tf.random_uniform: Generate A Random Tensor In Tensorflow

We import TensorFlow as tf.

import tensorflow as tf

Then we print out the TensorFlow version.

tf.__version__

We see that we're using 1.0.1.

We're going to create a Python variable that uses the TensorFlow random uniform functionality.

rand_tensor_ex = tf.random_uniform([1, 2, 3], minval=0, maxval=1, dtype=tf.float32, seed=None, name=None)

We're going to create a tensor that’s 1x2x3 with a minimum value of 0 and a max value of 1, and the data type that we're going to use is tf.float32.

We're not going to use a seed and we're not going to give it a name.

We're going to use this Python variable to show what happens every time you run a TensorFlow session.

This time, we're going to do the same, tf.random_uniform, but we're going to put it into a TensorFlow variable.

random_tensor_var_ex = tf.Variable(tf.random_uniform([2, 3, 4], minval=0, maxval=1, dtype=tf.float32, seed=None, name=None))

What this will do is once it's run once, it'll store that variable in the session regardless of how many times we run the TensorFlow session.

We do a tf.random_uniform.

This time, we're going to make our tensor 2x3x4, a min value of 0, a max value of 1.

Data type again is tf.float32 with no seed and no name.

So the only difference between the Python variable, rand_tensor_ex, and random_tensor_var_ex is that we are putting this into a TensorFlow variable, whereas this, we're just running the TensorFlow random uniform functionality and putting it into a Python variable.

Next, we create a TensorFlow operation that initializes all the global variables in the graph.

init_var = tf.global_variables_initializer()

Then we define a session Python variable that's going to contain our TensorFlow session.

sess = tf.Session()

Now that we have a session, we can initialize all the variables.

sess.run(init_var)

What we're going to do is we’re going to print our random Python variable and our random TensorFlow variable to show you what happens every time you run a session.

So with the first one, it's not in a TensorFlow variable, so what we expect it to do is generate new numbers every time.

With the second one, random_tensor_var_ex, we expect it to run once the first time it’s run inside of the session and then it’s going to stay the same.

So we print the sess.run(rand_tensor_ex) and we see that it is in fact a tensor that is 1x2x3.

So we print it the first time.

print(sess.run(rand_tensor_ex))

We print it again and see that it's completely different.

print(sess.run(rand_tensor_ex))

So here, we have 0.48.

Here, we have 0.42.

All of the entries in this tensor are completely different.

So even though we have defined it and it is a Python variable, every time the TensorFlow session runs, it gives us a different result because it’s running this operation every time.

Now, we're going to print the result of the random_tensor_var_ex.

print(sess.run(random_tensor_var_ex))

We see that it is a 2x3x4 tensor.

And if we print it again:

print(sess.run(random_tensor_var_ex))

we see that it's the exact same value – 0.77 here, 0.77 here; 0.7666 there, 0.7666 there.

So we ran the session twice – once here and once here.

But because we have saved this uniform distribution tensor that we generated into a TensorFlow variable, it stayed with us throughout the running of the sessions, whereas in our rand_tensor_ex, we used the TensorFlow random uniform functionality but because we didn’t save it into a TensorFlow variable, every time we ran it, it generated a different value.

So when you create a random tensor in TensorFlow that’s going to be used across multiple TensorFlow session runs, you want to make sure that you are slotting it into a TensorFlow variable.

That way, it remains the same across all the operations that you do in a TensorFlow session.

The final thing we do is we close the TensorFlow session to release all the TensorFlow resources we used with the session as they're no longer required.

sess.close()