Keras

Build, train, and debug deep learning models with Keras patterns, layer recipes, and training diagnostics.

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.

text

~/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

python

# 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,)

python

# 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

python

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

5. Callbacks Essentials

python

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

python

# 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

python

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

Related Skills

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

• If useful: clawhub star keras
• Stay updated: clawhub sync