CODEWITHSUNDEEP
pythonpython-3.xtensorflowpython-3.6
Benny K
Benny K

Reputation: 2087

Upgrading from tensorflow 1.x to 2.0

I am new to tensorflow. Have tried this simple example:

import tensorflow as tf
sess = tf.Session()
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
z = x + y
print(sess.run(z, feed_dict={x: 3.0, y: 4.5}))

and got some warnings The name tf.Session is deprecated. Please use tf.compat.v1.Session instead. and the right answer - 7.5

After reading here, I understand that the warnings are due to upgrading from tf 1.x to 2.0, the steps described are "simple" but they don't give any example....

I have tried:

@tf.function
def f1(x1, y1):
    return tf.math.add(x1, y1)


print(f1(tf.constant(3.0), tf.constant(4.5)))
  1. Is my code correct (in the sense defined in the link)?
  2. Now, I am getting Tensor("PartitionedCall:0", shape=(), dtype=float32) as output, how can I get the actual value?

Upvotes: 2

Views: 2205

Answers (2)

gorjan
gorjan

Reputation: 5575

You code is indeed correct. The warning that you get indicates that as from Tensorflow 2.0, tf.Session() won't exist in the API. Therefore, if you want you code to be compatible with Tensorflow 2.0, you should use tf.compat.v1.Session instead. So, just change this line:

sess = tf.Session()

To:

sess = tf.compat.v1.Session()

Then, even if you update Tensorflow from 1.xx to 2.xx, your code would execute in the same way. As for the code in Tensorflow 2.0:

@tf.function
def f1(x1, y1):
    return tf.math.add(x1, y1)

print(f1(tf.constant(3.0), tf.constant(4.5)))

it is fine if you run it in Tensorflow 2.0. If you want to run the same code, without installing Tensorflow 2.0, you can do the following:

import tensorflow as tf
tf.enable_eager_execution()

@tf.function
def f1(x1, y1):
    return tf.math.add(x1, y1)

print(f1(tf.constant(3.0), tf.constant(4.5)).numpy())

The reason for this is because the default way of executing Tensorflow operations starting from Tensorflow 2.0 is in eager mode. The way of activating eager mode in Tensorflow 1.xx, is to enable it right after the import of Tensorflow, as I am doing it in the example above.

Upvotes: 2

Alexander Ejbekov
Alexander Ejbekov

Reputation: 5960

Your code is correct as per Tensorflow 2.0. Tensorflow 2.0 is even more tightly bonded with numpy so if you want to get the result of the operation, you could use the numpy() method:

print(f1(tf.constant(3.0), tf.constant(4.5)).numpy())

Upvotes: 1

Related Questions