tensorflow tf.data使用笔记

dataset = tf.data.Dataset.range(100)

dataset = tf.data.Dataset.from_tensor_slices(np.array([1,2,3,4,5]))

dataset = tf.data.Dataset.from_tensor_slices(np.array([1,2,3,4,5]), np.array([5,4,3,2,1]))

dataset = dataset.shuffle(10000).batch(128)

dataset = dataset.map(some function)

iterator = dataset.make_one_shot_iterator()

iterator = dataset.make_initializable_iterator()    # have to be initialized

img, label = iterator.get_next()

sess.run(iterator.initializer, maybe feed_dict={...})

iterator = tf.data.Iterator.from_structure(output_types, output_shapes)

img, label = iterator.get_next()

train_init = iterator.make_initializer(training_data_set)

with tf.Session() as sess:

    for i in range(epoches):

        sess.run(train_init)

        try: 

            while true:

                sess.run(balabala)

        except    tf.errors.OutOfRangeError:

                do something