Deep Learning with Keras 3
Patterns and best practices based on *Deep Learning with Python, 2nd Edition* by François Chollet, updated for Keras 3 (Multi-Backend).
Core Workflow
- Prepare Data: Normalize, split train/val/test, create
tf.data.Dataset - Build Model: Sequential, Functional, or Subclassing API
- Compile:
model.compile(optimizer, loss, metrics) - Train:
model.fit(data, epochs, validation_data, callbacks) - Evaluate:
model.evaluate(test_data)
Model Building APIs
Sequential - Simple stack of layers:
model = keras.Sequential([
layers.Dense(64, activation="relu"),
layers.Dense(10, activation="softmax")
])Functional - Multi-input/output, shared layers, non-linear topologies:
inputs = keras.Input(shape=(64,))
x = layers.Dense(64, activation="relu")(inputs)
outputs = layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)Subclassing - Full flexibility with call() method:
class MyModel(keras.Model):
def __init__(self):
super().__init__()
self.dense1 = layers.Dense(64, activation="relu")
self.dense2 = layers.Dense(10, activation="softmax")
def call(self, inputs):
x = self.dense1(inputs)
return self.dense2(x)Quick Reference: Loss & Optimizer Selection
| Task | Loss | Final Activation |
|---|---|---|
| Binary classification | binary_crossentropy | sigmoid |
| Multiclass (one-hot) | categorical_crossentropy | softmax |
| Multiclass (integers) | sparse_categorical_crossentropy | softmax |
| Regression | mse or mae | None |
Optimizers: rmsprop (default), adam (popular), sgd (with momentum for fine-tuning)
Domain-Specific Guides
| Topic | Reference | When to Use |
|---|---|---|
| Keras 3 Migration | keras3_changes.md | START HERE: Multi-backend setup, keras.ops, import keras |
| Fundamentals | basics.md | Overfitting, regularization, data prep, K-fold validation |
| Keras Deep Dive | keras_working.md | Custom metrics, callbacks, training loops, tf.function |
| Computer Vision | computer_vision.md | Convnets, data augmentation, transfer learning |
| Advanced CV | advanced_cv.md | Segmentation, ResNets, Xception, Grad-CAM |
| Time Series | timeseries.md | RNNs (LSTM/GRU), 1D convnets, forecasting |
| NLP & Transformers | nlp_transformers.md | Text processing, embeddings, Transformer encoder/decoder |
| Generative DL | generative_dl.md | Text generation, VAEs, GANs, style transfer |
| Best Practices | best_practices.md | KerasTuner, mixed precision, multi-GPU, TPU |
Essential Callbacks
callbacks = [
keras.callbacks.EarlyStopping(monitor="val_loss", patience=3),
keras.callbacks.ModelCheckpoint("best.keras", save_best_only=True),
keras.callbacks.TensorBoard(log_dir="./logs")
]
model.fit(..., callbacks=callbacks)Utility Scripts
| Script | Description |
|---|---|
| quick_train.py | Reusable training template with standard callbacks and history plotting |
| visualize_filters.py | Visualize convnet filter patterns via gradient ascent |