python - 如何使用 shuffle_batch() 函数避免超出范围错误？

Question

更新问题

我正在尝试使用 shuffle_batch() 函数将标签与 tensorflow 中的图像匹配，但是当我开始使用shuffle_batch() 函数。

1。我更新的问题

使用shuffle_batch函数时如何避免queue out of range错误？

2.我更新的代码

以下代码在前 90 步左右运行良好，准确性不断提高，直到引发错误。

# Global Parameters

# Image Size
training_size = 1387
img_height = 64
img_width = 64

# File stream
batch_size = 128

# Training parameter
learning_rate = 0.001
training_iters = 100
keep_prob = 0.5 #dropout keep prob
display_step = 10

AdamOptimizer = 1
GradientDescentOptimizer = 0

# Filepath
csv_filepath = r'C:/Users/Jeffy/OneDrive/Course\NMDA\retinaProject\label.csv'
image_filepath = 'Image_P/'


import tensorflow as tf
# =============================================================================
# Read input data

# load csv content
csv_path = tf.train.string_input_producer(['label_3D.csv'])
textReader = tf.TextLineReader()
_, csv_content = textReader.read(csv_path)
im_name, col_2, col_3, col_4 = tf.decode_csv(csv_content, record_defaults=[[""], [1], [1], [1]])
label = tf.pack([col_2, col_3, col_4])
label_float32 = tf.cast(label, tf.float32)

# load images
im_content = tf.read_file(image_filepath + im_name+'.jpeg')
image = tf.image.decode_jpeg(im_content, channels=3)
image_float32 = tf.cast(image, tf.float32)/255

# Generate Batch
batch_shape = ((img_height, img_width, 3),(3))
images_batch, labels_batch = tf.train.shuffle_batch([image_float32, label_float32], 
                                                    batch_size = batch_size, 
                                                    capacity = batch_size * 50, 
                                                    min_after_dequeue = batch_size * 10, 
                                                    shapes = batch_shape)

# =============================================================================
# Construct Network
# define functions
def weight_varible(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# paras
W_conv1 = weight_varible([5, 5, 3, 32])
b_conv1 = bias_variable([32])

# conv layer-1
h_conv1 = tf.nn.relu(conv2d(images_batch, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# conv layer-2
W_conv2 = weight_varible([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# full connection
W_fc1 = weight_varible([16 * 16 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 16 * 16 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# dropout

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# output layer: softmax
W_fc2 = weight_varible([1024, 3])
b_fc2 = bias_variable([3])


y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# model training
cross_entropy = -tf.reduce_sum(labels_batch * tf.log(y_conv))

if GradientDescentOptimizer:
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
else:
    if AdamOptimizer:
        train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(labels_batch, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # Start file queue
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    sess.run([images_batch, labels_batch])

    coord.request_stop()
    coord.join(threads)

    for i in range(training_iters):
        # display the result on console
        if i % display_step == 0:
            train_accuacy = accuracy.eval()
            print("step %d, training accuracy %g"%(i, train_accuacy))
        # run the model
        train_step.run()
    print("test accuracy %g"%(accuracy.eval()))

3.更新运行结果

更新后的代码在第 90 步左右调用错误:

step 0, training accuracy 0.5625
step 10, training accuracy 0.6875
step 20, training accuracy 0.703125
step 30, training accuracy 0.625
step 40, training accuracy 0.65625
step 50, training accuracy 0.6875
step 60, training accuracy 0.6875
step 70, training accuracy 0.734375
step 80, training accuracy 0.632812
step 90, training accuracy 0.695312

然后

OutOfRangeError: RandomShuffleQueue '_24_shuffle_batch_3/random_shuffle_queue' is closed and has insufficient elements (requested 128, current size 1)
     [[Node: shuffle_batch_3 = QueueDequeueMany[_class=["loc:@shuffle_batch_3/random_shuffle_queue"], component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](shuffle_batch_3/random_shuffle_queue, shuffle_batch_3/n)]]

Caused by op 'shuffle_batch_3', defined at:
  File "C:\Users\Jeffy\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 223, in <module>
    main()
......

---

老问题:

当我使用 tf.batch() 将标签与图像匹配时，为什么图像标签会粘在第一个样本上？在 Standy 和我的 friend Dong 的大力帮助下，我的老问题得到了解决。非常感谢他们!

1.我的问题

1. 将“shuffle_batch()”函数放在位置 1 或位置 2 的什么位置？ (您可以在下面的代码的 tf.session() 部分中找到它)

2. 如何修改代码使 batch 成功地结合图像和标签，而不是停留在第一个示例上？

2.csv文件

图像名称和相关标签以下列格式(withour header)存储在 label.csv 中:

11219_right,0,1,0
15502_left,0,0,0
14481_right,0,1,0
11032_right,0,0,1
19322_right,0,0,0
......

3.源代码

代码的目的是使用 RNN 作为图像分类器。

CNN结构基于tensorflow示例文件。

您可以专注于读取数据部分并启动图表部分。

# Global Parameters

# Image Size
training_size = 1387
img_height = 64
img_width = 64

# File stream
batch_size = 128

# Training parameter
learning_rate = 0.001
training_iters = 100
keep_prob = 0.5 #dropout keep prob
display_step = 10

AdamOptimizer = 1
GradientDescentOptimizer = 0

# Filepath
csv_filepath = r'C:/Users/Jeffy/OneDrive/Course\NMDA\retinaProject\label.csv'
image_filepath = 'Image_P/'


# import library
import tensorflow as tf
import numpy as np
#=============================================================================
# Read input data

# load csv content
csv_path = tf.train.string_input_producer(['label.csv'])
textReader = tf.TextLineReader()
_, csv_content = textReader.read(csv_path)
im_name, label = tf.decode_csv(csv_content, record_defaults=[[""], [1]])

# load images
im_content = tf.read_file(image_filepath + im_name+'.jpeg')
image = tf.image.decode_jpeg(im_content, channels=3)

def label_3D (label_num):
    label_3D = np.zeros(3)
    if label_num == 0:
        label_3D[0] = 1
    else:
        if label_num == 3:
            label_3D[1] = 1
        else: # label_num == 4
            label_3D[2] = 1
    return label_3D
# =============================================================================
# Construct Network(you can skip this part)

# define functions
def weight_varible(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# paras
W_conv1 = weight_varible([5, 5, 3, 32])
b_conv1 = bias_variable([32])

# conv layer-1
x = tf.Variable(tf.zeros([batch_size, img_width, img_height, 3]))
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# conv layer-2
W_conv2 = weight_varible([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# full connection
W_fc1 = weight_varible([16 * 16 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 16 * 16 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# dropout

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# output layer: softmax
W_fc2 = weight_varible([1024, 3])
b_fc2 = bias_variable([3])


y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_ = tf.Variable(tf.zeros([batch_size, 3]))

# model training
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
if GradientDescentOptimizer:
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
else:
    if AdamOptimizer:
        train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# ==========================================================================
# Lauch the graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # Start file queue
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    images, labels = sess.run([image, label])
    #position1 for tf.train.shuffle_batch() function

    coord.request_stop()
    coord.join(threads)

    for i in range(training_iters):
        #position2 for tf.train.shuffle_batch() function
        batch = tf.train.shuffle_batch([images,label_3D(labels)], batch_size=batch_size,
                               capacity = batch_size * 50, 
                               min_after_dequeue = batch_size * 10,
                               num_threads = 1)

        if i % display_step == 0:
            x = batch[0]
            y_ = batch[1]
            train_accuacy = accuracy.eval()
            print("step %d, training accuracy %g"%(i, train_accuacy))
        x= batch[0]
        y_ = batch[1]
        train_step.run()

`

4.运行结果 - 在 IPython 控制台中

第 0 步，训练精度 0.226562
step 10，训练准确率1
第20步，训练精度1
第30步，训练精度1
步骤40，训练精度1
- 在变量探索中

变量labels和images不变，作为label和image第一个例子，保存在label.csv文件的第一行

因此，我推断读取文件队列卡在了第一行，导致CNN以1的精度快速收敛。

Answer 1

shuffle_batch 接受张量并返回一个张量，因此它是一个tensorflow op，应该放在Graph中。 shuffle_batch docs

我会在您解码单个图像后立即放置它:

image = tf.image.decode_jpeg(im_content, channels=3)
images_batch, labels_batch = tf.train.shuffle_batch([image, label], batch_size, batch_size * 50, batch_size * 10)
# images_batch is now Tensor of shape (batch_size, height, weight, channels)
...
h_conv1 = tf.nn.relu(conv2d(images_batch, W_conv1) + b_conv1)

您现在不需要变量 x 和 y_，并且您在使用 tf.train.shuffle_batch 时不需要手动分配输入。

tf.train.shuffle_batch 接受单个示例的张量并生成整个批处理，这似乎违反直觉，但请记住，您提供给此 op 的张量来自队列，因此 tf .train.shuffle_batch 可以“等待”多个元素(在幕后它实际上使用另一个队列来洗牌和存储中间元素，shuffle_batch 实现是 here )