import pickle import time import torch class Solver(object): """ A Solver encapsulates all the logic necessary for training classification models. The Solver performs stochastic gradient descent using different update rules. The solver accepts both training and validation data and labels so it can periodically check classification accuracy on both training and validation data to watch out for overfitting. To train a model, you will first construct a Solver instance, passing the model, dataset, and various options (learning rate, batch size, etc) to the constructor. You will then call the train() method to run the optimization procedure and train the model. After the train() method returns, model.params will contain the parameters that performed best on the validation set over the course of training. In addition, the instance variable solver.loss_history will contain a list of all losses encountered during training and the instance variables solver.train_acc_history and solver.val_acc_history will be lists of the accuracies of the model on the training and validation set at each epoch. Example usage might look something like this: data = { 'X_train': # training data 'y_train': # training labels 'X_val': # validation data 'y_val': # validation labels } model = MyAwesomeModel(hidden_size=100, reg=10) solver = Solver(model, data, update_rule=sgd, optim_config={ 'learning_rate': 1e-3, }, lr_decay=0.95, num_epochs=10, batch_size=100, print_every=100, device='cuda') solver.train() A Solver works on a model object that must conform to the following API: - model.params must be a dictionary mapping string parameter names to numpy arrays containing parameter values. - model.loss(X, y) must be a function that computes training-time loss and gradients, and test-time classification scores, with the following inputs and outputs: Inputs: - X: Array giving a minibatch of input data of shape (N, d_1, ..., d_k) - y: Array of labels, of shape (N,) giving labels for X where y[i] is the label for X[i]. Returns: If y is None, run a test-time forward pass and return: - scores: Array of shape (N, C) giving classification scores for X where scores[i, c] gives the score of class c for X[i]. If y is not None, run a training time forward and backward pass and return a tuple of: - loss: Scalar giving the loss - grads: Dictionary with the same keys as self.params mapping parameter names to gradients of the loss with respect to those parameters. - device: device to use for computation. 'cpu' or 'cuda' """ def __init__(self, model, data, **kwargs): """ Construct a new Solver instance. Required arguments: - model: A model object conforming to the API described above - data: A dictionary of training and validation data containing: 'X_train': Array, shape (N_train, d_1, ..., d_k) of training images 'X_val': Array, shape (N_val, d_1, ..., d_k) of validation images 'y_train': Array, shape (N_train,) of labels for training images 'y_val': Array, shape (N_val,) of labels for validation images Optional arguments: - update_rule: A function of an update rule. Default is sgd. - optim_config: A dictionary containing hyperparameters that will be passed to the chosen update rule. Each update rule requires different hyperparameters but all update rules require a 'learning_rate' parameter so that should always be present. - lr_decay: A scalar for learning rate decay; after each epoch the learning rate is multiplied by this value. - batch_size: Size of minibatches used to compute loss and gradient during training. - num_epochs: The number of epochs to run for during training. - print_every: Integer; training losses will be printed every print_every iterations. - print_acc_every: We will print the accuracy every print_acc_every epochs. - verbose: Boolean; if set to false then no output will be printed during training. - num_train_samples: Number of training samples used to check training accuracy; default is 1000; set to None to use entire training set. - num_val_samples: Number of validation samples to use to check val accuracy; default is None, which uses the entire validation set. - checkpoint_name: If not None, then save model checkpoints here every epoch. """ self.model = model self.X_train = data["X_train"] self.y_train = data["y_train"] self.X_val = data["X_val"] self.y_val = data["y_val"] # Unpack keyword arguments self.update_rule = kwargs.pop("update_rule", self.sgd) self.optim_config = kwargs.pop("optim_config", {}) self.lr_decay = kwargs.pop("lr_decay", 1.0) self.batch_size = kwargs.pop("batch_size", 100) self.num_epochs = kwargs.pop("num_epochs", 10) self.num_train_samples = kwargs.pop("num_train_samples", 1000) self.num_val_samples = kwargs.pop("num_val_samples", None) self.device = kwargs.pop("device", "cpu") self.checkpoint_name = kwargs.pop("checkpoint_name", None) self.print_every = kwargs.pop("print_every", 10) self.print_acc_every = kwargs.pop("print_acc_every", 1) self.verbose = kwargs.pop("verbose", True) # Throw an error if there are extra keyword arguments if len(kwargs) > 0: extra = ", ".join('"%s"' % k for k in list(kwargs.keys())) raise ValueError("Unrecognized arguments %s" % extra) self._reset() def _reset(self): """ Set up some book-keeping variables for optimization. Don't call this manually. """ # Set up some variables for book-keeping self.epoch = 0 self.best_val_acc = 0 self.best_params = {} self.loss_history = [] self.train_acc_history = [] self.val_acc_history = [] # Make a deep copy of the optim_config for each parameter self.optim_configs = {} for p in self.model.params: d = {k: v for k, v in self.optim_config.items()} self.optim_configs[p] = d def _step(self): """ Make a single gradient update. This is called by train() and should not be called manually. """ # Make a minibatch of training data num_train = self.X_train.shape[0] batch_mask = torch.randperm(num_train)[: self.batch_size] X_batch = self.X_train[batch_mask].to(self.device) y_batch = self.y_train[batch_mask].to(self.device) # Compute loss and gradient loss, grads = self.model.loss(X_batch, y_batch) self.loss_history.append(loss.item()) # Perform a parameter update with torch.no_grad(): for p, w in self.model.params.items(): dw = grads[p] config = self.optim_configs[p] next_w, next_config = self.update_rule(w, dw, config) self.model.params[p] = next_w self.optim_configs[p] = next_config def _save_checkpoint(self): if self.checkpoint_name is None: return checkpoint = { "model": self.model, "update_rule": self.update_rule, "lr_decay": self.lr_decay, "optim_config": self.optim_config, "batch_size": self.batch_size, "num_train_samples": self.num_train_samples, "num_val_samples": self.num_val_samples, "epoch": self.epoch, "loss_history": self.loss_history, "train_acc_history": self.train_acc_history, "val_acc_history": self.val_acc_history, } filename = "%s_epoch_%d.pkl" % (self.checkpoint_name, self.epoch) if self.verbose: print('Saving checkpoint to "%s"' % filename) with open(filename, "wb") as f: pickle.dump(checkpoint, f) @staticmethod def sgd(w, dw, config=None): """ Performs vanilla stochastic gradient descent. config format: - learning_rate: Scalar learning rate. """ if config is None: config = {} config.setdefault("learning_rate", 1e-2) w -= config["learning_rate"] * dw return w, config def check_accuracy(self, X, y, num_samples=None, batch_size=100): """ Check accuracy of the model on the provided data. Inputs: - X: Array of data, of shape (N, d_1, ..., d_k) - y: Array of labels, of shape (N,) - num_samples: If not None, subsample the data and only test the model on num_samples datapoints. - batch_size: Split X and y into batches of this size to avoid using too much memory. Returns: - acc: Scalar giving the fraction of instances that were correctly classified by the model. """ # Maybe subsample the data N = X.shape[0] if num_samples is not None and N > num_samples: mask = torch.randperm(N, device=self.device)[:num_samples] N = num_samples X = X[mask] y = y[mask] X = X.to(self.device) y = y.to(self.device) # Compute predictions in batches num_batches = N // batch_size if N % batch_size != 0: num_batches += 1 y_pred = [] for i in range(num_batches): start = i * batch_size end = (i + 1) * batch_size scores = self.model.loss(X[start:end]) y_pred.append(torch.argmax(scores, dim=1)) y_pred = torch.cat(y_pred) acc = (y_pred == y).to(torch.float).mean() return acc.item() def train(self, time_limit=None, return_best_params=True): """ Run optimization to train the model. """ num_train = self.X_train.shape[0] iterations_per_epoch = max(num_train // self.batch_size, 1) num_iterations = self.num_epochs * iterations_per_epoch prev_time = start_time = time.time() for t in range(num_iterations): cur_time = time.time() if (time_limit is not None) and (t > 0): next_time = cur_time - prev_time if cur_time - start_time + next_time > time_limit: print( "(Time %.2f sec; Iteration %d / %d) loss: %f" % ( cur_time - start_time, t, num_iterations, self.loss_history[-1], ) ) print("End of training; next iteration will exceed the time limit.") break prev_time = cur_time self._step() # Maybe print training loss if self.verbose and t % self.print_every == 0: print( "(Time %.2f sec; Iteration %d / %d) loss: %f" % ( time.time() - start_time, t + 1, num_iterations, self.loss_history[-1], ) ) # At the end of every epoch, increment the epoch counter and decay # the learning rate. epoch_end = (t + 1) % iterations_per_epoch == 0 if epoch_end: self.epoch += 1 for k in self.optim_configs: self.optim_configs[k]["learning_rate"] *= self.lr_decay # Check train and val accuracy on the first iteration, the last # iteration, and at the end of each epoch. with torch.no_grad(): first_it = t == 0 last_it = t == num_iterations - 1 if first_it or last_it or epoch_end: train_acc = self.check_accuracy( self.X_train, self.y_train, num_samples=self.num_train_samples ) val_acc = self.check_accuracy( self.X_val, self.y_val, num_samples=self.num_val_samples ) self.train_acc_history.append(train_acc) self.val_acc_history.append(val_acc) self._save_checkpoint() if self.verbose and self.epoch % self.print_acc_every == 0: print( "(Epoch %d / %d) train acc: %f; val_acc: %f" % (self.epoch, self.num_epochs, train_acc, val_acc) ) # Keep track of the best model if val_acc > self.best_val_acc: self.best_val_acc = val_acc self.best_params = {} for k, v in self.model.params.items(): self.best_params[k] = v.clone() # At the end of training swap the best params into the model if return_best_params: self.model.params = self.best_params