Neural Networks with Keras

Introduction

Build neural networks with Keras high-level API.

Sequential Model

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([
    layers.Dense(64, activation="relu", input_shape=(784,)),
    layers.Dense(32, activation="relu"),
    layers.Dense(10, activation="softmax")
])

Model Compilation

model.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"]
)

Training

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)

# Evaluate
loss, accuracy = model.evaluate(X_test, y_test)

# Predict
predictions = model.predict(X_test)

Practice Problems

1. Build multi-layer network
2. Use different optimizers
3. Add dropout for regularization
4. Monitor training with callbacks
5. Save and load models