使用tf.Variable（）和tf.get_variable（）时的结果不同

Question

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

• w and b are parameters ( tf.Variable )
• Y_predicted and X are training data ( placeholder )

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() ?

Answer 1

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.