How to resolve the error - module 'keras.utils' has no attribute 'to_categorical'?

846 Asked by AashnaSaito in Python , Asked on Feb 14, 2023

I'm trying to run the code below in my Jupyter Notebook. I get:

AttributeError: module 'tensorflow.python.keras.utils' has no attribute 'to_categorical' This is code from a Kaggle tutorial. I have installed Keras and Tensorflow.

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from tensorflow.python import keras

from tensorflow.python.keras.models import Sequential

from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, Dropout

      img_rows, img_cols = 28, 28
    num_classes = 10
    
    def data_prep(raw):
        out_y = keras.utils.to_categorical(raw.label, num_classes)
    
        num_images = raw.shape[0]
        x_as_array = raw.values[:,1:]
        x_shaped_array = x_as_array.reshape(num_images, img_rows, img_cols, 1)
        out_x = x_shaped_array / 255
        return out_x, out_y
    
    raw_data = pd.read_csv('trainMNIST.csv')
    
    x, y = data_prep(raw_data)
    
    model = Sequential()
    model.add(Conv2D(20, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=(img_rows, img_cols, 1)))
    model.add(Conv2D(20, kernel_size=(3, 3), activation='relu'))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    
    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer='adam',
                  metrics=['accuracy'])
    model.fit(x, y,
              batch_size=128,
              epochs=2,
              validation_split = 0.2)

Answered by Aalap prabhakaran

To resolve the error - module 'keras.utils' has no attribute 'to_categorical' -

Include this in your code

from tensorflow import keras

in place of

from tensorflow.python import keras

Your Answer

Answer (1)

Ranjana

The error Module 'keras.utils' has no attribute 'to_categorical' typically occurs when there is a mismatch between the versions of Keras and TensorFlow or an incorrect import statement. Here are the steps to resolve this issue:

Check Your Keras and TensorFlow Versions: Ensure you are using compatible versions of Keras and TensorFlow. As of TensorFlow 2.x, Keras is integrated into TensorFlow, and you should use the tensorflow.keras module.

  import tensorflow as tfprint(tf.__version__)Correct Import Statement: In TensorFlow 2.x, you should import to_categorical from tensorflow.keras.utils, not keras.utils.from tensorflow.keras.ut<strong>ils import to_categorical</strong>

Example Usage:

from tensorflow.keras.utils import to_categorical

  # Assuming y is your target labelsy = [0, 1, 2, 3, 4, 5]y_categorical = to_categorical(y)print(y_categorical)

Ensure TensorFlow is Installed: Make sure TensorFlow is installed in your environment.

pip install tensorflow

Update TensorFlow: If you have an older version of TensorFlow, consider updating to the latest version.

  pip install --upgrade tensorflow

By following these steps, you should be able to resolve the error and use the to_categorical function correctly. If the problem persists, consider checking for any other library conflicts or issues within your environment.

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