用 tf.data 替换基于队列的输入管道

Question

我正在使用 Tensorflow 阅读 Ganegedara 的 NLP。输入管道的介绍有以下例子

import tensorflow as tf
import numpy as np
import os

# Defining the graph and session
graph = tf.Graph() # Creates a graph
session = tf.InteractiveSession(graph=graph) # Creates a session

# The filename queue
filenames = ['test%d.txt'%i for i in range(1,4)]
filename_queue = tf.train.string_input_producer(filenames, capacity=3, shuffle=True,name='string_input_producer')

# check if all files are there
for f in filenames:
    if not tf.gfile.Exists(f):
        raise ValueError('Failed to find file: ' + f)
    else:
        print('File %s found.'%f)

# Reader which takes a filename queue and 
# read() which outputs data one by one
reader = tf.TextLineReader()

# ready the data of the file and output as key,value pairs 
# We're discarding the key
key, value = reader.read(filename_queue, name='text_read_op')

# if any problems encountered with reading file 
# this is the value returned
record_defaults = [[-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0], [-1.0]]

# decoding the read value to columns
col1, col2, col3, col4, col5, col6, col7, col8, col9, col10 = tf.decode_csv(value, record_defaults=record_defaults)
features = tf.stack([col1, col2, col3, col4, col5, col6, col7, col8, col9, col10])

# output x is randomly assigned a batch of data of batch_size 
# where the data is read from the txt files
x = tf.train.shuffle_batch([features], batch_size=3,
                           capacity=5, name='data_batch', 
                           min_after_dequeue=1,num_threads=1)

# QueueRunner retrieve data from queues and we need to explicitly start them
# Coordinator coordinates multiple QueueRunners
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=session)

# Executing operations and evaluating nodes in the graph
tf.global_variables_initializer().run() # Initialize the variables

# Calculate h with x and print the results for 5 steps
for step in range(5):
    x_eval = session.run(x) 
    print('========== Step %d =========='%step)
    print('Evaluated data (x)')
    print(x_eval)
    print('')

# We also need to explicitly stop the coordinator 
# otherwise the process will hang indefinitely
coord.request_stop()
coord.join(threads)
session.close()

输出如下：

========== Step 0 ==========
Evaluated data (x)
[[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]
 [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]
 [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]

========== Step 1 ==========
Evaluated data (x)
[[1.  0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1]
 [1.  0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1]
 [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]

========== Step 2 ==========
Evaluated data (x)
[[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]
 [1.  0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1]
 [1.  0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1]]

========== Step 3 ==========
Evaluated data (x)
[[0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]
 [0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]
 [0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]]

========== Step 4 ==========
Evaluated data (x)
[[0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]
 [1.  0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1]
 [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]

它还会生成大量关于不推荐使用基于队列的输入管道的警告，并建议改用 tf.data 模块。

这是我尝试使用 tf.data 模块

import tensorflow as tf
import numpy as np
import os

graph = tf.Graph()
session = tf.InteractiveSession(graph=graph)
filenames = ['test%d.txt'%i for i in range(1,4)]
record_defaults = [[-1.0]] * 10
features = tf.data.experimental.CsvDataset(filenames, record_defaults).batch(batch_size=3).shuffle(buffer_size=5)
x = features.make_one_shot_iterator().get_next()
x = tf.convert_to_tensor(x)
# Executing operations and evaluating nodes in the graph
tf.global_variables_initializer().run() # Initialize the variables
# Calculate h with x and print the results for 5 steps
for step in range(5):
    x_eval = session.run(x)
    print('========== Step %d =========='%step)
    print('Evaluated data (x)')
    print(x_eval)
    print('')
session.close()

这会产生这个输出：

========== Step 0 ==========
Evaluated data (x)
[[0.1 0.1 0.1]
 [0.2 0.2 0.2]
 [0.3 0.3 0.3]
 [0.4 0.4 0.4]
 [0.5 0.5 0.5]
 [0.6 0.6 0.6]
 [0.7 0.7 0.7]
 [0.8 0.8 0.8]
 [0.9 0.9 0.9]
 [1.  1.  1. ]]

========== Step 1 ==========
Evaluated data (x)
[[0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]
 [0.1 0.1 0.1]]

========== Step 2 ==========
Evaluated data (x)
[[1.  1.  1. ]
 [0.9 0.9 0.9]
 [0.8 0.8 0.8]
 [0.7 0.7 0.7]
 [0.6 0.6 0.6]
 [0.5 0.5 0.5]
 [0.4 0.4 0.4]
 [0.3 0.3 0.3]
 [0.2 0.2 0.2]
 [0.1 0.1 0.1]]

========== Step 3 ==========
Evaluated data (x)
[[0.1 0.1 0.1]
 [0.2 0.2 0.1]
 [0.3 0.3 0.1]
 [0.4 0.4 0.1]
 [0.5 0.5 0.1]
 [0.6 0.6 0.1]
 [0.7 0.7 0.1]
 [0.8 0.8 0.1]
 [0.9 0.9 0.1]
 [1.  1.  0.1]]

========== Step 4 ==========
Evaluated data (x)
[[0.1 1.  1. ]
 [0.1 0.9 0.9]
 [0.1 0.8 0.8]
 [0.1 0.7 0.7]
 [0.1 0.6 0.6]
 [0.1 0.5 0.5]
 [0.1 0.4 0.4]
 [0.1 0.3 0.3]
 [0.1 0.2 0.2]
 [0.1 0.1 0.1]]

看起来原始代码每次采样 3 行，而我对 tf.data 的尝试采样 3 列。为什么会这样？如何修复我的代码并使其与原始代码等效？

Answer 1

我最终通过别人的代码找到了答案，那是inquiring about the poor performance of TextLineDataset and decode_csv。

这是我的代码，它使用 tf.data 执行类似于 Ganegedara 书中的代码：

import tensorflow as tf
import numpy as np
import os

graph = tf.Graph()
session = tf.InteractiveSession(graph=graph)
filenames = ['test%d.txt'%i for i in range(1,4)]

record_defaults = [[-1.0]] * 10

features = tf.data.TextLineDataset(filenames=filenames)

def parse_csv(line):
        cols_types = [[-1.0]] * 10  # all required
        columns = tf.decode_csv(line, record_defaults=cols_types)
        return tf.stack(columns)

features = features.map(parse_csv).batch(batch_size=3).shuffle(buffer_size=5)

x = features.make_one_shot_iterator().get_next()
x = tf.convert_to_tensor(x)
W = tf.Variable(tf.random_uniform(shape=[10,5], minval=-0.1,maxval=0.1, dtype=tf.float32),name='W') 
b = tf.Variable(tf.zeros(shape=[5],dtype=tf.float32),name='b')
h = tf.nn.sigmoid(tf.matmul(x,W) + b) # Operation to be performed

tf.global_variables_initializer().run() # Initialize the variables

# Calculate h with x and print the results for 5 steps
for step in range(5):
    x_eval, h_eval = session.run([x,h]) 
    print('========== Step %d =========='%step)
    print('Evaluated data (x)')
    print(x_eval)
    print('Evaluated data (h)')
    print(h_eval)
    print('')
session.close()