The optimizer function runs when another guess is made. It tries to minimise the loss based on the loss function.

The optimizer function runs when another guess is made. It tries to ==minimise the loss based on the loss function==.

Example in TensorflowJS

The adam optimiser is effective and requires no configuration.

// Prepare the model for training.
model.compile({
  optimizer: tf.train.adam(),
  loss: tf.losses.meanSquaredError,
  metrics: ['mse'],
});

Example in Python

Example using mean_squared_error for the loss and stochastic gradient descent (sgd) for the optimizer.

# add loss and optimizer to model
model.compile(optimizer='sgd', loss='mean_squared_error')