FLAGS = No value?

Question

I am new to python and tensorFlow and I follow this MNIST tutorial in tensorFlow documentation.

In the first bit, I do not know what FLAGS = None is. I searched on Google and returned empty. Does this seem too obvious to others?

from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf FLAGS = None def main(_): # Import data mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) 

So what is FLAGS and how is it used? e.g. FLAGS.data_dir

Any help would be appreciated!

Answer 1

Initializing FLAGS=None is just a way to initialize a global constant. If left as is, this will lead to an error in main , since None does not have any attributes.

But if you install through argparse parser , as shown in more complete examples, it is a simple object with many attributes. main assumes that one of these attributes is called data_dir .

If after

 FLAGS, unparsed = parser.parse_known_args() print(FLAGS) 

you should see Namespace(data_dir='a directory', ....) , where the value for data_dir was parsed from the command line.

Answer 2

This was the complete code you are looking at: I will explain:

 from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf FLAGS = None #Adds a default value to FLAGS def main(_): #Everything inside the function is not checked until it called mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) #FLAGS is not None anymore because it got changed below x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.matmul(x, W) + b y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() # Train for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # Test trained model correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', help='Directory for storing input data') FLAGS, unparsed = parser.parse_known_args() #Here it changed the value of FLAGS to the first thing returned from parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) #runs the app (calling main) 

what happened is that FLAGS have changed here: FLAGS, unparsed = parser.parse_known_args()