def fetch_datasets(self, activation_func_bounds): self.dataset = inp.read_ds_zip(FLAGS.input_path) if DEV: self.dataset = self.dataset[:FLAGS.batch_size*5] shape = list(self.dataset.shape) self.epoch_size = int(shape[0] / FLAGS.batch_size) self._batch_shape = shape self._batch_shape[0] = FLAGS.batch_size self.dataset = self.dataset[:int(len(self.dataset) / FLAGS.batch_size) * FLAGS.batch_size] self.dataset = inp.rescale_ds(self.dataset, activation_func_bounds.min, activation_func_bounds.max) self._image_shape = list(self.dataset.shape)[1:] self.test_set = inp.read_ds_zip(FLAGS.test_path)[0:FLAGS.test_max] self.test_set = inp.rescale_ds(self.test_set, self._activation.min, self._activation.max)
def fetch_datasets(self, activation_func_bounds): original_data, filters = inp.get_images(FLAGS.input_path) assert len(filters) == len(original_data) original_data, filters = self.bloody_hack_filterbatches(original_data, filters) ut.print_info('shapes. data, filters: %s' % str((original_data.shape, filters.shape))) original_data = inp.rescale_ds(original_data, activation_func_bounds.min, activation_func_bounds.max) self._image_shape = inp.get_image_shape(FLAGS.input_path) if DEV: original_data = original_data[:300] self.epoch_size = math.ceil(len(original_data) / FLAGS.batch_size) self.test_size = math.ceil(len(original_data) / FLAGS.batch_size) return original_data, filters
def fetch_datasets(self, activation_func_bounds): original_data, filters = inp.get_images(FLAGS.input_path) assert len(filters) == len(original_data) original_data, filters = self.bloody_hack_filterbatches( original_data, filters) ut.print_info('shapes. data, filters: %s' % str( (original_data.shape, filters.shape))) original_data = inp.rescale_ds(original_data, activation_func_bounds.min, activation_func_bounds.max) self._image_shape = inp.get_image_shape(FLAGS.input_path) if DEV: original_data = original_data[:300] self.epoch_size = math.ceil(len(original_data) / FLAGS.batch_size) self.test_size = math.ceil(len(original_data) / FLAGS.batch_size) return original_data, filters
def fetch_datasets(): activation_func_bounds = act.sigmoid original_data, filters = inp.get_images(source) original_data = inp.rescale_ds(original_data, activation_func_bounds.min, activation_func_bounds.max) part = 1. return original_data[:len(original_data)*part], original_data[len(original_data)*part:]