Training a Keras-Style Model: History and Early Stopping

How it works

1. Build a Sequential stack of Dense layers ending in Dense(1, sigmoid); each layer has units * (inputs + 1) weights and biases, initialised Glorot-uniform with zero biases.
2. compile(): mini-batch SGD with the chosen learning rate, binary cross-entropy (or mean squared error) loss, and accuracy (prediction > 0.5) as the metric.
3. fit(): each epoch walks the training set in batches of batch_size; every batch runs a forward pass, backpropagates the mean batch loss and updates every weight w <- w - lr * dL/dw.
4. After each epoch the history records the epoch's training loss and accuracy (batch-weighted average of values seen during the epoch) and the val_loss and val_accuracy on the held-out validation points.
5. EarlyStopping(monitor="val_loss", patience) keeps the best val_loss; after patience epochs with no improvement it stops training, and restore_best_weights puts back the weights of the best epoch.
6. evaluate() reports loss and accuracy on the validation data with the final weights.

Default run (153 steps): Sequential([Dense(16, "relu"), Dense(16, "relu"), Dense(1, "sigmoid")]) on inputs of shape (2,). model.summary(): dense 16 * (2 + 1) = 48; dense_1 16 * (16 + 1) = 272; dense_2 1 * (16 + 1) = 17; total 337 trainable parameters, initialised with Glorot uniform (biases 0). … model.evaluate(x_val, y_val) with the weights from epoch 150: loss 1.2082, accuracy 0.75. Training loss went 0.6964 -> 0.0194. Stopping at epoch 32 would have given val_loss 0.5272 and val_accuracy 0.775.