reader.py 文件源码

def _generate_image_and_label_batch(self, image, label, min_queue_examples,
                                        shuffle):
        """Construct a queued batch of images and labels.

        Args:
            image: 3-D Tensor of [height, width, 3] of type.float32.
            label: 1-D Tensor of type.int32
            min_queue_examples: int32, minimum number of samples to retain
                in the queue that provides of batches of examples.
            batch_size: Number of images per batch.
            shuffle: boolean indicating whether to use a shuffling queue.

        Returns:
            images: Images. 4D tensor of [batch_size, height, width, 3] size.
            labels: Labels. 1D tensor of [batch_size] size.
        """
        # Create a queue that shuffles the examples, and then
        # read 'batch_size' images + labels from the example queue.
        if shuffle:
            images, labels = tf.train.shuffle_batch(
                [image, label],
                batch_size = self.batch_size,
                num_threads = self.num_threads,
                capacity = min_queue_examples + 3 * self.batch_size,
                min_after_dequeue = min_queue_examples)
        else:
            images, labels = tf.train.batch(
                [image, label],
                batch_size = self.batch_size,
                num_threads = self.num_threads,
                capacity = min_queue_examples + 3 * self.batch_size)

        # Display the training images in the visualizer.
        tf.image_summary('images', images, max_images = 3)

        return {'images' : images, 'labels' : labels}