import tensorflow as tf

import numpy as np

data = np.array([[1, 2, 3],

                 [2, 3, 4],

                 [4, 5, 6]])

filenames = tf.unstack(data)  #表示输入的数据

with tf.Session() as sess:

    for filename in filenames:

        print(sess.run(filename))
# [1, 2, 3]
# [4, 5, 6]
# [7, 8, 9]

import tensorflow as tf

from tensorflow.python.ops import data_flow_ops

import numpy as np

# 构造初始的数据

image_paths_placeholder = tf.placeholder(tf.string, shape=(None, 3), name='image_path')

label_paths_placeholder = tf.placeholder(tf.int32, shape=(None, 3), name='labels')

# 构造输入的队列

input_queue = data_flow_ops.FIFOQueue(capacity=3,

                                      dtypes=[tf.string, tf.int32],

                                      shapes=([3, ], [3, ]),

                                      shared_name=None, name=None)

# 将数据放入

enqueue_op = input_queue.enqueue_many([image_paths_placeholder, label_paths_placeholder])

# 进行变量初始化

init = tf.global_variables_initializer()

X = np.array([['', '', ''], ['', '', ''], ['', '', '']])

Y = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])

filename_labels = []

with tf.Session() as sess:

    # 将数据进行打包输出

    filenames, labels = input_queue.dequeue()

    # print(sess.run(filenames))

    images = []

    for filename in tf.unstack(filenames): # 将数据集按照axis=0进行拆分

        images.append(filename) # 将数据进行拆分, 这里可以对图片进行处理

        # print(sess.run(filename))

    filename_labels.append([images, labels]) # 将图片和标签进行添加

    #

    # # 使用图片和标签构造batch_size数据集

    image_batch, label_batch = tf.train.batch_join(

        filename_labels, batch_size=1,

        shapes=[(), ()], enqueue_many=True,

        capacity= 4 * 10,

        allow_smaller_final_batch=True

    )

    image_batch = tf.identity(image_batch, 'image_batch')

    enqueue_op.run(feed_dict={image_paths_placeholder: X, label_paths_placeholder: Y})

    x = sess.run([image_batch])

    print(1)

    # print(sess.run(image_batch))

    # 将数据进行输入