratch on a typically small target dataset, regularization is essential:

• Dropout randomly zeroes a fraction of activations during training, preventing co-adaptation of neurons.
• L2 regularization (weight decay) on Dense layers penalizes large weights.
• Small hidden layer sizes limit the model's capacity, reducing overfitting risk.

Usage

Task head construction is the central architectural step in transfer learning:

• Image classification -- Flatten + Dense + Softmax for classifying images into target categories.
• Object detection -- Multiple heads for bounding box regression and class prediction.
• Feature embedding -- Dense layers reducing feature dimensionality for similarity search.
• Multi-task learning -- Multiple heads branching from the same feature extractor.

Design Guidelines

1. Keep it simple -- Start with a minimal task head (Flatten + Dense) and add complexity only if needed.
2. Match the output to the task -- Use softmax for multi-class classification, sigmoid for multi-label, linear for regression.
3. Use dropout -- Especially important when the target dataset is small.
4. Consider GlobalAveragePooling -- For spatial feature maps, GlobalAveragePooling2D is often preferable to Flatten as it reduces parameters and is more robust to spatial translations.

Related Pages

• Principle:Tensorflow_Tfjs_Feature_Extraction_Layer_Selection -- Selecting the layer whose output feeds into the task head
• Principle:Tensorflow_Tfjs_Layer_Freezing -- Freezing the base layers while the task head trains
• Principle:Tensorflow_Tfjs_Fine_Tuning -- Training the complete transfer learning model
• Implementation:Tensorflow_Tfjs_Tf_Model_Functional -- TensorFlow.js implementation of task head construction