keras

Setup

On first use, check setup.md for integration guidelines. The skill stores preferences in ~/keras/ when the user confirms.

When to Use

User builds neural networks with Keras or TensorFlow. Agent handles model architecture, layer configuration, training loops, callbacks, debugging loss issues, and deployment preparation.

Architecture

Memory lives in ~/keras/. See memory-template.md for setup.

~/keras/
├── memory.md          # Preferred architectures, hyperparams
└── models/            # Saved model configs (optional)

Quick Reference

Topic	File
Setup process	`setup.md`
Memory template	`memory-template.md`
Layer patterns	`layers.md`
Training diagnostics	`training.md`
Common architectures	`architectures.md`

Core Rules

1. Sequential vs Functional API

Sequential: simple stacks, no branching
Functional: multi-input/output, skip connections, shared layers
Subclassing: custom forward pass, dynamic architectures

# Sequential - simple stack
model = keras.Sequential([
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Functional - flexible graphs
inputs = keras.Input(shape=(784,))
x = layers.Dense(64, activation='relu')(inputs)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)

2. Input Shape Patterns

First layer needs input_shape (exclude batch)
Images: (height, width, channels) for channels_last
Sequences: (timesteps, features)
Tabular: (features,)

# Image input
layers.Conv2D(32, 3, input_shape=(224, 224, 3))

# Sequence input
layers.LSTM(64, input_shape=(100, 50))  # 100 timesteps, 50 features

# Tabular input
layers.Dense(64, input_shape=(20,))  # 20 features

3. Activation Functions

Task	Output Activation	Loss
Binary classification	`sigmoid`	`binary_crossentropy`
Multi-class	`softmax`	`categorical_crossentropy`
Multi-label	`sigmoid`	`binary_crossentropy`
Regression	`linear` (none)	`mse` or `mae`

4. Regularization Stack

Apply in this order for overfitting: 1. Dropout - after dense/conv layers (0.2-0.5) 2. BatchNorm - before or after activation 3. L2 regularization - in layer (0.01-0.001) 4. Early stopping - callback with patience

layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.01))
layers.Dropout(0.3)
layers.BatchNormalization()

5. Callbacks Essentials

callbacks = [
    keras.callbacks.EarlyStopping(
        monitor='val_loss', patience=5, restore_best_weights=True
    ),
    keras.callbacks.ModelCheckpoint(
        'best_model.keras', save_best_only=True
    ),
    keras.callbacks.ReduceLROnPlateau(
        monitor='val_loss', factor=0.5, patience=3
    ),
    keras.callbacks.TensorBoard(log_dir='./logs')
]

6. Data Pipeline

# tf.data for performance
dataset = tf.data.Dataset.from_tensor_slices((x, y))
dataset = dataset.shuffle(10000).batch(32).prefetch(tf.data.AUTOTUNE)

# ImageDataGenerator for augmentation
datagen = keras.preprocessing.image.ImageDataGenerator(
    rotation_range=20,
    horizontal_flip=True,
    validation_split=0.2
)

7. Compile Checklist

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

Learning rate: start 0.001, reduce on plateau
Batch size: 32-128 typical, larger = smoother gradients

Common Traps

Input shape mismatch → check data shape vs model input_shape, exclude batch dim
Loss is NaN → reduce learning rate, check for inf/nan in data, add gradient clipping
Validation loss diverges → add regularization, reduce model capacity, more data
Model not learning → check labels are correct, verify loss function matches task
GPU OOM → reduce batch size, use mixed precision, gradient checkpointing
Slow training → use tf.data pipeline with prefetch, enable XLA compilation

External Endpoints

Endpoint	Data Sent	Purpose
TensorFlow model hub	None (download only)	Pretrained weights when using `weights='imagenet'`

Note: Transfer learning examples download pretrained weights on first use. Use weights=None for fully offline operation.

Security & Privacy

Data that stays local: - Model architectures and configs in ~/keras/ - Training preferences and hyperparameters

This skill does NOT: - Upload models or data anywhere - Access files outside ~/keras/ and working directory - Store training data

Install with clawhub install <slug> if user confirms:

tensorflow — TensorFlow operations and deployment
pytorch — Alternative deep learning framework
ai — General AI and ML patterns
models — Model architecture design

Feedback

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