# Untitled

unknown

python

a year ago

3.3 kB

2

Indexable

Never

^{}

def train_one_epoch( model: Type[LogisticRegressionModel], train_set: np.ndarray, train_labels: np.ndarray, val_set: np.ndarray, val_labels:np.ndarray, batch_size: int, learning_rate: float, validation_every_x_step: int ) -> float: """ Trains the model for one epoch on the entire dataset with the given learning rate and batch size Args: model (class): the model used to train train_set (np.ndarray): Numpy array of shape [val_size x num_features] train_labels (np.ndarray): Numpy array of shape [val_size] val_set (np.ndarray): Numpy array of shape [val_size x num_features] val_labels (np.ndarray): Numpy array of shape [val_size] batch_size (int): the batch size to be used to iterate through the dataset learning_rate (float): the learning rate to be used for mini-batch gradient descent optimization validation_every_x_step (int): the number of steps to wait before performing validation Returns: train_losses (list): a list of training losses train_accuracies (list): a list of training accuracies # train_steps (list): a list of the training batch ids, i.e. each element is the n-th batch of the training set train_steps (list): a list of the number of training steps. One training step is defined as one forward pass (i.e. calculating the loss) AND one backward pass (i.e. calculating the gradients and updating the parameters) of a mini-batch of samples through the model val_losses (list): a list of validation losses val_accuracies (list): a list of validation accuracies val_steps (list): a list of the validation steps. One validation step is defined one forward pass of the validaton mini-batch samples through the model """ train_losses = [] train_accuracies = [] train_steps = [] val_losses = [] val_accuracies = [] val_steps = [] step_count = 0 # Iterate through the training set and append the corresponding metrics to the list for x_batch, targets in iterate_samples(batch_size, train_set, train_labels, True): step_count += 1 pred = model.__call__(x_batch) grad_W, grad_b = compute_gradients(x_batch, pred, targets) model.W = model.W - learning_rate * grad_W model.b = model.b - learning_rate * grad_b train_loss, train_acc = validation(model, train_set, train_labels, batch_size) train_losses.append(train_loss) train_accuracies.append(train_acc) train_steps.append(step_count) # perform validation depending on the value of `validation_every_x_step` if (step_count % validation_every_x_step) == 0 or step_count == 1: validation_loss, validation_acc = validation(model, val_set, val_labels, batch_size) val_losses.append(validation_loss) val_accuracies.append(validation_acc) val_steps.append(step_count) return train_losses, train_accuracies, train_steps, val_losses, val_accuracies, val_steps