Arguments to tensorflow session.run() - do you pass operations?

Question

I'm following this tutorial for tensorflow:

I'm trying to understand the arguments to tf.session.run(). I understand that you have to run operations in a graph in a session.

Is train_step passed in because it encapsulates all the operations of the network in this particular example? I'm trying to understand why I don't need to pass any other variables to the session like cross_entropy.

sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

Here is the full code:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

x = tf.placeholder(tf.float32, [None, 784])

W = tf.Variable(tf.zeros([784, 10]))

b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.InteractiveSession()

tf.global_variables_initializer().run()

for _ in range(10):
    batch_xs, batch_ys = mnist.train.next_batch(100)

    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

    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}))

Answer 1

In a TensorFlow Session tf.Session, you want to run (or execute) the optimizer operation (in this case it is train_step). The optimizer minimizes your loss function (in this case cross_entropy), which is evaluated or computed using the model hypothesis y.

In the cascade approach, the cross_entropy loss function minimizes the error made when computing y, so it finds the best values of the weights W that when combined with x accurately approximates y.

So using a TensorFlow Session object tf.Session as sess we run the optimizer train_step, which then evaluates the entire Computational Graph.

sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

Because the cascade approach ultimately calls cross_entropy which makes use of the placeholders x and y, you have to use the feed_dict to pass data to those placeholders.

Answer 2

As you mentioned, Tensorflow is used to build a graph of operations. Your train_step operation (i.e. "minimize by gradient descent") is connected/depends on the result of cross_entropy. cross_entropy itself relies on the results of y (softmax operation) and y_ (data assignment); etc.

When you are calling sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}), you are basically asking Tensorflow "run all the operations leading to train_step, and return its result (with x = batch_xs and y = batch_ys for input)". So yes, Tensorflow will itself go through your graph backward to figure out the operation/input dependencies for train_step, then execute all these operations forward, to return what you asked.