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_