Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

If you encounter errors like Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’.

Then, in this tutorial, I will explain how to resolve this error using the tensorflow and Keras framework.

You will first explain the error and why it can occur; you will see how to solve it and ensure you won’t get this error again.

Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

The error Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’ means when you try to import a module tensorflow.keras.utils.np_utils that either does not exist or has been moved in the version of Tensorflow you are using.

If you are using TensorFlow version 1.x, then you may not get this error, but if you are using the same code and updating the TensorFlow to the latest version, like 2.x, then you get the error.

The reason for this error is that from tensorflow version 2.x, many modules or functions have been reorganised or moved, so the way to import them has changed compared to tensorflow version 1.x.

The np_utils is used for categorical encodings, such as converting class vectors to binary class matrices.

For example, you get an error if you try to import the module, as shown below.

import tensorflow.keras.utils.np_utils
Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

Now, you can see the error; the above import is just an example of how the error can appear.

So, if you get the same error, two approaches exist to resolve that.

Let’s begin with the first one: use the to_categorical function directly from the tensorflow.keras.utils, not the np_utils.

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A small example is given below.

from tensorflow.keras.utils import to_categorical

y_data = [2, 5, 7, 1]
y_binary_data = to_categorical(y_data)
print(y_binary_data)
Resolving using First Method Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

Imported the to_categorical successfully and converted the class to categorical.

Next, the approach involves using the Keras Library; again, don’t use np_utils directly import to_categorical from keras.utils as shown below.

from keras.utils import to_categorical
y_class = [2, 5, 7, 1]
y_binary = to_categorical(y_class)
print(y_binary)
Resolving using Second Method Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

Imported the to_catgorical() method from keras.utils instead of using np_utils. Always install the latest version of the framework, such as TensorFlow, and if you get an error, see if the method or module is deprecated.

Let me again show you a complete example using tensorflow.keras.utils.to_categorical() function converts a vector to a binary class matrix.

The syntax is given below.

tf.keras.utils.to_categorical(
    y, num_classes=None, dtype='float32'
)
  • It consists of a few parameters
    • y: integers from 0 to num classes – 1 are in an array-like structure that will be transformed into a matrix.
    • num_classes: number of classes combined. If None, this is implied to be max(y) + 1.
    • dtype: By default, it takes float32 and defines the data type.

Now let’s see a quick example,

import tensorflow as tf 
from tensorflow.keras.utils import to_categorical
based_code = tf.keras.utils.to_categorical([1, 0, 2, 3], num_classes=4)
b_code = tf.constant(based_code, shape=[4, 4])
print(b_code)
Complete Solution Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

In the following given code, we first imported the tensorflow.keras.utils import to_categorical, then declare a variable “based_code” and use the tf.keras.utils.to_categorical() function converts the given list of values to a binary matrix.

This is how to resolve Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’.

Conclusion

Using two methods, you learned how to solve the error Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’.

In the first method, you learned how to use TensorFlow to resolve that error by importing the to_categorical() function from the tensorflow.keras.utils.

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Then, in the second method, the to_categorical() function was imported from the keras.utils, and the class was converted to binary data.

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