Intro to Categorical Deep Learning

What Category Theory Sees in a Network

A neural network is specified twice over. The first specification is the code, where the network is a function with a great many numbers attached to it. The second is the set of sentences we use to say what the code is doing: this layer shares its weights with that one, the optimizer updates the parameters, the backward pass reuses what the forward pass computed. That second description is the one that carries the design decisions, and it is the one with no settled mathematical notation. Categorical deep learning (CDL) is an attempt to give it one.

The vocabulary CDL uses is already familiar from the page on categories, functors, and naturality: objects, morphisms, composition, and the two axioms composition is required to satisfy. What CDL asks is what those axioms say when the morphisms are the layers of a network. The question is a semantic one rather than an empirical one. It is not whether describing a network categorically makes it train faster, but what a network is when described compositionally, and the answer is not settled by measuring anything. The page on geometric deep learning asks something different. There a symmetry group is fixed in advance and the question is which architectures respect it. Here nothing is fixed except the demand that the description compose.

Part of the answer is established. A compositional account presented at a peer-reviewed venue in 2019 organizes supervised learning systems into a category whose morphisms carry a parameter space, an implementation, an update rule, and a request function that passes training data backward to the preceding morphism. In that account, once a step size and an error function satisfying a condition on its derivative are fixed, gradient descent determines a functor from parameterized differentiable maps between Euclidean spaces into that category [1]. A paper from 2022 rebuilds the same material on three ingredients instead. Computation is parameterized, information flows in both directions, and the update step is obtained by differentiation. From these the paper recovers several standard optimizers and loss functions uniformly inside the resulting framework [2]. A peer-reviewed survey from 2025 maps the area these and their successors have produced [3].

Part of it is not. A position paper accepted at a peer-reviewed venue in 2024 proposes that the architectures in use at the time can be presented, essentially without exception, as instances of a single algebraic pattern imposed on parameterized maps [4]. The proposal is labeled a position paper by its authors and by the venue that accepted it, and the claim about all architectures is a research program, not a theorem. The distinction matters for reading the field. What has been proved is that certain constructions compose and that certain familiar algorithms arise from them, and what has been proposed is that one such algebraic pattern exhausts the design space. No claim about deployed systems is attached to either, and none is made here.

This page takes one small part of that program, a part that can be made fully precise with the tools already available on this site, and follows it to its conclusion. We define what a layer is once its parameters are treated as part of the layer rather than as an extra argument, then compose two such layers and watch the parameters accumulate. From there we observe that the composition we have defined does not satisfy the category axioms on the nose, and identify the second kind of morphism that this failure points at. Two operations practitioners already perform, tying weights together and updating them, turn out to live in that second kind. The ambient setting is fixed throughout to sets and functions.

A Layer Is Not a Map From Inputs to Outputs

The distinction we need is already written down. The definition of a multilayer perceptron records the network as \(f(\mathbf{x}; \boldsymbol{\theta})\), with a semicolon between the two arguments. Writing the comma form \(f(\mathbf{x}, \boldsymbol{\theta})\) instead is the rarer choice, and the reason is not typographical. The semicolon marks the input as the thing the network is applied to and the weights as the thing the network is, and the subscript form \(f_{\boldsymbol{\theta}}(\mathbf{x})\) makes the same choice more emphatically. What the semicolon does not have is a meaning. It is a piece of notation with no corresponding piece of structure.

The difference between the two arguments is visible in what we do to them. An input is supplied and discarded. A weight is initialized, updated at every step, sometimes constrained to equal another weight elsewhere in the network, sometimes frozen while its neighbors continue to move. None of these are operations on inputs. They are operations on the second argument alone, and a description of a network which treats both arguments alike cannot express any of them.

Care is needed in saying what fails here, because a layer is perfectly well a function. Fix the weights and it is a function from inputs to outputs. Leave them free and it is a function of the pair. What a layer is not is a morphism from the object of inputs to the object of outputs, in the sense the page on categories fixes that word. A morphism \(A \to B\) has nothing attached to it. If we want the weights to survive the translation, they have to be part of the arrow.

Convention. We write the parameter on the right, so that a layer from \(A\) to \(B\) is implemented by a map out of \(A \times P\), with the ordinary input first and the weights second. Here \(A\), \(B\) and \(P\) are sets, \(A \times P\) is the set of pairs, and products of sets are the only construction used anywhere on this page.

For the \(\ell\)-th layer of a multilayer perceptron the definition reads \[ \begin{align*} A &= \mathbb{R}^{d_{\ell-1}}, \\\\ B &= \mathbb{R}^{d_\ell}, \\\\ P_\ell &= \mathbb{R}^{d_\ell \times d_{\ell-1}} \times \mathbb{R}^{d_\ell}, \\\\ f_\ell\!\left(\mathbf{z}, \left(W^{(\ell)}, \mathbf{b}^{(\ell)}\right)\right) &= g_\ell\!\left(W^{(\ell)} \mathbf{z} + \mathbf{b}^{(\ell)}\right), \end{align*} \] where the elements of the parameter object are exactly the pairs consisting of a weight matrix and a bias vector. Nothing has been added. Every symbol on the right already appears in the definition the semicolon came from, and \(f_\ell\), which that definition writes as a map of \(\mathbf{z}\) alone with the weights implicit, now takes them as its second argument. What has changed is that the weights sit inside the layer's data rather than beside it.

The clause about equality is where the definition earns its keep, and it is stricter than it looks. A layer is not determined by the functions it can compute. Take the layer above and replace its bias vector by a pair of vectors which are always added together. The parameter object has changed, the collection of functions \(f(-, p)\) available to the network has not, and the two are different parameterized layers. This is the ordinary situation of a model with redundant parameters, and the definition declares it a difference rather than an artifact. Training operates on the parameter object, not on the collection of functions it can compute, so a notion of layer which identified these two would be blind to the thing we spend the compute on.

Stacking Accumulates Parameters

Layers are stacked, so the definition has to say how. Given a parameterized layer \((P, f)\) from \(A\) to \(B\) and a parameterized layer \((Q, g)\) from \(B\) to \(C\), there is one evident way to run them in order. We feed the input and the first set of weights to \(f\), then feed the result and the second set of weights to \(g\). The weights of the two layers are both needed, and neither is consumed by the other, so the composite carries both of them. It is the parameterized layer \(\bigl(P \times Q,\ h\bigr)\) from \(A\) to \(C\) whose implementation is \[ h\bigl(a, (p, q)\bigr) = g\bigl(f(a, p),\, q\bigr). \] In words, layers compose in sequence, and their parameter objects compose in parallel. Depth accumulates on one axis, weights on the other.

Now stack three. Let \((S, k)\) run from \(C\) to \(D\), and compose the three layers in the two possible orders of bracketing. Composing the first two and then the third gives a parameter object \((P \times Q) \times S\). Composing the first with the composite of the last two gives \(P \times (Q \times S)\). The implementations are \[ \begin{align*} h_{L}\bigl(a, ((p, q), s)\bigr) &= k\bigl(g(f(a, p), q),\, s\bigr), \\\\ h_{R}\bigl(a, (p, (q, s))\bigr) &= k\bigl(g(f(a, p), q),\, s\bigr). \end{align*} \] The right-hand sides are the same expression. The two composites compute the same output from the same weights, and they are still not equal as parameterized layers, because their parameter objects are different sets. One consists of pairs whose first entry is a pair, the other of pairs whose second entry is a pair. In code the difference is the shape of the tuple the optimizer is handed, and it is a real difference there too.

The identity behaves no better. A layer that does nothing to its input still has to have a parameter object, and the only parameter object that adds nothing beyond packaging is a one-element set \(1 = \{*\}\), with implementation \((a, *) \mapsto a\). Composing any layer \((P, f)\) with it, identity last, produces the parameter object \(P \times 1\), whose elements are the pairs \((p, *)\). Identity first gives \(1 \times P\) instead. Again this is not \(P\), but a set that looks like \(P\) with a piece of packaging around it.

So the composition we have just defined satisfies neither of the two axioms that the page on categories requires of a composition. Both failures are of the same kind. In each case the two sides are isomorphic rather than equal, by a bijection which is entirely canonical, namely rebracketing the tuple or deleting the \(*\). Nothing is lost under either bijection and no choice is involved in making it.

That observation is not yet a repair, and the reason is worth stating precisely. A bijection \((P \times Q) \times S \to P \times (Q \times S)\) is a map between parameter objects, and the axioms it would have to rescue are statements about layers. To use it we would need to know what a map between two parameterized layers is, and so far we have not said. Layers have been compared only by equality, and equality is exactly what is failing. The structure the situation is asking for is a second kind of morphism, one that relates two layers with different parameter objects, and it turns out that deep learning has been using it all along.

A Second Kind of Morphism

Suppose we want to describe the same computation with a different set of weights. To do that we have to say, for each element of the new parameter object, which element of the old one it stands for. That is a map \(r : P' \to P\), and once it is given, the new layer has no freedom left. Its implementation must be the old one, run on the weights that \(r\) produces. Notice which way the map points. The layer we are moving towards has the parameter object in the domain, because what a redescription of the weights has to supply is a recipe for recovering the old weights from the new ones, not the other way round.

The definition asks nothing of \(r\) beyond that one equation. It need not be injective, surjective, or invertible, and the two layers it relates need not compute the same collection of functions. The word is also used for something else in generative modeling. The reparameterization trick rewrites a sample so that its randomness enters as an independent input, and is a statement about distributions. What is defined here relates parameter objects, and no probability enters into it.

Return to the two failures of the previous section with this in hand. The canonical bijection \(\rho : P \times (Q \times S) \to (P \times Q) \times S\) that rebrackets a tuple satisfies \[ h_{R} = h_{L} \circ (1_A \times \rho), \] which is exactly the condition above. So \(\rho\) is a reparameterization from \(\bigl((P \times Q) \times S,\, h_{L}\bigr)\) to \(\bigl(P \times (Q \times S),\, h_{R}\bigr)\), and since \(\rho\) is a bijection the reparameterization runs both ways.

The same holds for the unit. Write \(f_1\) for the implementation of \((P, f)\) composed with the identity layer, so that \[ f_1\bigl(a, (p, *)\bigr) = f(a, p). \] The map \(p \mapsto (p, *)\) is a reparameterization from \((P \times 1,\, f_1)\) to \((P, f)\), and it too runs both ways. The axioms were not failing because the composition was wrong. They were failing because equality was the wrong relation to test them with, and the right relation lives one level up: not an equality of layers but a morphism between them.

The second kind of morphism is not an artifact of bookkeeping either, and the plainest evidence is an operation practitioners perform. Take two layers that are to share their weights: \(f : A \times P \to B\) and \(g : B \times P \to C\), with the same \(P\), tied so that both use the same values. Composing them as parameterized layers produces the parameter object \(P \times P\) with implementation \[ h\bigl(a, (p, p')\bigr) = g\bigl(f(a, p),\, p'\bigr), \] which is precisely what tying is meant to prevent.

The tied layer is instead \((P, h')\), and the two are related by the diagonal \(\Delta_P : P \to P \times P\), \(p \mapsto (p, p)\): \[ \begin{align*} h'(a, p) &= g\bigl(f(a, p),\, p\bigr) \\\\ &= h\bigl(a, \Delta_P(p)\bigr), \\\\ h' &= h \circ (1_A \times \Delta_P). \end{align*} \] So weight sharing is a reparameterization, and a non-invertible one whenever \(P\) has more than one element. In code it is the line where the same parameter object is handed to two layers instead of two separate ones being allocated, which is exactly a map \(P \to P \times P\) written in another language.

The diagonal is the only construction on this page that needs more than the bare set of pairs, and it needs very little. In sets it is the map \(p \mapsto (p, p)\). In any category with finite products it is the unique map into \(P \times P\) whose composites with the two projections are both the identity on \(P\), supplied by the universal property and by nothing else. Every argument above therefore runs in any such category once the pointwise formulas are read as composites of projections and pairings, which is why fixing sets as the ambient setting cost us nothing within the reach of finite products.

That requirement is modest. The page on adjunctions and universal properties singled out the cartesian closed categories, those with finite products in which every exponential object exists, as the reference point against which categorical accounts of learning systems would be positioned. One might therefore expect such an account to start there. Nothing on this page has used an exponential, or currying, or any structure beyond finite products. Layers, their composition, their failure to be a category on the nose, and the repair of that failure are all supported by products alone.

The figure below draws the two directions in play, with the weight-sharing example as the specimen.

Why the Second Dimension Is Kept

Nothing forces us to keep the second kind of morphism. If what we want is a category in the ordinary sense, we can have one, and there are two obvious ways to arrange it. We can throw the reparameterizations away and go back to comparing layers by equality alone, or we can go the other way and declare two layers the same whenever a reparameterization connects them. Both produce something simpler. The cost of each is worth seeing, because in both cases what is lost is an operation that training runs perform.

Take the first. Discard the reparameterizations, and the only relation left between two layers is equality. Now consider what a single step of training does, a step of gradient descent for instance. Once the batch is fixed, the step takes the weights of a layer and moves them, which is a map \(u : P \to P\), and it leaves us holding the layer \(\bigl(P,\, f \circ (1_A \times u)\bigr)\). That is not the layer we started with, since its implementation is different, and under the definition it is a different layer. What relates the two is \(u\) itself, which satisfies the reparameterization condition by construction. Discard that relation and a training run becomes a sequence of unrelated layers. The update step is not merely hard to express. There is nothing left for it to be.

Take the second. Identify any two layers joined by a reparameterization, together with everything that identification forces, and the diagonal from the previous section merges \((P \times P,\, h)\) with \((P,\, h')\). Those are the untied and the tied network. Under this identification a network whose two layers share their weights and a network whose two layers have independent weights become the same object, though one has half as many parameters as the other. Weight sharing is not a small feature of the field. It is part of what makes a convolutional layer convolutional and what a recurrent layer does by definition, and the collapse removes the distinction it consists of.

So the structure resists flattening in both directions, and it resists it for the same reason each time. There are two independent things one can do to a network. Layers can be stacked, and weights can be moved or tied. Neither is a variation of the other. The stacking runs along one axis, the reparameterizations run across it, and every construction on this page has had this shape: the composition accumulating parameter objects along the first axis, the diagonal and the rebracketings acting along the second. A description with only one axis has to give up one of the two, and the field uses both.

Keeping both axes is what the categorical account is for. The account does not replace the network with something more abstract, and it does not propose a new architecture. What it does is give a name to a distinction that every framework already implements and the usual notation does not record. Once the distinction has a name, the operations that depend on it can be stated rather than described. The general form of the construction is developed at length in the literature [5]. There it is carried out over ambient settings well beyond sets, and in considerably more depth than a first pass requires.

References

• Fong, Spivak, and Tuyéras, "Backprop as Functor: A Compositional Perspective on Supervised Learning," Proceedings of the 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 2019.
• Cruttwell, Gavranović, Ghani, Wilson, and Zanasi, "Categorical Foundations of Gradient-Based Learning," Programming Languages and Systems (ESOP 2022), Lecture Notes in Computer Science, vol. 13240, pp. 1-28, 2022.
• Jia, Peng, Yang, and Chen, "Category-Theoretical and Topos-Theoretical Frameworks in Machine Learning: A Survey," Axioms, vol. 14, no. 3, article 204, 2025.
• Gavranović, Lessard, Dudzik, von Glehn, Araújo, and Veličković, "Position: Categorical Deep Learning is an Algebraic Theory of All Architectures," Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR vol. 235, pp. 15209-15241, 2024.
• Gavranović, "Fundamental Components of Deep Learning: A Category-Theoretic Approach," PhD thesis, University of Strathclyde, 2024, arXiv:2403.13001.