Modulenotfounderror no module named tensorflow Keras

The common error in tensorflow is ‘Modulenotfounderror no module named tensorflow.keras‘, In this tutorial, I will explain different approaches to resolving this error.

Additionally, you will see different kinds of errors related to this error and learn how to resolve those errors, too.

These are Modulenotfounderror no module named ‘tensorflow.keras.engine’ and Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’.

By the end of this tutorial, you can handle these errors very smoothly.

I’ll be covering the following topics:

  • Modulenotfounderror no module named tensorflow Keras
  • Modulenotfounderror no module named ‘tensorflow.keras.layers.experimental.preprocessing’
  • Modulenotfounderror no module named ‘tensorflow.keras.engine’
  • Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’
  • Modulenotfounderror: No Module Named ‘tensorflow.keras.wrappers’

Modulenotfounderror no module named tensorflow Keras

First, you must know what the error Modulenotfounderror no module named tensorflow.keras means.

It means Python doesn’t find the submodule named keras of the TensorFlow library; there can be several reasons for this error.

The first mistake you can make is importing the module incorrectly if you type something like the command below.

import tensorflow.Keras
Modulenotfounderror no module named tensorflow Keras dut to Wrong package name

You get the error Modulenotfounderror no module named tensorflow Keras because you specify something like this ‘tensorflow.Keras’ while importing the Keras module, but here in Keras, the k should be small like this tensorflow.keras.

So, the complete fix for that error is given below.

import tensorflow.keras

Executing the above doesn’t show an error and imports the module keras from the TensorFlow in your environment.

The second mistake can be different versions of TensorFlow and Keras; if you use an old version of TensorFlow, like 1.1 or below, you may have to install Keras, too.

So, for example, if you use TensorFlow 1 and the Karas version is 1.1, you will get an error ‘no module named tensorflow Keras‘.

Here, you need to keep the same version of TensorFlow and Keras. But remember, after Keras, version 2.0 has become part of tensorflow since version tf=2.0.

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This means you don’t need to install or access the Keras separately when you install TensorFlow, as shown below.

import keras

model = keras.Sequential()

But if you are using the latest version of TensorFlow, like above 2.0, then you can follow the below procedure to access the Keras and define the model.

So, when importing the module, make sure you are using correct typos and spelling.

Let’s create a simple model using the keras module.

import tensorflow as tf
from tensorflow.keras import layers

new_model = tf.keras.Sequential(
    [
        layers.Dense(3, activation="relu", name="layer1"),
        layers.Dense(1, activation="relu", name="layer2"),
        layers.Dense(2, name="layer3"),
    ]
)
tens_1 = tf.ones((3, 3))
tens_2 = new_model(tens_1)
new_model.summary()
Resolving Modulenotfounderror no module named tensorflow Keras

Look, new_model is created using the tensorflow.keras module; especially using this model, you can create or define neural networks or deep learning models in TensorFlow.

A third mistake is importing the Keras module from TensorFlow on an old version of TensorFlow, like tensorflow==1.14.0, so make sure you have installed the latest version of TensorFlow.

There is another form of this error is modulenotfounderror: no module named ‘tensorflow.keras’; ‘tensorflow’ is not a package, so apply the above approach.

To ensure you have installed the latest version of Tensorflo

Read: Attributeerror: module ‘tensorflow’ has no attribute ‘scalar_summary’

Modulenotfounderror no module named ‘tensorflow.keras.engine’

You get this error because you are trying to access the internal component of Keras directly. Also, there is no submodule named tensorflow.keras.engine.

So execute; you get that error when you execute the code below.

import tensorflow.keras.engine
Modulenotfounderror no module named ‘tensorflow.keras.engine’

You get that error because all the submodules under keras.engine are moved to different modules onwards tensorflow 2.xxx.

The solution to the above error is given below in the code.

from tensorflow.keras.layers import Layer, InputSpec

result= tf.keras.layers.InputSpec(dtype=None, shape=(2,2),ndim=None)
print(result)

Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’

First, the error means no module named keras.layers.merge in the latest version of TensorFlow.

The purpose of this keras.layers.merge is converting the multiple layers into single, which means multidimensional tensors are converted into single tensors.

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In convolution, 3D layers are converted into 1D layers, reducing memory. Also, the model works best on the 1D layers or 1D tensors.

Let me show you the possible way you get this error when you import it, as shown below.

from tensorflow.keras.layers.merge import concatenate
Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’

So, I have the following two approaches to resolve this error.

The first approach is to import the concatenate from the tensorflow.layers instead of tensorflow.layers.merge as shown below.

from tensorflow.keras.layers import concatenate

print(concatenate)
Resolving using First Approach Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’

Look at the picture: The concatenate was successfully imported from the module tensorflow.keras.layers.

The second approach is about using the keras framework directly, so use the command below to import the concatenate from keras.layers.

from keras.layers import concatenate

print(concatenate)
Resolving using Second Approach Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’

The approach depends on you; using tensorflow, use the first approach; otherwise, the second would be best.

Now, I will show a complete example of using concatenate() from the tensorflow.keras.layers. In this example, you will use the tf.keras.layers.concatenate() function to concatenate two input layers.

Syntax:

Let’s look at the syntax and understand how to use the tf.keras.layers.concatenate() function in TensorFlow.

tf.keras.layers.concatenate(
    inputs, axis=-1, **kwargs
)
  • Where it consists of a few parameter
    • inputs: This parameter defines the list of input tensors.
    • axis: By default, it takes a -1 value and defines the concatenation axis.
    • **kwargs: This parameter indicates the standard layer keyword parameters.

Now, test with an example.

import tensorflow as tf
import numpy as np
from keras.layers import concatenate
USA_based_random_num = np.arange(20).reshape(2, 2, 5)
print(USA_based_random_num )
sample_num = np.arange(20, 30).reshape(2, 1, 5)
print(sample_num)

result= tf.keras.layers.concatenate([USA_based_random_num , sample_num],axis=1)
print(result)
Complete Solution Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’

Here, the above created two input layers, USA_based_random_num and sample_num. They are not real layers but, for example, considered as layers.

These two layers are now passed to function concatenate() with axis=1. Then layers are flattened along axis 1, or, in other words, one array dimension is created from a multi-dimension array.

This is how to resolve the error Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’.

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Read: How to convert dictionary to tensor tensorflow

Modulenotfounderror: No Module Named ‘tensorflow.keras.wrappers’

The error Modulenotfounderror: No Module Named ‘tensorflow.keras.wrappers’ means there are no subpackages called wrappers in the submodule ‘tensorflow.keras’.

I have two approaches to solve this error, which are discussed below.

You get an error because you may import some of the below functions.

from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
Modulenotfounderror No Module Named tensorflow.keras.wrappers

Here, within tensorflow.keras accessing the wrappers.scikit_learn. This keras.wrappers thing doesn’t exist. But if you are using a tensorflow version like 2.15 or above, then the module keras.wrappers have been removed, so, normally, you get the error.

If you still want to use it, I suggest you uninstall the current version of Tensorflow and install version 2.15, as shown below.

pip uninstall tensorflow
pip install tensorflow==2.12.0
Resolving Modulenotfounderror No Module Named tensorflow.keras.wrappers Install Old version of Tensorflow

Using the above command, uninstall the currently installed tensorflow and install the specific version 2.12.0.

As shown below, import the KerasRegressor from the tensorflow.keras.wrappers.scikit_learn.

from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
print(KerasRegressor)
Solution Modulenotfounderror No Module Named tensorflow.keras.wrappers

So, an alternative approach is to install the scikeras package and then import KerasRegressor or KerasClassifier, etc.

Follow the steps below to install the package.

!pip install scikeras

Then again, use the import statement as shown below.

from scikeras.wrappers import KerasRegressor
print(KerasRegressor)
Resolving Modulenotfounderror No Module Named tensorflow.keras.wrappers

Look, I successfully imported the KerasRegressor from the scikeras. It depends on which approach you adopt.

Conclusion

This tensorflow tutorial taught you about different solutions to the error ‘Modulenotfounderror no module named tensorflow.keras‘,

Additionally, you learned how to solve these errors Modulenotfounderror no module named ‘tensorflow.keras.engine’ and Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’.

Also, take a look at some more Python TensorFlow tutorials.