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:

python

model = keras.Sequential([
    layers.Dense(64, activation="relu"),
    layers.Dense(10, activation="softmax")
])

Functional - Multi-input/output, shared layers, non-linear topologies:

python

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:

python

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

python

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