How to fix "AttributeError: module 'tensorflow' has no attribute 'get_default_graph'"?

Turns out I was using the wrong version (2.0.0a0), so i reset to the latest stable version (1.13.1) and it works.

Downgrading will fix the problem but if you want to use latest version, you must try this code:
from tensorflow import keras and ‘from tensorflow.python.keras import backend as k
That’s work for me

This has also happend to me. The reason is your tensorflow version. Try to get older version of tensorflow. Another problem can be you have a python script named tensorflow.py in your project.

Yes, the code is not working with this version of tensorflow tensorflow == 2.0.0 . moving to version older than 2.0.0 will help.

for latest tensorflow 2 replace the above code with below code with some changes

for details check keras documentation:
https://www.tensorflow.org/guide/keras/overview

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential, load_model

model = tf.keras.Sequential()
model.add(layers.Dense(32, input_dim=784))
model.add(layers.Activation('relu'))
model.add(layers.LSTM(17))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(0.01), metrics=['accuracy'])

Use the following:

tf.compat.v1.disable_eager_execution()
print(tf.compat.v1.get_default_graph())

It works for tensorflow 2.0

I had the same problem. I tried

from tensorflow.keras.models import Sequential

and

from keras.models import Sequential

none of them works. So I update tensorflow, keras and python:

$conda update python
$conda update keras
$conda update tensorflow

or

pip install --upgrade tensorflow
pip install --upgrade keras
pip install --upgrade python

My tensorflow version is 2.1.0; my keras version is 2.3.1; my python version is 3.6.10.
Nothing works until I unintall keras and reinstall keras:

pip uninstall keras
pip install keras --upgrade

For tf 2.1.0 I used tf.compat.v1.get_default_graph() – e.g:

import tensorflow as tf
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)
tf.compat.v1.keras.backend.set_session(sess)

To solve the problem I used the code below:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy

Assuming people referring to this thread will be using more and more tensorflow 2:

Tensorflow 2 integrates further keras api, since keras is designed/developed very wisely. The answer is very easy if you are using tensorflow 2, as described also here:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, LSTM

model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
model.add(LSTM(17))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss=tensorflow.keras.losses.binary_crossentropy, optimizer=tensorflow.keras.optimizers.Adam(), metrics=['accuracy'])

and that’s how you change one would use something like MNIST from keras official page with just replacing tensorflow.keras instead of keras and runnig it also on gpu;

from __future__ import print_function
import tensorflow
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K

batch_size = 1024
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)
y_test = tensorflow.keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
             activation='relu',
             input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=tensorflow.keras.losses.categorical_crossentropy,
          optimizer=tensorflow.keras.optimizers.Adadelta(),
          metrics=['accuracy'])

model.fit(x_train, y_train,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

YES, it won’t work since you are using the updated version of tensorflow ie tensorflow == 2.0 , the older version of tensorflow might help.
I had the same problem but i fixed it using the following code.

try:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout

instead:

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

Replace all keras.something.something with tensorflow.keras.something, and use:

import tensorflow as tf
from tensorflow.keras import backend as k

For TensorFlow 2.0, use keras bundled with tensorflow.

try replacing keras.models with tensorflow.python.keras.models or tensorflow.keras.models:

from tensorflow.python.keras.models import Sequential

from tensorflow.python.keras.layers.core import Dense, Activation

This should solve the problem.

Please try to be concise!

First –>

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

Then –>

model = keras.Sequential(
    [
        layers.Dense(layers.Dense(32, input_dim=784)),
        layers.Dense(activation="relu"),
        layers.Dense(LSTM(17))

    ]
)
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(0.01), metrics=['accuracy'])

and voila!!

It occurs due to changes in tensorflow version ::
Replace

tf.get_default_graph()

by

tf.compat.v1.get_default_graph()

!pip uninstall tensorflow 
!pip install tensorflow==1.14

this worked for me… working on hrnetv2.. ty

This worked for me. Please use the below import

from tensorflow.keras.layers import Input

To resolve version issues in TensorFlow, it’s a good idea to use this below technique to import v1 (version 1 or TensorFlow 1. x) and we also can disable the TensorFlow 2. x behaviors.

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

You can refer to the following link to check the mapping between Tensorflow 1. x and 2. x