Build and Optimize Keras Sequential Models

Keras Sequential Models provides guidance for constructing, training, and optimizing neural networks with the Keras Sequential API. It starts from explicit input shapes, appropriate activations, regularization, and computationally sensible layer composition. The examples connect model architecture with compilation, validation monitoring, evaluation, and saving.

Dense-network patterns combine decreasing hidden-layer sizes with batch normalization, dropout, and a softmax classification output. A separate construction example adds L2 kernel regularization. The convolutional pattern stacks convolution, normalization, pooling, and dropout before flattening and dense classification layers. These patterns show how feature processing and output-layer choices fit within a sequential model.

Compilation examples use Adam with explicit learning-rate parameters and classification losses and metrics. ReduceLROnPlateau lowers the learning rate when validation loss stalls, while EarlyStopping restores the best weights after its patience period. Training supplies validation data, batch size, epoch count, callbacks, shuffling, and progress output.

Advanced patterns include transfer learning from VGG16 with ImageNet weights, a frozen base model, global average pooling, and a new classification head. The time-series example stacks LSTM layers, passes sequences between recurrent layers, applies dropout, and ends in a linear scalar output. It accepts sequence length, feature count, and recurrent-layer widths.

Use this skill when developing sequential dense, convolutional, or recurrent models and deciding how to monitor training. It is also useful for adapting a pretrained image model or inspecting architecture and learning curves.

The guidance warns against inconsistent normalization between training and inference, data leakage from poor train-validation-test splits, and judging overfitting from training metrics alone. Loss functions need to match sparse or categorical targets. Example architecture sizes and training settings are starting patterns, not measured accuracy guarantees.

Inputs include input dimensions, image shapes, class counts, sequence lengths, feature counts, training data, validation data, and test data. Outputs include Sequential model definitions, training history, evaluation results, architecture summaries and images, and saved models or weight files.

Save and reload a model with these operations:

model.save('my_model.h5')
model.save_weights('model_weights.h5')
from tensorflow.keras.models import load_model
loaded_model = load_model('my_model.h5')

Analysis examples inspect the model summary, render layer names and shapes, evaluate held-out data, and plot training and validation accuracy and loss. Memory and performance suggestions cover batch prediction, tf.data.Dataset loading, mixed precision on suitable GPUs, and quantization for deployment optimization.

The examples use TensorFlow's Keras models, layers, regularizers, optimizers, callbacks, pretrained applications, and visualization utilities. Matplotlib plots learning history, and VGG16 supplies the frozen image-feature base in the transfer-learning pattern.