CODEWITHSUNDEEP
pythonmachine-learningtensorflow
Uvuvwevwevwe
Uvuvwevwevwe

Reputation: 1041

Different outcomes when using tf.Variable() and tf.get_variable()

I'm trying to get familiar with TensorFlow framework from this site by playing around with Linear Regression (LR). The source code for LR can be found here, with the name 03_linear_regression_sol.py.

Generally, the defined model for LR is Y_predicted = X * w + b, where

For w and b, in the sample code, they are defined as following

w = tf.Variable(0.0, name='weights')
b = tf.Variable(0.0, name='bias')

And I changed these two lines of code a little bit as following

w = tf.get_variable('weights', [], dtype=tf.float32)
b = tf.get_variable('bias', [], dtype=tf.float32)

For this experiment, I got two different total_loss/n_samples for those two versions. More specifically, in the original version, I got a deterministic result at anytime, 1539.0050282141283. But, in the modified version, I got undeterministic results at different running time, for example, total_loss/n_samples could be 1531.3039793868859, 1526.3752814714044, ... etc.

What is the difference between tf.Variable() and tf.get_variable()?

Upvotes: 2

Views: 470

Answers (1)

Maxim
Maxim

Reputation: 53758

tf.Variable accepts an initial value upon creation (a constant), this explains deterministic results when you use it.

tf.get_variable is slightly different: it has an initializer argument, by default None, which is interpreted like this:

If initializer is None (the default), the default initializer passed in the variable scope will be used. If that one is None too, a glorot_uniform_initializer will be used.

Since you didn't pass an initializer, the variable got uniform random initial value.

Upvotes: 4

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