I hope you are doing well.
I want to run the below code for maxiter = 20001. I don’t want it to stop by some default criteria.

    fun=get_loss_and_grads,
    x0=params,
    jac=True,
    method='L-BFGS-B',
    options={'maxiter': 20001, 'disp': True, 'ftol': 0.0, 'gtol': 0.0, 'maxcor': 50},
    callback=PrintLossCallback(print_freq=100)
)

Currently, it is stopped at approx. 2500 iterations but I want it to run for a full 20001 iterations. How to do that? Below is the full code:

def get_loss_and_grads(params):
    shapes = [w.shape for w in model.get_weights()]
    split_params = np.split(params, np.cumsum([np.prod(shape) for shape in shapes[:-1]]))
    weights = [tf.Variable(param.reshape(shape)) for param, shape in zip(split_params, shapes)]
    model.set_weights(weights)

    with tf.GradientTape() as tape:
        loss = combined_loss(X_train_tf[:, 0:1], X_train_tf[:, 1:2], x_init_tf, t_init_tf, u_initial_tf, x_bc_tf, t_bc_tf)
    gradients = tape.gradient(loss, model.trainable_variables)

    flattened_gradients = np.concatenate([grad.numpy().flatten() for grad in gradients])
    return loss.numpy().astype(np.float64), flattened_gradients

X_train_tf = tf.convert_to_tensor(X_train.astype(np.float32))
x_init_tf = tf.convert_to_tensor(x_init.astype(np.float32))
t_init_tf = tf.convert_to_tensor(t_init.astype(np.float32))
u_initial_tf = tf.convert_to_tensor(u_initial.astype(np.float32))
x_bc_tf = tf.convert_to_tensor(x_bc.astype(np.float32))
t_bc_tf = tf.convert_to_tensor(t_bc.astype(np.float32))


shapes = [w.shape for w in model.get_weights()]
params = np.concatenate([w.flatten() for w in model.get_weights()])

class PrintLossCallback:
    def __init__(self, print_freq=100):
        self.print_freq = print_freq
        self.iteration = 0

    def __call__(self, xk):
        self.iteration += 1
        if self.iteration % self.print_freq == 0:
            loss, _ = get_loss_and_grads(xk)
            print(f"Iteration {self.iteration}, Loss: {loss}")

start_time = time.time()


results = scipy.optimize.minimize(
    fun=get_loss_and_grads,
    x0=params,
    jac=True,
    method='L-BFGS-B',
    options={'maxiter': 20001, 'disp': True, 'ftol': 0.0, 'gtol': 0.0, 'maxcor': 50},
    callback=PrintLossCallback(print_freq=100)
)

optimized_params = results.x
split_params = np.split(optimized_params, np.cumsum([np.prod(shape) for shape in shapes[:-1]]))
model.set_weights([param.reshape(shape) for param, shape in zip(split_params, shapes)])

elapsed_time = time.time() - start_time
elapsed_minutes = elapsed_time / 60.0  # Convert seconds to minutes

print(f"Execution time: {elapsed_minutes:.2f} minutes")

Thank you in advance.

Best,
Saif Ur Rehman.

I tried to set ‘ftol’: 0.0, ‘gtol’: 0.0, and maxiter to a large number like 200001 but all in vain.