[python] Tensorflow 2.0 - AttributeError: module 'tensorflow' has no attribute 'Session'

When I am executing the command sess = tf.Session() in Tensorflow 2.0 environment, I am getting an error message as below:

Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: module 'tensorflow' has no attribute 'Session'

System Information:

• OS Platform and Distribution: Windows 10
• Python Version: 3.7.1
• Tensorflow Version: 2.0.0-alpha0 (installed with pip)

Steps to reproduce:

Installation:

1. pip install --upgrade pip
2. pip install tensorflow==2.0.0-alpha0
3. pip install keras
4. pip install numpy==1.16.2

Execution:

1. Execute command: import tensorflow as tf
2. Execute command: sess = tf.Session()

Same problem occurred for me

import tensorflow as tf
hello = tf.constant('Hello World ') 
sess = tf.compat.v1.Session()    *//I got the error on this step when I used 
                                   tf.Session()*
sess.run(hello)

Try replacing it with tf.compact.v1.Session()

I faced this problem when I first tried python after installing windows10 + python3.7(64bit) + anacconda3 + jupyter notebook.

I solved this problem by refering to "https://vispud.blogspot.com/2019/05/tensorflow200a0-attributeerror-module.html"

I agree with

I believe "Session()" has been removed with TF 2.0.

I inserted two lines. One is tf.compat.v1.disable_eager_execution() and the other is sess = tf.compat.v1.Session()

My Hello.py is as follows:

import tensorflow as tf

tf.compat.v1.disable_eager_execution()

hello = tf.constant('Hello, TensorFlow!')

sess = tf.compat.v1.Session()

print(sess.run(hello))

Tensorflow 2.x support's Eager Execution by default hence Session is not supported.

Using Anaconda + Spyder (Python 3.7)

[code]

import tensorflow as tf
valor1 = tf.constant(2)
valor2 = tf.constant(3)
type(valor1)
print(valor1)
soma=valor1+valor2
type(soma)
print(soma)
sess = tf.compat.v1.Session()
with sess:
    print(sess.run(soma))

[console]

import tensorflow as tf
valor1 = tf.constant(2)
valor2 = tf.constant(3)
type(valor1)
print(valor1)
soma=valor1+valor2
type(soma)
Tensor("Const_8:0", shape=(), dtype=int32)
Out[18]: tensorflow.python.framework.ops.Tensor

print(soma)
Tensor("add_4:0", shape=(), dtype=int32)

sess = tf.compat.v1.Session()

with sess:
    print(sess.run(soma))
5

For TF2.x, you can do like this.

import tensorflow as tf
with tf.compat.v1.Session() as sess:
    hello = tf.constant('hello world')
    print(sess.run(hello))

>>> b'hello world

If this is your code, the correct solution is to rewrite it to not use Session(), since that's no longer necessary in TensorFlow 2

If this is just code you're running, you can downgrade to TensorFlow 1 by running

pip3 install --upgrade --force-reinstall tensorflow-gpu==1.15.0 

(or whatever the latest version of TensorFlow 1 is)

try this

import tensorflow as tf

tf.compat.v1.disable_eager_execution()

hello = tf.constant('Hello, TensorFlow!')

sess = tf.compat.v1.Session()

print(sess.run(hello))

import tensorflow as tf
sess = tf.Session()

this code will show an Attribute error on version 2.x

to use version 1.x code in version 2.x

try this

import tensorflow.compat.v1 as tf
sess = tf.Session()

TF v2.0 supports Eager mode vis-a-vis Graph mode of v1.0. Hence, tf.session() is not supported on v2.0. Hence, would suggest you to rewrite your code to work in Eager mode.

TF2 runs Eager Execution by default, thus removing the need for Sessions. If you want to run static graphs, the more proper way is to use tf.function() in TF2. While Session can still be accessed via tf.compat.v1.Session() in TF2, I would discourage using it. It may be helpful to demonstrate this difference by comparing the difference in hello worlds:

TF1.x hello world:

import tensorflow as tf
msg = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(msg))

TF2.x hello world:

import tensorflow as tf
msg = tf.constant('Hello, TensorFlow!')
tf.print(msg)

For more info, see Effective TensorFlow 2