---

name: neural-network-building-blocks description: "Use this sub-skill for MLAlgorithms NeuralNet construction, activations, losses, parameters, constraints, regularizers, optimizers, convolutional/recurrent layers, and DQN wiring." disable-model-invocation: true metadata: disco-role: operating license: MIT

Neural Network Building Blocks

Use this sub-skill when a task asks for the custom MLAlgorithms neural-network stack: how to build a NeuralNet, choose layers or optimizers, debug training phases and shapes, or adapt the repo's MLP/CNN/RNN/LSTM/DQN examples into shorter checks. The implementation is educational and CPU-only, with no external training service or model hub dependency.

Route elsewhere

• Tabular supervised estimators, SVM kernels, ensembles, KNN, Naive Bayes, and factorization machines belong in ../classical-estimators/SKILL.md.
• KMeans, Gaussian mixtures, PCA, t-SNE, RBM, and demo dataset loaders belong in ../unsupervised-and-reduction/SKILL.md.
• Cross-cutting install, provenance, and route selection remain in the root skill.

Start here

1. Confirm the package imports the neural stack you need:

   from mla.neuralnet import NeuralNet
   from mla.neuralnet.layers import Dense, Activation, Dropout
   from mla.neuralnet.optimizers import Adam
   

2. Choose the architecture family:

   • Simple dense model: Dense + Activation.
   • CNN: Convolution, MaxPooling, Flatten, Dense.
   • Sequence model: RNN or LSTM, optionally followed by TimeDistributedDense or TimeStepSlicer.
   • Reinforcement learning wrapper: mla.rl.dqn.DQN with a user-provided neural model factory.

3. Use explicit one-hot labels for categorical_crossentropy, and remember that NeuralNet uses a separate optimizer.setup(self) step when fit starts.

4. Run scripts/run_neural_smoke.py --workflow all for a short no-display check.

Primary workflows

Build a dense network

NeuralNet takes a list of layers plus an optimizer and a loss name. The container sets up layers, performs forward/backward passes, and delegates parameter updates to the optimizer.

from mla.neuralnet import NeuralNet
from mla.neuralnet.layers import Dense, Activation
from mla.neuralnet.optimizers import Adam

model = NeuralNet(
    layers=[Dense(16), Activation("relu"), Dense(1)],
    optimizer=Adam(),
    loss="mse",
    batch_size=32,
    max_epochs=5,
    metric="mse",
)

Convolutional workflow

Use Convolution, MaxPooling, Flatten, and Dense for the repo's MNIST-style example. The ConvNet expects image tensors shaped like (n_samples, n_channels, height, width).

Recurrent workflow

Use RNN or LSTM for sequence problems. The classes accept return_sequences=True by default, which is helpful for per-time-step outputs. TimeDistributedDense applies a dense layer to each time step.

DQN wiring

DQN is a lightweight training loop that asks the user to supply a model factory. It is designed around legacy Gym-style reset()/step() interactions and should be treated as an integration recipe rather than a production RL stack.

Bundled references and helpers

• Read references/api-reference.md for exact constructor signatures and output/shape contracts.
• Read references/workflows.md for dense/CNN/RNN/DQN recipes and validation advice.
• Read references/neural-workflows.md for longer CNN and sequence reference workflows.
• Read references/rl-dqn.md before touching CartPole or other Gym environments.
• Read references/troubleshooting.md when training stalls, shapes break, or Gym/NumPy compatibility drifts.
• Run scripts/run_neural_smoke.py --workflow mlp or --workflow all for a small installed-package check.