def run(self):
imgs, labels = read_mnist(path=self.data_path, training=True)
imgs = binarize_mnist_images(imgs)
self.fit(imgs, labels)
imgs, labels = read_mnist(path=self.data_path, training=False)
imgs = binarize_mnist_images(imgs)
self.coding_cost = -self.score(imgs, labels)
def run(self):
# Fit training data.
imgs, labels = read_mnist(path=self.data_path, training=True)
imgs = binarize_mnist_images(imgs)
machines = self.fit(imgs, labels)
# Compute error rate on test set.
imgs, labels = read_mnist(path=self.data_path, training=False)
idx = np.in1d(labels, machines.keys())
imgs = binarize_mnist_images(imgs[idx])
labels = labels[idx]
predicted = self.predict(imgs)
self.error_rate = np.sum(predicted != labels) / float(len(imgs))
def run(self):
# Validate settings.
if len(self.digits) < 2:
raise ValueError("Must use at least 2 digit classes")
# Train the machine.
imgs, labels = read_mnist(path=self.data_path, training=True)
idx = np.in1d(labels, self.digits)
imgs = binarize_mnist_images(imgs[idx])
self.machine = machine = self.create_machine()
self.train(machine, imgs)
# Score the machine.
imgs, labels = read_mnist(path=self.data_path, training=False)
imgs = binarize_mnist_images(imgs)
self.score(imgs, labels)
def run(self):
imgs, labels = read_mnist(path=self.data_path, training=True)
imgs = binarize_mnist_images(imgs)
gs = GridSearchCV(self.base_runner, self.param_grid,
n_jobs = self.jobs, pre_dispatch = '2*n_jobs',
cv = self.cv, verbose = self.verbose)
gs.fit(imgs, labels)
self.param_scores = gs.grid_scores_
self.best_params = gs.best_params_
self.best_score = gs.best_score_
