Tensorflow 2.0 – AttributeError: module 'tensorflow' has no attribute '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))

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)

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

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

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.

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

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()

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 also faced same problem when I first tried Google Colab after updating Windows 10. Then I changed and inserted two lines,

• tf.compat.v1.disable_eager_execution()
• sess = tf.compat.v1.Session()

As a result, everything goes OK

use this:

sess = tf.compat.v1.Session()

if there is an error, use the following

tf.compat.v1.disable_eager_execution()
sess = tf.compat.v1.Session()

If you’re doing it while some imports like,

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

Then I suggest you to follow these steps,
NOTE: For TensorFlow2 and for CPU Process only
Step 1: Tell your code to act as if the compiler is TF1 and disable TF2 behavior, use the following code:

import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

Step 2: While importing libraries, remind your code that it has to act like TF1, yes EVERYTIME.

tf.disable_v2_behavior()
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

Conclusion: This should work, let me know if something goes wrong, also if it is GPU, then do mention to add a backend code for keras. Also, TF2 does not support session there is a separate understanding for that and has been mentioned on TensorFlow, the link is:

TensorFlow Page for using Sessions in TF2

Other major TF2 changes have been mentioned in this link, it is long but please go through it, use Ctrl+F for assistance. Link,

Effective TensorFlow 2 Page Link

import tensorflow._api.v2.compat.v1 as tf
tf.disable_v2_behavior()

For Tensorflow 2.0 and later, try this.

import tensorflow as tf

tf.compat.v1.disable_eager_execution()

a = tf.constant(5)
b = tf.constant(6)
c = tf.constant(7)
d = tf.multiply(a,b)
e = tf.add(c,d)
f = tf.subtract(a,c)

with tf.compat.v1.Session() as sess:
  outs = sess.run(f)
  print(outs)

It is not easy as you think, running TF 1.x with TF 2.x environment I found some errors and need to reviews of some variables usages when I fixed the problems on the neuron networks on the Internet. Transform to TF 2.x is better idea.
( Easier and adaptive )

TF 2.X

while not done:
    next_obs, reward, done, info = env.step(action) 
        env.render()
    img = tf.keras.preprocessing.image.array_to_img(
            img,
            data_format=None,
            scale=True
    )
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    predictions = model_self_1.predict(img_array) ### Prediction

### Training: history_highscores = model_highscores.fit(batched_features, epochs=1 ,validation_data=(dataset.shuffle(10))) # epochs=500 # , callbacks=[cp_callback, tb_callback]    

TF 1.X

with tf.compat.v1.Session() as sess:
    saver = tf.compat.v1.train.Saver()
    saver.restore(sess, tf.train.latest_checkpoint(savedir + '\invader_001'))
    train_loss, _ = sess.run([loss, training_op], feed_dict={X:o_obs, y:y_batch, X_action:o_act})
    
    for layer in mainQ_outputs: 
                model.add(layer)
        model.add(tf.keras.layers.Flatten() )
        model.add(tf.keras.layers.Dense(6, activation=tf.nn.softmax))
        predictions = model.predict(obs) ### Prediction

    
### Training: summ = sess.run(summaries, feed_dict={X:o_obs, y:y_batch, X_action:o_act})

To work with TensorFlow 2.x, you should use tf.compat.v1.Session instead of tf.Session.