DocsFirst Model

Your First ML Model

Build your first machine learning model with CyxWiz using the visual Node Editor or Python scripting.

Option 1: Using the Node Editor

Launch CyxWiz Engine
Create New Project: File > New Project
Open Node Editor: View > Node Editor
Build a Simple Network:

[Data Input] -> [Dense 784->128 ReLU] -> [Dense 128->10 Softmax] -> [Model Output]

Adding Nodes

Right-click canvas > Add Node > Data > DataInput
Right-click canvas > Add Node > Layers > Dense
Connect nodes by dragging from output pins to input pins

Configuring Dense Layer

Select the Dense node
In Properties panel: Set Units=128, Activation=ReLU

Generate Code

Edit > Generate Code > PyTorch

Option 2: Using Python Scripting

Open the Console panel (View > Console) and try:

import cyxwiz as cyx

# Create a simple model
model = cyx.Sequential([
    cyx.layers.Dense(128, activation='relu', input_shape=(784,)),
    cyx.layers.Dropout(0.2),
    cyx.layers.Dense(64, activation='relu'),
    cyx.layers.Dense(10, activation='softmax')
])

# Print summary
model.summary()

# Compile
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Load sample data
(X_train, y_train), (X_test, y_test) = cyx.datasets.mnist.load_data()

# Train
history = model.fit(X_train, y_train,
                    epochs=5,
                    batch_size=32,
                    validation_split=0.2)

# Evaluate
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test accuracy: {accuracy:.4f}")

Project Structure

After creating a project, you'll have:

MyProject/
├── models/              # .cyxgraph files (node editor graphs)
├── scripts/             # Python scripts
├── data/                # Datasets
├── outputs/
│   ├── checkpoints/     # Model checkpoints
│   └── logs/            # Training logs
└── project.json         # Project configuration

Loading Data

From File

# CSV
data = cyx.datasets.load_csv('data/train.csv')

# Images from folder
train_data = cyx.datasets.ImageFolder(
    'data/images',
    transform=cyx.transforms.Compose([
        cyx.transforms.Resize(224),
        cyx.transforms.ToTensor(),
        cyx.transforms.Normalize(
            [0.485, 0.456, 0.406],
            [0.229, 0.224, 0.225]
        )
    ])
)

Built-in Datasets

# MNIST
(X_train, y_train), (X_test, y_test) = \
    cyx.datasets.mnist.load_data()

# CIFAR-10
(X_train, y_train), (X_test, y_test) = \
    cyx.datasets.cifar10.load_data()

# Fashion-MNIST
(X_train, y_train), (X_test, y_test) = \
    cyx.datasets.fashion_mnist.load_data()

Training a Model

# Configure training
history = model.fit(
    X_train, y_train,
    epochs=10,
    batch_size=32,
    validation_data=(X_val, y_val),
    callbacks=[
        cyx.callbacks.EarlyStopping(patience=3),
        cyx.callbacks.ModelCheckpoint(
            'best_model.h5',
            save_best_only=True
        ),
        cyx.callbacks.TensorBoard(log_dir='./logs')
    ]
)

# Plot training history
cyx.plot.training_history(history)

Common Architectures

CNN for Image Classification

model = cyx.Sequential([
    cyx.layers.Conv2D(32, (3, 3),
        activation='relu',
        input_shape=(28, 28, 1)),
    cyx.layers.MaxPool2D((2, 2)),
    cyx.layers.Conv2D(64, (3, 3),
        activation='relu'),
    cyx.layers.MaxPool2D((2, 2)),
    cyx.layers.Conv2D(64, (3, 3),
        activation='relu'),
    cyx.layers.Flatten(),
    cyx.layers.Dense(64, activation='relu'),
    cyx.layers.Dense(10, activation='softmax')
])

RNN for Sequence Data

model = cyx.Sequential([
    cyx.layers.Embedding(
        10000, 128,
        input_length=100),
    cyx.layers.LSTM(64,
        return_sequences=True),
    cyx.layers.LSTM(32),
    cyx.layers.Dense(1,
        activation='sigmoid')
])

Saving and Loading

Models

# Save model
model.save('my_model.h5')

# Load model
model = cyx.load_model('my_model.h5')

# Save weights only
model.save_weights('weights.h5')
model.load_weights('weights.h5')

Node Editor Graphs

Save: File > Save or Ctrl+S
Load: File > Open or Ctrl+O
Export: File > Export > PyTorch/TensorFlow/Keras

Next Steps

Explore Examples

Check the examples/ folder

Read Documentation

Full API reference

Join Community

Discord for help

Contribute

Open source project