I am reading Ganegedara‘s NLP with Tensorflow. The introduction to input pipieline has the following example
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()
Which has the following output:
========== 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]]
It also generates a lot of warning about Queue-based input pipelines being deprecated and suggests using the tf.data module instead.
This is my attempt to using tf.data module
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()
Which produces this output instead:
========== 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]]
It looks like the original code samples 3 rows everytime, and my attempt with tf.data samples 3 columns. Why is this and how can I fix my code and make it equivalent to the original?
I ended up finding my answer through someone else's code, which was inquiring about the poor performance of TextLineDataset and decode_csv.
Here's my code that uses tf.data to do something similar to the code on Ganegedara‘s book:
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()
来源：https://stackoverflow.com/questions/53571432/replacing-queue-based-input-pipelines-with-tf-data