supervised.py 文件源码

def read_and_decode(self, example_serialized):
        """ Read and decode binarized, raw MNIST dataset from .tfrecords file generated by MNIST.py """
        features = tf.parse_single_example(
            example_serialized,
            features={
                'image': tf.FixedLenFeature([], tf.string),
                'label': tf.FixedLenFeature([self.flags['num_classes']], tf.int64, default_value=[-1]*self.flags['num_classes']),
                'height': tf.FixedLenFeature([], tf.int64),
                'width': tf.FixedLenFeature([], tf.int64),
                'depth': tf.FixedLenFeature([], tf.int64),
            })
        # now return the converted data
        label = features['label']
        image = tf.decode_raw(features['image'], tf.float32)
        image.set_shape([784])
        image = tf.reshape(image, [28, 28, 1])
        image = (image - 0.5) * 2  # max value = 1, min value = -1
        return image, tf.cast(label, tf.int32)