How deeply does Deep Learning with R, Third Edition cover classification head?

Explains contextual pooling, Dense projection, dropout, sigmoid output, loss selection, and training, while deferring broader architecture theory.

Classification head

As explained in Deep Learning with R, Third Edition

A classification layer is a neural-network layer that produces outputs representing predicted class assignments or probabilities.

A classification head is the task-specific part of a neural network that turns learned representations into class predictions. Attached to a general-purpose backbone, it specializes the model for a classification task, much like choosing a screwdriver head for a particular job. section 15.4.2

A practical classification head commonly performs these steps:

Condenses a sequence representation into one vector per sample, using the first token representation rather than mean- or max-pooling. section 15.4.4
Transforms that vector with a Dense projection, a ReLU nonlinearity, and dropout before producing the prediction. section 15.4.4
Uses a final one-unit sigmoid output with binary crossentropy and accuracy for binary classification. section 15.4.4

Listing 15.22 Extending the base RoBERTa model for classification

inputs <- backbone$input
outputs <- inputs |>
  backbone() |>
  op_subset(, 1, ) |>  #1
  layer_dropout(0.1) |>
  layer_dense(768, activation = "relu") |>
  layer_dropout(0.1) |>
  layer_dense(1, activation = "sigmoid")

classifier <- keras_model(inputs, outputs)

The Keras model definition shows a RoBERTa backbone extended with contextual representation selection, dropout, Dense layers, and a sigmoid classification output. section 15.4.4

IMDb review classification

A pretrained RoBERTa backbone can be extended with this head and fine-tuned on IMDb reviews. The resulting model reached 93% accuracy after one epoch, improving on a simpler classifier’s 90% ceiling. section 15.4.4

How Deep Learning with R, Third Edition covers it

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Deep Learning with Python, Third EditionThe final neural-network layer that maps learned representations to class predictions.

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Related concepts

foundational modelpart of · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
neural network layerkind of · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
fine-tuningimplemented by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
softmaxconfigured by · Softmax is a function that exponentiates a vector of scores and normalizes the results into nonnegative values that sum to one.
linear layerhas part · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.