## Code Snippets: DomiKnowS, DeepProbLog, and Scallop
### Overview
The image presents code snippets from three different systems: DomiKnowS, DeepProbLog, and Scallop. Each snippet defines classes and their initialization, showcasing how neural networks are implemented and integrated within these systems.
### Components/Axes
* **Titles:** DomiKnowS (top-left), DeepProbLog (top-right), Scallop (bottom-center)
* **Code Snippets:** Each section contains Python-like code defining classes and their attributes.
### Detailed Analysis or ### Content Details
**1. DomiKnowS (Top-Left, Light Gray Background)**
* **Class Definition:** `class Net:`
* **Comment:** `# neural network`
* **Image Processing:**
* `image['pixels'] = ReaderSensor(keyword='pixels')`
* `image_batch['pixels', image_contains.reversed] = JointSensor(image['pixels'], forward=make_batch)`
* `image['logits'] = ModuleLearner('pixels', module=Net())`
* **Ellipsis:** `...` (indicating more code not shown)
**2. DeepProbLog (Top-Right, Light Purple Background)**
* **Class Definition:** `class MNIST_Net:`
* **Comment:** `# neural network`
* **Network Initialization:**
* `network = MNIST_Net()`
* `net = Network(network, "mnist_net", batching=True)`
* `net.optimizer = torch.optim.Adam(network.parameters(), lr=1e-3)`
* `model = Model("models/addition.pl", [net])`
* **Ellipsis:** `...` (indicating more code not shown)
**3. Scallop (Bottom-Center, Light Green Background)**
* **Class Definition 1:** `class MNISTSum2Net(nn.Module):`
* `def __init__(self, provenance, k):`
* `self.mnist_net = MNISTNet()`
* `# neural network`
* `self.scl_ctx = scallopy.ScallopContext(provenance=provenance, k=k)`
* **Class Definition 2:** `class Trainer():`
* `def __init__(self, train_loader, test_loader, model_dir, learning_rate, loss, k, provenance):`
* `self.model_dir = model_dir`
* `self.network = MNISTSum2Net(provenance, k)`
* `self.optimizer = optim.Adam(self.network.parameters(), lr=learning_rate)`
* **Ellipsis:** `...` (indicating more code not shown)
### Key Observations
* All three systems involve neural networks.
* DomiKnowS focuses on image processing using `ReaderSensor`, `JointSensor`, and `ModuleLearner`.
* DeepProbLog uses `MNIST_Net` and `Network` classes, with an Adam optimizer and a learning rate of `1e-3`.
* Scallop integrates with `scallopy.ScallopContext` and defines a `Trainer` class with configurable learning rates.
### Interpretation
The code snippets illustrate different approaches to integrating neural networks within various systems. DomiKnowS appears to be geared towards image-based tasks, DeepProbLog focuses on probabilistic logic programming with neural networks, and Scallop seems to provide a framework for training and managing neural network models with provenance tracking. The use of common libraries like `torch.optim.Adam` suggests a shared foundation in deep learning practices.
