Practical: Full Training Pipeline

Every piece of this track, assembled

This is the payoff lesson: the seven concepts you've met so far — model, compile, optimizer, loss, metrics, fit, callbacks — snap together into one script you'll reuse for the rest of your career. Read the Code section top to bottom and you can name every line's job. That recognition is the skill; the syntax is just the surface.

Why this exact skeleton

The structure isn't arbitrary. Build runs first because nothing downstream exists without a model. The two Dropout layers are regularization wired in from the start, not bolted on after you notice overfitting. Compile binds Adam, the loss, and accuracy. The callback list is the same non-negotiable core from the last lesson — EarlyStopping to avoid wasting epochs, ModelCheckpoint so a crash never costs the run, ReduceLROnPlateau to squeeze a stalled plateau. Then one fit() call runs the whole loop and returns the history.

Internalize this order — build → compile → callbacks → fit — and almost every supervised-learning script you ever write becomes a fill-in-the-blanks variation of it. Swap the layers for your architecture, the loss for your task, the data for your problem; the skeleton holds. That reusability is exactly the kind of structural thinking the whole quest is training.

Code

Full pipeline: build → compile → callbacks → fit·python

import keras
from keras import layers

# Build model
model = keras.Sequential([
    keras.Input(shape=(784,)),
    layers.Dense(256, activation="relu"),
    layers.Dropout(0.3),
    layers.Dense(128, activation="relu"),
    layers.Dropout(0.2),
    layers.Dense(10, activation="softmax"),
])

# Compile
model.compile(
    optimizer=keras.optimizers.Adam(1e-3),
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)

# Callbacks
callbacks = [
    keras.callbacks.EarlyStopping(
        monitor="val_loss", patience=5,
        restore_best_weights=True,
    ),
    keras.callbacks.ModelCheckpoint(
        "best.keras", save_best_only=True,
    ),
    keras.callbacks.ReduceLROnPlateau(
        factor=0.5, patience=3,
    ),
]

# Train
history = model.fit(
    x_train, y_train,
    epochs=50,
    batch_size=32,
    validation_split=0.2,
    callbacks=callbacks,
)

Practical: Full Training Pipeline

Every piece of this track, assembled

Why this exact skeleton

Code

External links

Exercise

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