text: from __future__ import print_function, division
  from future import standard_library
  
  standard_library.install_aliases()
  from builtins import range
  from builtins import object
  import os
  import pickle as pickle
  
  import numpy as np
  
  from cs231n import optim
  
  
  class Solver(object):
      """
      A Solver encapsulates all the logic necessary for training classification
      models. The Solver performs stochastic gradient descent using different
      update rules defined in optim.py.
  
      The solver accepts both training and validataion 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)
      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.
      """
  
      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 string giving the name of an update rule in optim.py.
            Default is 'sgd'.
          - optim_config: A dictionary containing hyperparameters that will be
            passed to the chosen update rule. Each update rule requires different
            hyperparameters (see optim.py) 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.
          - 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", "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.checkpoint_name = kwargs.pop("checkpoint_name", None)
          self.print_every = kwargs.pop("print_every", 10)
          self.verbose = kwargs.pop("verbose", True)
          self.verbose_epoch = kwargs.pop("verbose_epoch", 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)
  
          # Make sure the update rule exists, then replace the string
          # name with the actual function
          if not hasattr(optim, self.update_rule):
              raise ValueError('Invalid update_rule "%s"' % self.update_rule)
          self.update_rule = getattr(optim, self.update_rule)
  
          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 = np.random.choice(num_train, self.batch_size)
          X_batch = self.X_train[batch_mask]
          y_batch = self.y_train[batch_mask]
  
          # Compute loss and gradient
          loss, grads = self.model.loss(X_batch, y_batch)
          self.loss_history.append(loss)
  
          # Perform a parameter update
          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)
  
      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 = np.random.choice(N, num_samples)
              N = num_samples
              X = X[mask]
              y = y[mask]
  
          # 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(np.argmax(scores, axis=1))
          y_pred = np.hstack(y_pred)
          acc = np.mean(y_pred == y)
  
          return acc
  
      def train(self):
          """
          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
  
          for t in range(num_iterations):
              self._step()
  
              # Maybe print training loss
              if self.verbose and t % self.print_every == 0:
                  print(
                      "(Iteration %d / %d) loss: %f"
                      % (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.
              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_epoch:
                      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.copy()
  
          # At the end of training swap the best params into the model
          self.model.params = self.best_params
  