Image Augmentation for Deep Learning with Keras

Last Updated on August 6, 2022

Data preparation is required when working with neural networks and deep learning models. Increasingly, data augmentation is also required on more complex object recognition tasks.

In this post, you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras.

After reading this post, you will know:

  • About the image augmentation API provided by Keras and how to use it with your models
  • How to perform feature standardization
  • How to perform ZCA whitening of your images
  • How to augment data with random rotations, shifts, and flips
  • How to save augmented image data to disk

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Jun/2016: First published
  • Update Aug/2016: The examples in this post were updated for the latest Keras API. The function was removed
  • Update Oct/2016: Updated for Keras 1.1.0, TensorFlow 0.10.0 and scikit-learn v0.18
  • Update Jan/2017: Updated for Keras 1.2.0 and TensorFlow 0.12.1
  • Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0
  • Update Sep/2019: Updated for Keras 2.2.5 API
  • Update Jul/2022: Updated for TensorFlow 2.x API with a workaround on the feature standardization issue

For an extended tutorial on the ImageDataGenerator for image data augmentation, see:

Keras Image Augmentation API

Like the rest of Keras, the image augmentation API is simple and powerful.

Keras provides the ImageDataGenerator class that defines the configuration for image data preparation and augmentation. This includes capabilities such as:

  • Sample-wise standardization
  • Feature-wise standardization
  • ZCA whitening
  • Random rotation, shifts, shear, and flips
  • Dimension reordering
  • Save augmented images to disk

An augmented image generator can be created as follows:

Rather than performing the operations on your entire image dataset in memory, the API is designed to be iterated by the deep learning model fitting process, creating augmented image data for you just in time. This reduces your memory overhead but adds some additional time cost during model training.

After you have created and configured your ImageDataGenerator, you must fit it on your data. This will calculate any statistics required to actually perform the transforms to your image data. You can do this by calling the fit() function on the data generator and passing it to your training dataset.

The data generator itself is, in fact, an iterator, returning batches of image samples when requested. You can configure the batch size and prepare the data generator and get batches of images by calling the flow() function.

Finally, you can make use of the data generator. Instead of calling the fit() function on your model, you must call the fit_generator() function and pass in the data generator and the desired length of an epoch as well as the total number of epochs on which to train.

You can learn more about the Keras image data generator API in the Keras documentation.

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Point of Comparison for Image Augmentation

Now that you know how the image augmentation API in Keras works, let’s look at some examples.

We will use the MNIST handwritten digit recognition task in these examples. To begin with, let’s take a look at the first nine images in the training dataset.

Running this example provides the following image that you can use as a point of comparison with the image preparation and augmentation in the examples below.

Example MNIST images

Feature Standardization

It is also possible to standardize pixel values across the entire dataset. This is called feature standardization and mirrors the type of standardization often performed for each column in a tabular dataset.

You can perform feature standardization by setting the featurewise_center and featurewise_std_normalization arguments to True on the ImageDataGenerator class. These are set to False by default. However, the recent version of Keras has a bug in the feature standardization so that the mean and standard deviation is calculated across all pixels. If you use the fit() function from the ImageDataGenerator class, you will see an image similar to the one above:

For example, the minimum, mean, and maximum values from the batch printed above are:

And the image displayed is as follows:

Image from feature-wise standardization

The workaround is to compute the feature standardization manually. Each pixel should have a separate mean and standard deviation, and it should be computed across different samples but independent from other pixels in the same sample. You just need to replace the fit() function with your own computation:

The minimum, mean, and maximum as printed now have a wider range:

Running this example, you can see that the effect is different, seemingly darkening and lightening different digits.

Standardized feature MNIST images

ZCA Whitening

A whitening transform of an image is a linear algebraic operation that reduces the redundancy in the matrix of pixel images.

Less redundancy in the image is intended to better highlight the structures and features in the image to the learning algorithm.

Typically, image whitening is performed using the Principal Component Analysis (PCA) technique. More recently, an alternative called ZCA (learn more in Appendix A of this tech report) shows better results in transformed images that keep all the original dimensions. And unlike PCA, the resulting transformed images still look like their originals. Precisely, whitening converts each image into a white noise vector, i.e., each element in the vector has zero mean and unit standard derivation and is statistically independent of each other.

You can perform a ZCA whitening transform by setting the zca_whitening argument to True. But due to the same issue as feature standardization, you must first zero-center your input data separately:

Running the example, you can see the same general structure in the images and how the outline of each digit has been highlighted.

ZCA whitening MNIST images

Random Rotations

Sometimes images in your sample data may have varying and different rotations in the scene.

You can train your model to better handle rotations of images by artificially and randomly rotating images from your dataset during training.

The example below creates random rotations of the MNIST digits up to 90 degrees by setting the rotation_range argument.

Running the example, you can see that images have been rotated left and right up to a limit of 90 degrees. This is not helpful on this problem because the MNIST digits have a normalized orientation, but this transform might be of help when learning from photographs where the objects may have different orientations.

Random rotations of MNIST images

Random Shifts

Objects in your images may not be centered in the frame. They may be off-center in a variety of different ways.

You can train your deep learning network to expect and currently handle off-center objects by artificially creating shifted versions of your training data. Keras supports separate horizontal and vertical random shifting of training data by the width_shift_range and height_shift_range arguments.

Running this example creates shifted versions of the digits. Again, this is not required for MNIST as the handwritten digits are already centered, but you can see how this might be useful on more complex problem domains.

Random shifted MNIST images

Random Flips

Another augmentation to your image data that can improve performance on large and complex problems is to create random flips of images in your training data.

Keras supports random flipping along both the vertical and horizontal axes using the vertical_flip and horizontal_flip arguments.

Running this example, you can see flipped digits. Flipping digits is not useful as they will always have the correct left and right orientation, but this may be useful for problems with photographs of objects in a scene that can have a varied orientation.

Randomly flipped MNIST images

Saving Augmented Images to File

The data preparation and augmentation are performed just in time by Keras.

This is efficient in terms of memory, but you may require the exact images used during training. For example, perhaps you would like to use them with a different software package later or only generate them once and use them on multiple different deep learning models or configurations.

Keras allows you to save the images generated during training. The directory, filename prefix, and image file type can be specified to the flow() function before training. Then, during training, the generated images will be written to the file.

The example below demonstrates this and writes nine images to a “images” subdirectory with the prefix “aug” and the file type of PNG.

Running the example, you can see that images are only written when they are generated.

Augmented MNIST Images Saved To File

Augmented MNIST images saved to file

Tips for Augmenting Image Data with Keras

Image data is unique in that you can review the data and transformed copies of the data and quickly get an idea of how the model may perceive it.

Below are some tips for getting the most from image data preparation and augmentation for deep learning.

  • Review Dataset. Take some time to review your dataset in great detail. Look at the images. Take note of image preparation and augmentations that might benefit the training process of your model, such as the need to handle different shifts, rotations, or flips of objects in the scene.
  • Review Augmentations. Review sample images after the augmentation has been performed. It is one thing to intellectually know what image transforms you are using; it is a very different thing to look at examples. Review images both with individual augmentations you are using as well as the full set of augmentations you plan to use. You may see ways to simplify or further enhance your model training process.
  • Evaluate a Suite of Transforms. Try more than one image data preparation and augmentation scheme. Often you can be surprised by the results of a data preparation scheme you did not think would be beneficial.


In this post, you discovered image data preparation and augmentation.

You discovered a range of techniques you can use easily in Python with Keras for deep learning models. You learned about:

  • The ImageDataGenerator API in Keras for generating transformed images just in time
  • Sample-wise and Feature-wise pixel standardization
  • The ZCA whitening transform
  • Random rotations, shifts, and flips of images
  • How to save transformed images to file for later reuse

Do you have any questions about image data augmentation or this post? Ask your questions in the comments, and I will do my best to answer.

192 Responses to Image Augmentation for Deep Learning with Keras

  1. Avatar
    Andy August 2, 2016 at 7:34 am #

    Interesting tutorial.

    I’m working through the step to standardize images across the dataset and run into the following error:

    AttributeError Traceback (most recent call last)
    in ()
    18 datagen.flow(X_train, y_train, batch_size=9)
    19 # retrieve one batch of images
    —> 20 X_batch, y_batch =
    21 # create a grid of 3×3 images
    22 for i in range(0, 9):

    AttributeError: ‘ImageDataGenerator’ object has no attribute ‘next’

    I have checked the Keras documentation and see no mention of a next attribute.

    Perhaps I’m missing something.

    Thanks for the great tutorials!

  2. Avatar
    narayan August 9, 2016 at 6:38 pm #

    for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):
    File “/usr/local/lib/python2.7/dist-packages/keras/preprocessing/”, line 475, in next
    x = self.image_data_generator.random_transform(x.astype(‘float32’))
    File “/usr/local/lib/python2.7/dist-packages/keras/preprocessing/”, line 346, in random_transform
    fill_mode=self.fill_mode, cval=self.cval)
    File “/usr/local/lib/python2.7/dist-packages/keras/preprocessing/”, line 109, in apply_transform
    x = np.stack(channel_images, axis=0)
    AttributeError: ‘module’ object has no attribute ‘stack’

    how to solve this error …?

    • Avatar
      Jason Brownlee August 15, 2016 at 11:13 am #

      I have not seen an error like that before. Perhaps there is a problem with your environment?

      Consider re-installing Theano and/or Keras.

      • Avatar
        narayan August 26, 2016 at 9:02 pm #

        i solved this error by updating numpy version ….previously it means it should be more than 1.9.0

  3. Avatar
    narayan August 26, 2016 at 9:05 pm #

    Now i have question that how to decide value for this parameter So that i can get good testing accuracy ..i have training dataset with 110 category with 32000 images ..


    Waiting for your positive reply…

    • Avatar
      Jason Brownlee August 27, 2016 at 11:34 am #

      My advice is to try a suite of different configurations and see what works best on your problem.

  4. Avatar
    Walid Ahmed November 9, 2016 at 2:08 am #

    Thanks a lot.
    all worked fine except the last code to save images to file, I got the following exception

    Walids-MacBook-Pro:DataAugmentation walidahmed$ python
    Using TensorFlow backend.
    Traceback (most recent call last):
    File “”, line 20, in
    for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, save_to_dir=’images’, save_prefix=’aug’, save_format=’png’):
    File “/usr/local/lib/python2.7/site-packages/keras/preprocessing/”, line 490, in next
    img = array_to_img(batch_x[i], self.dim_ordering, scale=True)
    File “/usr/local/lib/python2.7/site-packages/keras/preprocessing/”, line 140, in array_to_img
    raise Exception(‘Unsupported channel number: ‘, x.shape[2])
    Exception: (‘Unsupported channel number: ‘, 28)

    Any advice?
    thanks again

    • Avatar
      Jason Brownlee November 9, 2016 at 9:52 am #

      Double check your version of Keras is 1.1.0 and TensorFlow is 0.10.

  5. Avatar
    Sudesh November 11, 2016 at 9:37 pm #

    Hello Jason,

    Thanks a lot for your tutorial. It is helping me in many ways.

    I had question on mask image or target Y for training image X
    Can i also transform Y along with X. Helps in the case of training for segmentation

    • Avatar
      Sudesh November 15, 2016 at 5:25 am #

      I managed to do it.

      datagen = ImageDataGenerator(shear_range=0.02,dim_ordering=K._image_dim_ordering,rotation_range=5,width_shift_range=0.05, height_shift_range=0.05,zoom_range=0.3,fill_mode=’constant’, cval=0)

      for samples in range(0,100):
      seed = rd.randint(low=10,high=100000)
      for imags_batch in datagen.flow(imgs_train,batch_size=batch_size,save_to_dir=’augmented’,save_prefix=’aug’,seed=seed,save_format=’tif’):
      for imgs_mask_batch in datagen.flow(imgs_mask_train, batch_size=batch_size, save_to_dir=’augmented’,seed=seed, save_prefix=’mask_aug’,save_format=’tif’):

  6. Avatar
    Addie November 29, 2016 at 6:01 am #

    This is great stuff but I wonder if you could provide an example like this with an RGB image with three channels? I am getting some really buggy results personally with this ImageGenerator.

  7. Avatar
    Lucas December 24, 2016 at 9:02 am #

    I wonder what channel_shift_range is about. The doc says “shift range for each channels”, but what does this actually mean? Is it adding a random value to each channel or doing something else?

    • Avatar
      Jason Brownlee December 26, 2016 at 7:37 am #

      I have not used this one yet, sorry Lucas.

      You could try experimenting with it or dive into the source to see what it’s all about.

  8. Avatar
    Indra December 26, 2016 at 5:30 pm #


    Thanks for the post. I’ve one question i.e., we do feature standardization in the training set, so while testing, we need those standardized values to apply on testing images ?

    • Avatar
      Jason Brownlee December 27, 2016 at 5:22 am #

      Yes Indra, any transforms like standardization performed on the data prior to modeling will also need to be performed on new data when testing or making predictions.

      In the case of standardization, we need to keep track of means and standard deviations.

  9. Avatar
    Dan March 11, 2017 at 11:01 pm #

    Thanks again Jason. Why do we subplot 330+1+i? Thanks

    • Avatar
      Jason Brownlee March 12, 2017 at 8:24 am #

      This is matplotlab syntax.

      The 33 creates a grid of 3×3 images. The number after that (1-9) indicates the position in that grid to place the next image (left to right, top to bottom ordering).

      I hope that helps.

  10. Avatar
    Vineeth March 13, 2017 at 7:52 pm #

    How do I save the augmented images into a directory with a class label prefix or even better into a subdirectory of class name?

    • Avatar
      Jason Brownlee March 14, 2017 at 8:15 am #

      Great question Vineeth,

      You can specify any directory and filename prefix you like in the call to flow()

  11. Avatar
    Richa March 21, 2017 at 10:45 pm #

    can we augment data of a particular class. I mean images of a class which are less, to deal with the class imbalance problem.

    • Avatar
      Jason Brownlee March 22, 2017 at 8:06 am #

      Great idea.

      Yes, but you may need to prepare the data for each class separately.

  12. Avatar
    Lebron March 26, 2017 at 4:07 pm #

    Hi Jason,

    Thanks for your post!

    I have a question: Does this apply to image data with RGBXYZ for each pixel?

    Each of my input image is of six channels including RGB and XYZ (world coordinate), which was acquired from an organized point cloud by PCL(Point Cloud Library). I am wondering whether there is a correct way to do data augmentation for my images.

    I think ImageDataGenerator might be correct only for RGB images? Because when you shift/rotate/flip the RGB image, it means camera movement indeed, and the XYZ coordinates should be changed as well.


    • Avatar
      Jason Brownlee March 27, 2017 at 7:52 am #

      Hi Lebron, I believe this specific API is intended for 3d pixel data. You might be able to devise your own similar domain-specific transforms for you own data.

      • Avatar
        Lebron March 27, 2017 at 3:51 pm #

        Thanks Jason!

        To confirm, do you mean image with RGB only by “3d pixel data”? And if I have more channels, I have to do all the augmentation by myself, rather than using Keras API?

        • Avatar
          Jason Brownlee March 28, 2017 at 8:21 am #

          Yes, I believe that to be the case, but I could be wrong.

  13. Avatar
    Brian April 16, 2017 at 9:35 am #

    When I use zoom_range of 0.2 and inspect the output images, it seems to zoom h and v axes independently. However I want to have a small amount of zoom variation while preserving the aspect ratio of the images.

    Also, when I specify a rotation_range, the rotated images have aliasing artefacts. Is there any way to specify rotations with antialiasing?

    • Avatar
      Jason Brownlee April 17, 2017 at 5:06 am #

      I’m not sure off hand.

      Do you think these concerns will affect the skill of the model?

      • Avatar
        Brian April 19, 2017 at 11:15 am #

        Thanks Jason,

        Aspect ratio of the image is important in a facial recognition setting. Antialiasing of rotated images I’m not so sure about, but as they are small images (244 x 244) it doesn’t make sense to degrade them further.

        I can modify my own copy of the Keras code to maintain the aspect ratio of the zoom and should be able to substitute PIL’s rotation function, which does antialiasing, for the one used in Keras.

        Keep up the good work, your writing has really helped me get up to speed with Keras quickly

  14. Avatar
    Wuchi May 4, 2017 at 5:38 pm #

    Hi Jason,

    Thank you for your post! Very clear!
    I am trying to use ImageDataGenerator now. But if I want to apply feature standardization to unseen data in the future, I need to save the ImageDataGenerator to disk, right? Any suggestion to do it? Thanks a lot.

    • Avatar
      Jason Brownlee May 5, 2017 at 7:29 am #

      That is correct, or you can standardize manually and just save the coefficients used.

  15. Avatar
    RogerLo May 17, 2017 at 2:23 pm #

    Hi Jason

    I using Keras 2.x ‘tf’ seeting.
    Why I can’t using
    X_batch, y_batch = datagen.flow(train, train, batch_size=32)
    For example :

    from keras.datasets import mnist
    from keras.preprocessing.image import ImageDataGenerator

    # load data
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    # reshape to be [samples][pixels][width][height]
    X_train = X_train.reshape(X_train.shape[0], 28, 28,1)
    X_test = X_test.reshape(X_test.shape[0], 28, 28,1)
    # convert from int to float
    X_train = X_train.astype(‘float32’)
    X_test = X_test.astype(‘float32’)
    # define data preparation
    datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)
    # fit parameters from data
    # configure batch size and retrieve one batch of images
    X_batch, y_batch = datagen.flow(X_train, y_train, batch_size=9)

    Can you tell me why?

    • Avatar
      Jason Brownlee May 18, 2017 at 8:28 am #

      What error do you get exactly?

      • Avatar
        RogerLo May 19, 2017 at 4:26 pm #

        Hi, Hason

        The error message is :
        too many values to unpack (expected 2)

        • Avatar
          Jason Brownlee May 20, 2017 at 5:35 am #

          I’m sorry I have not seen this error before, I do not have any good suggestions.

    • Avatar
      N1k31t4 November 24, 2017 at 7:53 am #

      # load data
      (X_train, y_train), (X_test, y_test) = mnist.load_data()

      I think this should be done without the brackets around the train and test pairs:
      # load data
      X_train, y_train, X_test, y_test = mnist.load_data()

      It is returning four things, but you are only accepting the two tuples.

  16. Avatar
    Fahad June 16, 2017 at 12:25 am #

    Hi Jason,

    I have training data of the shape (2000,4,100,100) which means 2000 samples of the size 100×100 with 4 channels and dtype= uint8, stored as ‘.npy’ file. Can I use Image Augmentation technique on such data?

  17. Avatar
    Umberto June 23, 2017 at 7:00 pm #

    Hi Jason,
    Since I used the fit_generator method instead of fit(), I need to use evaluate_generator in order to correctly evaluate the model or not? Is the same for predict_generator? I’m a little confused.

  18. Avatar
    Matthew Hancock June 25, 2017 at 1:03 am #

    Hi Jason,

    I have a quick question about the image augmentation. I am attempting to greatly increase the size of my training data set using data augmentation in order to increase my segmentation accuracy. Does the image generator feed multiple augmentations of the same image to the model or does it just return a single augmented version instead of the original? There seems to be no way to modify the number of augmented images the Image Data Generator actually returns.

    • Avatar
      Matthew Hancock June 25, 2017 at 1:06 am #

      Never mind, I found my answer in the Keras documentation.

    • Avatar
      Jason Brownlee June 25, 2017 at 6:02 am #

      Great question.

      From the doc: “The data will be looped over (in batches) indefinitely.”

      • Avatar
        Bojan March 24, 2018 at 8:48 pm #

        Also, if for example I have multiple options set in the data augmentation generator. Will it create a lot of different combinations of the data? For example:
        – original data;
        – shifted data;
        – rotated data;
        – noisy data;
        – shifted + rotated data;
        – shifted + noisy data;
        – shifted + rotated + noisy data, etc.

        Or will it only create one set of all the transforms created together, i.e:
        – shifted + rotated + noisy data only;

        If it is the latter, do you have any advice as to how should we combine different output results? Append them maybe in a list or something?

        All the best,
        a very good tutorial

        • Avatar
          Jason Brownlee March 25, 2018 at 6:28 am #

          It applies all of the specified transforms in creating the augmented data.

  19. Avatar
    Alice July 4, 2017 at 7:30 pm #

    Hello Jason,
    I made the exercice with your book which I find just great!!!
    The problem is: it applies on randomly choosen images instead of doing it on the same ones from the “Point of comparison” sub-chapter. And always different samples.
    How could I solve this?
    I must say I don’t understand how it comes the “i” applies on the pyplot.subplot and on the X-batch[].
    Thank you!!

    • Avatar
      Jason Brownlee July 6, 2017 at 10:14 am #

      Think of the augmented images as randomly modified versions of your training dataset. You have a new dataset with lots of variations of the data you provided. You do not need to tie them back to the original examples.

      Or perhaps I misunderstand your question?

      • Avatar
        Nathan July 25, 2017 at 6:39 pm #

        I think the problem of Alice is the same as mine, the data that are plotted after each modification are never the same, which is difficult to make a comparison because they change everytime.

        For example :
        -the first plot gives me : 5 6 3, 0 1 9, 2 3 1
        – after the ZCA whitening i have : 2 3 8, 3 2 5, 0 1 7

        • Avatar
          Jason Brownlee July 26, 2017 at 7:48 am #

          Yes, by design, the augmentation will create different augmented versions of the images each time it is called.

          This is what we want, so the model does a better job of generalizing.

          What is the problem exactly, could you help me to understand please?

      • Avatar
        Antoine Simon July 25, 2017 at 10:59 pm #

        Hello Jason,

        I have the same problem as Alice. I think that what she was saying was that the pictures that she plot after random modifications are never the same

        It looks like the 9 pictures that are plotted are chosen randomly everytime.

        It would be nice if you could answer me on this problem,

        Thank you !

        • Avatar
          Jason Brownlee July 26, 2017 at 7:54 am #

          Yes, this is by design. This is exactly what we want from image augmentation.

        • Avatar
          ondrejsv September 22, 2018 at 10:54 pm #

          If you want to preserve the order of the original dataset, use the shuffle parameter in the flow method:

          for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False):

  20. Avatar
    john Landler July 25, 2017 at 5:45 am #


    When I run the above script, I get this error:
    Using TensorFlow backend.
    C:\Users\sacheu\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\preprocessing\ UserWarning: Expected input to be images (as Numpy array) following the data format convention “channels_first” (channels on axis 1), i.e. expected either 1, 3 or 4 channels on axis 1. However, it was passed an array with shape (60000, 1, 28, 28) (1 channels).
    ‘ (‘ + str(x.shape[self.channel_axis]) + ‘ channels).’)

    can you please tell me how to fix it?
    i think i have the latest version of the libraries. And I am using python 3.5.

    Thank you.

    • Avatar
      Jason Brownlee July 25, 2017 at 9:49 am #

      You could try changing the order of the channels in code or in the Keras configuration file.

      For example, in code:

      Or if this is the cause, comment it out.

      • Avatar
        Tom November 11, 2017 at 2:10 am #

        I have the same problem. Nothing works.

        The message is a warning, and I still get the output images, but i.e. Feature Standardization is black and white, not gray scaled. So I suppose it is not working?

        When I try to comment it out or change the order from ‘th’ to ‘tf’ – it completely brakes. The message is: … (28 channels)

        I am new and any comments are welcome.

        • Avatar
          Jason Brownlee November 11, 2017 at 9:23 am #

          Perhaps double check you have the latest version of Keras installed? 2.0.8 or 2.0.9?

          • Avatar
            Tom November 13, 2017 at 9:04 pm #

            (C:\ProgramData\Anaconda3) C:\ProgramData\Anaconda3\etc\conda\activate.d>set “KERAS_BACKEND=theano”

            (C:\ProgramData\Anaconda3) C:\Users\Tom>conda install -c conda-forge keras Fetching package metadata ……………
            Solving package specifications: .

            # All requested packages already installed.
            # packages in environment at C:\ProgramData\Anaconda3:
            keras 2.0.6 py36_0 conda-forge

            It looks like it is up to date. But..
            I used this tutorial: and typing:

            import keras
            print(‘keras: %s’ % keras.__version__)

            gives me:
            Using Theano backend.
            keras: 2.0.6

            but according to tutorial it should be:
            Using TensorFlow backend.
            keras: 1.2.1

          • Avatar
            Jason Brownlee November 14, 2017 at 10:09 am #

            You can change the backend used by Keras in the ~/.keras/keras.json configuration file.

          • Avatar
            Tom November 13, 2017 at 9:18 pm #

            Ok, at the moment I can say that:

            – Those warning messages are just a warning. They are in each example. I do not know how to make them disappear, but it turns out that they are harmless.

            – Example Feature Standardization is black and white, not gray scaled. Maybe there is an error in script? I am to novice to spot it.

            – Other examples seems to work correctly despite the warning message. (So I just saw a warning, saw different output, spotted comment with similar problem and just stopped. My bad).

            – I can write set “KERAS_BACKEND=tensorflow” to change the backend (I don’t know what does it means, but never mind 🙂 ).

            – You are awesome for making those tutorials. Thank You!

          • Avatar
            Jason Brownlee November 14, 2017 at 10:12 am #


  21. Avatar
    Xiaojie Zhang July 28, 2017 at 2:45 pm #

    Hi, thanks for your share. When I try to use zca-whitening and feature-wised centering on bigger data, I found it’s very very hard to get enough memory to do the fit() function. As the data-set has about 10000 pictures and 224*224 pixels, even generate a flow iterator will use full of my 16GB memory. When try to use fit() for zca-whitening,centering,normalization which the documents said have to use the fit() function, I never success. Will you give some advice for data preparation for bigger data? Thank you very much!

    • Avatar
      Jason Brownlee July 29, 2017 at 8:04 am #

      Are you able to use the flow_from_directory instead of loading it all into memory?

    • Avatar
      Willie Maddox December 3, 2017 at 2:44 am #

      I have the same exact problem with MS-COCO and NUS-WIDE datasets and I have 128GB memory. The flow_from_directory() requires your data to be in a specific directory structure.

      From the Keras v2.1.2 documentation…

      directory: path to the target directory. It should contain one subdirectory per class.

      MS-COCO and NUS-WIDE are not structured this way natively.

      Also, Xiaojie was talking specifically about the fit() function, which is called before the flow (or flow_from_directory) function.

  22. Avatar
    Muneer Ahmad Dedmari August 20, 2017 at 10:02 pm #

    Hi Jason,
    Thanks for this nice post. I have a quick question. I have large-dataset which I am loading to model using custom data-generator. I am using it in model.fit_generator(). Now I want to use data-augmentation. So my question is, how/where can I use keras ImageDataGenerator? Thank you very much.

  23. Avatar
    Steve September 27, 2017 at 11:31 am #


    Shall we run both fit_transform with origin images set and with augmented ones separately ? Or shell we combine them into ơne. On the latter one how do we combine them ?


  24. Avatar
    Steve September 28, 2017 at 1:54 am #

    Thanks Jason. Bought all of your ML books, love it ! Would you write one about Transfer Learning deep enough with ImageNet and few other so that we can re-use the pre-trained one for our own purpose ?


    • Avatar
      Jason Brownlee September 28, 2017 at 5:27 am #

      Thanks again for your support Steve.

      Yes, I have a post scheduled on re-using the VGG model. It should appear on the blog soon.

  25. Avatar
    Momo October 24, 2017 at 2:13 am #

    Thanks for this great post !
    In the Random Shifts part, can we have control on the file names ?
    Is it possible to save files as:
    ‘aug’+original_file_name+’png’ ?


  26. Avatar
    Abraham Ben November 3, 2017 at 5:12 pm #

    thanks for your tutorial. When I try to use :

    pred = model.predict_generator(data_gen.flow_from_directory(“../input/valid_img”, target_size=(input_size, input_size)))

    I cannot get the image filename that correspond to the predicted probability. Is there any solutions?

    • Avatar
      Jason Brownlee November 4, 2017 at 5:27 am #

      The order of predictions will match the order of files in the directory I would expect.

  27. Avatar
    Abraham Ben November 4, 2017 at 3:01 pm #

    I have check it, however, this is not the case since I got a pretty low accuracy compared to the val_acc. I found someone solving it by setting the batch_size to 1 when use predict_generator and ImageDataGenerator.flow_from_directory, but this is not what I want.

  28. Avatar
    Masun November 21, 2017 at 10:42 am #

    Hi Jason,

    Do you have any idea about how we can apply the same idea on signals? I mean signal augmentation for Deep learning? Thank you

    • Avatar
      Jason Brownlee November 22, 2017 at 10:47 am #

      Not off hand. Consider how transforms can be used on your specific data to create new patterns. E.g. even just adding random noise is a good start.

  29. Avatar
    smriti December 4, 2017 at 4:57 pm #

    Once the features have been centered, using featurewise_center=True in keras.preprocessing.image.ImageDataGenerator(), How can I retrieve that statistics so that I use it to preprocess the images to be used for prediction/testing during evaluate_generator() etc

  30. Avatar
    safae January 4, 2018 at 12:50 am #

    Hi Jason,

    Thank you very much for all the posts you shared, which are a very useful and help a lot.

    I would like to ask if do you have an idea about implemented data augmentation algorithms for time series data(such as acceleration, AC voltage, …)

    Thank you

  31. Avatar
    Dimitris Mallios January 30, 2018 at 3:28 am #

    Hi Jason, i have a question, suppose we have semantic segmentation task and we want to rotate and flip both the image and the output image labels, how do we apply the transformations? Should i concatenate the image and the labels to a homogeneous array and then apply the appropriate transformations?

    • Avatar
      Jason Brownlee January 30, 2018 at 9:55 am #

      Good question, you might want more control and apply augmentation one pair at a time.

  32. Avatar
    Wafa March 10, 2018 at 10:38 am #

    Hi Jason,

    Thank you for your nice post! How can I use augmentation to data that I have on my disk? I see you imported mnist and I could not realize how to change this for my purpose.

    • Avatar
      Jason Brownlee March 11, 2018 at 6:18 am #

      You can load the data via the augmentation API and use it to make an augmented version of your dataset. This augmentation could be used directly or saved to file.

  33. Avatar
    Steven March 22, 2018 at 12:27 pm #

    hi Jason,

    Quick question: after the images are augmented, your script saves them into smaller files. What if I want to stitch these augmented files into a single file, similar to the original mnist file so that I can use nmist.load_data() function to load them into, say a CNN engine? Would the following work?

    nmist_new = []
    for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):
    # create a grid of 3×3 images
    for i in range(0, 9):
    nmist_new.append(array(X_batch, y_batch))

    I am not sure what format should be used.

    • Avatar
      Jason Brownlee March 22, 2018 at 2:52 pm #

      Good question, I think some experimentation would be required.

      Perhaps use of numpy’s hstack and vstack to create a larger array from the image arrays?

      • Avatar
        Steven March 23, 2018 at 5:05 am #

        Yeah, I think need some tweaks or look into Keras’ mnist_load() function how what the data format is when writing back. This link has the original data format:

        Scroll down to the bottom for training and test dataset structures. They used some header information for each image. I think hstack and vstack are the way to go, but need to take care of those headers (think should be easy). I am still not sure what those “xxxx” mean. I think the files are just filled by the 28×28 small image data.

        • Avatar
          Jason Brownlee March 23, 2018 at 6:14 am #

          It might be easier to write your own progressive loading function. A heck of a lot simpler actually.

  34. Avatar
    Tien April 8, 2018 at 2:30 pm #

    Dear Jason,

    I used the code to generator featurewise standardized samples, but did not get the same result as shown in the web.


  35. Avatar
    iles April 22, 2018 at 11:38 am #

    Hello Jason

    i have a dataset of images (jpg not a csv file) and each class of this images is in a folder car in folder with name cat and dog with folder name dog so how can i apply a deep learning model on this data do i need image data generator ?
    thank you

  36. Avatar
    Kien Nguyen May 8, 2018 at 1:45 pm #

    Your blog is awesome. Appreciate it.

  37. Avatar
    maulik May 10, 2018 at 6:01 pm #

    Hello Jason,

    I am a bit confused while understanding of “with image augmentation”


    Total Training Image:- 10 (#X_train Image)
    ImageDataGenerator(rotation_range=90) #Randomly Rotation

    model.fit_generator(datagen.flow(x_train, y_train, batch_size=1),
    steps_per_epoch=len(x_train) / 1, epochs=epochs)

    Does it mean we are training model of 10 randomly rotated image ?
    or Are we Training model 10 original images + 10 randomly rotated images with fit_generator () ?

    • Avatar
      maulik May 10, 2018 at 8:24 pm #

      I am assuming , We are training model with 10 randomly rotated image.
      Q.1 Am i correct ?

      Here I tried data augmentation on CIFAR10 (color Image, image[10])
      But I am not getting proper output image after augmentation

      Here is code:-
      import matplotlib.pyplot as plt
      from keras.preprocessing.image import ImageDataGenerator

      datagen = ImageDataGenerator(rotation_range=90)

      print (x1.shape) #Shape (1, 32, 32, 3)
      print (y1.shape) #Shape (1, 10)
      plt.imshow(x1[0]) #Image 1

      for x_batch,y_batch in datagen.flow(x1,y1, batch_size=1):
      for i in range(0, 1):
      plt.imshow(x_batch[i].reshape(32,32,3)) #Image 2
      print (y_batch)

      Jason, Could you please help ?

      Thank you

    • Avatar
      Jason Brownlee May 11, 2018 at 6:34 am #

      10 randomly modified versions of 10 different images.

      • Avatar
        maulik May 11, 2018 at 2:19 pm #

        Got it.

        I Tried to do rotation of 1 image. But when i am doing, image is not properly displayed.

        Here is image

        Here is code:-
        import matplotlib.pyplot as plt
        from keras.preprocessing.image import ImageDataGenerator

        datagen = ImageDataGenerator(rotation_range=90)

        print (x1.shape) #Shape (1, 32, 32, 3)
        print (y1.shape) #Shape (1, 10)
        plt.imshow(x1[0]) #Image 1

        for x_batch,y_batch in datagen.flow(x1,y1, batch_size=1):
        for i in range(0, 1):
        plt.imshow(x_batch[i].reshape(32,32,3)) #Image 2
        print (y_batch)

        Thank you Jason

        • Avatar
          Jason Brownlee May 11, 2018 at 2:32 pm #

          I don’t have any good suggestions sorry. Perhaps try posting your code and error on StackOverflow?

  38. Avatar
    Malek July 2, 2018 at 5:53 am #

    Hello. Thanks for the tutorial. It was so helpful. Actually, I have 3 questions:

    1. When I use your code, the Mnist images change from one data augmentation method to the other. I mean, it’s performed on different 9 samples of digits each time. Do you know how to solve this problem? Because they need to be the same so that I can compare.

    2. The first technique (standardization) doesn’t work for me. I get digits with the same luminosity: no darkening or lightening. Why is that? I just copied and pasted the code.

    3. I need to do data augmentation on 3D medical images (DICOM format). Is it possible to do it using the ImageDataGenerator from Keras? Or is there a simple way to do it?

    Thanks again.

    • Avatar
      Jason Brownlee July 2, 2018 at 6:27 am #

      Sorry, I don’t follow the first question.

      Perhaps the code has changed?

      Sorry, I don’t know about 3D images.

  39. Avatar
    Zara July 5, 2018 at 9:17 am #

    Dear Jason,
    I want to know that how can I upload an image file in python which contains multiple images that is more than one images in a single file.Thanks for your precious time.

  40. Avatar
    Zara July 6, 2018 at 8:52 pm #

    ok Thanx Sir for your time.

  41. Avatar
    Arhe July 7, 2018 at 8:37 pm #

    I am running the examples in a Jupyter notebook and I don’t get to see the darkening and whitening that we see in this blog in section “Feature Standardization”. In this section, I also see different numbers every time I run it. I have double check the code to see that I am not missing anything.
    Has anybody got the same results here?

    • Avatar
      Jason Brownlee July 8, 2018 at 6:21 am #

      Perhaps the API has changed?

      Perhaps results are diffrent when running from the command line, as I would recommend?

  42. Avatar
    Franz Götz-Hahn July 19, 2018 at 7:41 pm #

    Hi Jason!

    Thanks for all your tutorials, you are doing great work bringing concepts forward to people wanting to learn!

    I have a question with respect to data wrangling. I have always written my own small scripts that handle data loading. But what I’d actually like to do is prepare the data in a similar way to how for example the MNIST or CIFAR datasets are stored for Keras. I have read up on the format of the files etc, but I wonder, someone else must’ve written some code that does this for their own data, and surely they would’ve made it public. I’ve searched everywhere and can’t find it.

    Basically, what I am looking for is a script that takes data in some structure (say data/train and data/validation subfolders) and prepares it in pickled batches like MNIST and CIFAR come, so that it can be easily shared and reused.

    Do you have any leads for this?

    • Avatar
      Jason Brownlee July 20, 2018 at 5:57 am #

      Nice idea.

      Not that I’m aware. I would expect you’ll have to code something custom for this.

  43. Avatar
    JJ Goldman August 14, 2018 at 4:44 pm #

    Little update :
    According to the doc (,
    featurewise_center and featurewise_std_normalization are set False by default.

  44. Avatar
    Manohar September 25, 2018 at 1:09 am #

    HI Jason,
    How to rotate an image with exactly 90 degree. ImageDataGenerator produces images which randomly rotated. I want the images rotated exactly 90 degree. How to do that

    • Avatar
      Jason Brownlee September 25, 2018 at 6:26 am #

      Perhaps write code to pre-rotate and save the rotated images?

  45. Avatar
    Mario Gtz October 27, 2018 at 4:04 am #

    Hi Jason, I’m working on a classificator just for learn. I’m using sequence class to generate batches on my data and use multiprocessing function to training the model.

    My problem is: ¿how can I fit this generator of augmentations and the generator that I’ve been created?. I mean this augmentation generator could be trained by:

    And my generator of batches:

    Can I mix this, doing something like:

    history = model.fit_generator(train_datagen.flow(batcg_train)….

    Thank You

  46. Avatar
    duliqiang November 18, 2018 at 2:58 pm #

    Hi, Jason, thanks for this great tutorial.

    Here I have a question for you, when I’m trying your example of feature standardization in my local computer, I see no changes related to darkening or lightening, the resulting images seems same. I cannot figure out why. By the way, in order to just compare the first 9 images, we must set shuffle to False when we use flow() function to get batches.

    Here are code:

    • Avatar
      Jason Brownlee November 19, 2018 at 6:43 am #

      Perhaps the API has changed?

    • Avatar
      Chan Kim March 18, 2019 at 8:03 pm #

      Hi, duliqiang, (and Hi Jason, thanks again for these awesome tutorials)

      I have the same problem (no lightening or deeming). Looked into the API but couldn’t find anything to try. Are there any update on this?


  47. Avatar
    Benjamin November 20, 2018 at 9:28 pm #

    Hi Jason,
    I have a question:
    why do you convert the data to float before further working with it.
    Is this necessary and will it lead to wrong results when obmitted?

    Thank you

  48. Avatar
    Luc November 29, 2018 at 2:47 am #

    Hey Jason !
    Nice post as always !
    I think I missed something, I looked at previous replies but I didnt find a clear answer to my question, so let me ask it, sorry if you already answered to it.

    When you use the fit_generator() method, the model is trained only with the transformed images? It is not trained with original images + transformed image, right?

    ImageGenerator allows to generate transformed data to augment the dataset.
    But what is the point of training the model only with the transformed images and not with original images PLUS the transformed images?

    I feel that the point of augmenting is to augment 😀 so if our dataset is not bigger after, it is not augmented.

    What am I missing here? If keras implemented it this way, it maybe good to train only with transformed images, but why? I thought that the whole point of this was to increase the number of images to train with.

    Is it possible, with the same methods, to generate mutiple type of transformed images (rotation, translation and whitening for exemple) and train the model with all transformed images and also with original images?

    That would make more sense and that would really be augmentation of the original dataset.

    • Avatar
      Jason Brownlee November 29, 2018 at 7:46 am #

      It is only trained on the augmented images. The set of augmented images covers un-augmented images – it has expanded the training dataset.

  49. Avatar
    Bipin January 15, 2019 at 8:31 pm #

    Hi Jason,
    I need to predict value on 125000 images, for which I’m using following snippet:

    test_generator = test_datagen.flow(x,batch_size=10,seed=42,shuffle=False)

    y_pred = model.predict_generator(test_generator,steps=n_pred//10,verbose=1)

    But I’m getting Memory Error.
    Can you please suggest. How to approach

    • Avatar
      Jason Brownlee January 16, 2019 at 5:47 am #

      Perhaps try flow_from_directory() to progressively load data rather than loading it all into memory?

      • Avatar
        Bipin January 16, 2019 at 4:15 pm #

        Since, I’m reading from a readOnly directory and without and subfolder to identify the labels, hence I can’t use flow_from_directory. Also, I think copying all the image to separate directory would be a costly process.

        • Avatar
          Jason Brownlee January 17, 2019 at 5:22 am #

          You can flow from a read only directory, just disable any saving of images (the default).

          • Avatar
            Bipin January 17, 2019 at 4:04 pm #

            It’s unable to read when there are no subfolder.
            I have used:
            # Define data pre-processing
            test_image_gen = ImageDataGenerator(rescale=1/255)
            test_generator = test_image_gen.flow_from_directory(test_dir,target_size=(Image_width,Image_height),batch_size=batch_size,seed=42,class_mode = None,classes=None)

            Found 0 images belonging to 0 classes.

          • Avatar
            Jason Brownlee January 18, 2019 at 5:29 am #

            I see, yes, when flowing from a dir, you must have a sub directory for each class.

  50. Avatar
    sampath kumaran g January 18, 2019 at 2:07 am #

    Hi Jason,

    I have doubt like I have trained a convnet model with data augmentation. Now i need to interpret and get classification from the trained model using the new inputs in real time. I have used ImageDataGenerator with flow_from_directory and fit_generartor… Which method i need to use to predict the result? Could you please brief on that?

    • Avatar
      Jason Brownlee January 18, 2019 at 5:46 am #

      You use predict_generator(), you may also need to create a new generator that only has the same scaling (fit on the training dataset), but no augmentation.

  51. Avatar
    Del February 18, 2019 at 1:07 pm #

    Hi Jason,

    I wished to convert the augmented data (cifar10 test dataset) to 0 -1 values in npz format.
    Not sure if my script is correct as below:

    However when I run the CNN model using the npz file as input image (which is using
    the npz value as poisson rate), It complains that the rate for poisson generator
    cannnot be negative.

    Is there any other settings in the ImageDataGenerator() to be set in order to get
    values between 0 and 1?

    Best Regards

    for x_batch, y_batch in test

    • Avatar
      Jason Brownlee February 18, 2019 at 2:15 pm #

      Not sure I understand how saving the data is related to the poisson distribution, sorry?

      • Avatar
        del February 19, 2019 at 12:21 pm #

        Hi Jason,

        Sorry I am actually trying to convert a ANN CNN network to Spiking NN, the image pixel
        density value is treated as poisson rate for poisson generator,
        Sorry I am new to this. I am able to generate 0-1 value range datasets.
        From my previous post, I am able to save x_test.npz, how do I save the corrresponding

  52. Avatar
    kay June 17, 2019 at 10:38 am #

    I checked my input images generated with rotation and zoom (ImageDataGenerator)
    But after the images change, black spaces created.
    Originally all image have a white background.
    How can I remove this black spaces?

  53. Avatar
    Royal June 25, 2019 at 6:47 am #

    Hi Jason,
    I’m getting an error in the example “Saving Augmented Images to File”

    Traceback (most recent call last):
    File “”, line 1, in
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\keras_preprocessing\image\”, line 100, in __next__
    return*args, **kwargs)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\keras_preprocessing\image\”, line 112, in next
    return self._get_batches_of_transformed_samples(index_array)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\keras_preprocessing\image\”, line 159, in _get_batches_of_transformed_samples
    img = array_to_img(batch_x[i], self.data_format, scale=True)
    File “C:\Users\XXX\AppData\Local\Continuum\anaconda3\envs\env_python_3.6\lib\site-packages\keras_preprocessing\image\”, line 228, in array_to_img
    raise ImportError(‘Could not import PIL.Image. ‘
    ImportError: Could not import PIL.Image. The use of array_to_img requires PIL.

  54. Avatar
    Chiranjibi Sitaula August 19, 2019 at 1:36 pm #

    Is this code correct if I use featurewise_center and featurewise_std_normalization for both train and test datasets?
    test_datagen = ImageDataGenerator(

    train_datagen = ImageDataGenerator(

    train_generator = train_datagen.flow_from_directory(
    os.path.join(args.data_dir , ‘train/’),
    target_size=(args.fixed_width, args.fixed_width),

    validation_generator = test_datagen.flow_from_directory(
    os.path.join(args.data_dir, ‘val/’),
    target_size=(args.fixed_width, args.fixed_width),

    sgd = SGD(lr=args.learning_rate, decay=0.005, momentum=0.9, nesterov=True)
    model.compile(loss=’categorical_crossentropy’, optimizer=sgd, metrics=[‘accuracy’])

  55. Avatar
    Ankit August 27, 2019 at 5:20 am #

    Sir, I am having trouble while saving the images. After the data augmentation, how can we save all those new images to our local machines? any idea for that.

  56. Avatar
    Noushin October 11, 2019 at 10:05 pm #

    I have a dataset with 4532 samples in the train, 698 in the validation set and 599 samples in the test set. I am trying to train VGG16 with data augmentation using the following code:

    # Data Preprocess and Data Augmentation
    train_datagen = ImageDataGenerator(
    rescale=1. / 255,

    vald_datagen = ImageDataGenerator()
    test_datagen = ImageDataGenerator(), augment=True)

    tr_bs = train_data.shape[0]//batch_size
    vl_bs = vald_data.shape[0]//batch_size
    for e in range(epoch):
    print(‘Epoch’, e)
    batches = 0

    for (x_batch, y_batch), (val_x, val_y) in zip(
    train_datagen.flow(train_data, train_labels_one_hot_coded,
    vald_datagen.flow(vald_data, vald_labels_one_hot_coded,
    batch_size=vl_bs)):, y_batch, validation_data=(val_x, val_y))
    batches += 1
    if batches >= len(train_data) / tr_bs:
    # we need to break the loop by hand because
    # the generator loops indefinitely

    At the beginning of the train procedure keras shows this information:

    Found 4532 images belonging to 7 classes.
    Found 698 images belonging to 7 classes.
    Found 599 images belonging to 7 classes.

    which are compatible with the numbers in my dataset. However, this became a source of confusion for me. I thought when we perform augmentation, I should at least have 8 times of actual number of samples in the train set. can you please tell me how to check number of augmented data samples? (I don’t save the augmented images anywhere, is that a must or I can use online as I do the training?!)

    • Avatar
      Jason Brownlee October 12, 2019 at 6:57 am #

      The summary is just what was found as a starting point, augmentation is performed on top of that.

  57. Avatar
    Jose Q October 12, 2019 at 5:37 am #

    Great post Jason!
    I have this question:

    During the model training process, does it consider only the augmented data? or does it also consider the initial data as well?
    I suppose that considering the initial data would help to a better fit.
    Does it matter?

    Thank you

    • Avatar
      Jason Brownlee October 12, 2019 at 7:09 am #

      Training only used augmented, which is a super set (includes) of the initial data.

  58. Avatar
    Bobby January 9, 2020 at 6:37 pm #

    Hi Jason,
    First of all a hearty thanks for making such detailed tutorials. I can’t thank you enough.
    While running the image standardization script, the image grid plot I get has no dark or lightening effects on any of the digits. Also, the resulting plot of the final script is completely different with no visible data augmentation. Keras version (2.3.1)

  59. Avatar
    Salma March 5, 2020 at 6:42 am #

    Hi Jason,

    Thank you for all your articles. They are very helpful. 🙂

    Do you have any idea/hints as to how to use the fit_generator() method if the model has multiple inputs?

  60. Avatar
    sumaya saadat March 5, 2020 at 8:04 pm #

    if I have another dataset like malimg dataset, how to get X_train, X_test, y_train, y_test values, in my dataset i have grayscale images in 25 folders.
    all my dataset is in png format.

    kindly help me

  61. Avatar
    Mustafa March 26, 2020 at 12:13 am #

    great tutorial as usual, many thanks

    I’m trying this on colored images and I’m getting images with different colors, how can keep the colors and just do the augmentation ?

    • Avatar
      Jason Brownlee March 26, 2020 at 7:56 am #

      The operations like zoom and flip do not effect colors.

      Perhaps confirm that your images are being loaded correctly?

  62. Avatar
    Dan May 1, 2020 at 5:48 pm #

    Hey Jason,

    Your tutorials are awesome. Thank you for such an effort you put on preparing these materials.

    Data Augmentation is used to increase the size of training data, but you set number_of_step = training_size / number of batches. In other words, number of training data stays the same while our purpose was increasing the training size! Am I missing anything here?

  63. Avatar
    wu May 18, 2020 at 11:30 am #

    sir,I’m a novice. I’d like to ask you how you do a series of operations on the same nine pictures

    • Avatar
      Jason Brownlee May 18, 2020 at 1:26 pm #

      Specify all of the operations you want in the imagedatagenerator configuration.

  64. Avatar
    wu May 18, 2020 at 2:02 pm #

    Sir,the problem has been solved,but I read the local image in the flow from directory method for zca operation, but it has no effect

    • Avatar
      Jason Brownlee May 19, 2020 at 5:51 am #

      Well done.

      Perhaps double check your configuration for ZCA?

  65. Avatar
    Mostafa May 27, 2020 at 6:30 am #

    Thanks for your nice tutorial
    My question is about using “zca_whitening” with data generators.
    Actually in time of using data generators, we do not have our train data in the form of numpy array to pass it “fit” method in following code:
    Whyat should be done here?

  66. Avatar
    Margi June 3, 2020 at 11:56 am #

    How to perform image augmentation on dicom(.dcm) image??

  67. Avatar
    David Rodriguez September 5, 2020 at 10:30 am #

    How could I specify the number of augmented images to be generated?

    • Avatar
      Jason Brownlee September 6, 2020 at 6:02 am #

      Typically the augmented images are generated just in time as they are needed by the model, e.g. batch by batch.

      If you want to pre-generate them, you can loop through the image generation process until you have a sufficient number for your project.

  68. Avatar
    Fatemeh Esfahani December 1, 2020 at 4:23 pm #

    Thank you for your post. Is there a way for up-sampling an imbalanced class when we load images using imagegenerator and flow_directory in keras?
    Thank you for your help

    • Avatar
      Jason Brownlee December 1, 2020 at 4:34 pm #

      I don’t think so off the cuff. You might need to write a custom data generator.

  69. Avatar
    Dan March 19, 2021 at 9:41 am #

    Great tutorial, Jason.

    Is there a way to have random rotations by a fixed angle? For example, a rotation of -90, 0 , or 90, i.e., not any angle within the rotation range but exactly 90, -90 or 90.

    • Avatar
      Jason Brownlee March 20, 2021 at 5:15 am #

      Off the cuff, I don’t think so. It might be easier to create your own generator with more control over the augmentations.

  70. Avatar
    marjan April 4, 2021 at 4:20 am #

    hello Jason
    My project is about estimating age using OPG images with neural network danet(Based on age) and dasnet(Based on age and aid sex) This is a raw project in gitlab and Implementation for a article with Over 2000 images. But me I tried to run it for 112 images, which is 72 tutorials, 20 validations and 20 tests, with a little change danet runed but dasnet NO. Please guide me…
    This is my Google Drive project link:

    link in git lab:

    Article link is in git lab.
    I really need your help, thank you

    I sent you a message in Canton, but because I did not know where to see it, I wrote the answer to my message here as well

    • Avatar
      Jason Brownlee April 4, 2021 at 6:55 am #

      I’m happy to answer questions, but I don’t have the capacity to review code/data.

  71. Avatar
    Samir March 10, 2022 at 11:01 am #

    Thank you Jason for your help.
    I have a question, when we apply data augmentation ( I am applying the Sequential augmenter with only flip operation using imgaug package) does it replace the original image? should we concatenate the original data as well when generating the batch data?

  72. Avatar
    matt July 31, 2022 at 3:41 pm #

    hey jason great post im trying to use but keep getting error after first example

    /usr/local/lib/python3.7/dist-packages/matplotlib/ in set_data(self, A)
    697 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):
    698 raise TypeError(“Invalid shape {} for image data”
    –> 699 .format(self._A.shape))
    701 if self._A.ndim == 3:

    TypeError: Invalid shape (28, 28, 1) for image data

    any thoughts on how to fix doing it on google colab

    • Avatar
      James Carmichael August 1, 2022 at 8:42 am #

      Hi Matt…Did you copy and paste the code or did you type it in manually?

  73. Avatar
    matt July 31, 2022 at 3:51 pm #

    update got it you just have to squeeze the img first :D)

    img = X_batch[i*3+j]
    img = np.squeeze(img)
    ax[i][j].imshow(img, cmap=plt.get_cmap(“gray”))

    • Avatar
      James Carmichael August 1, 2022 at 8:43 am #

      Thank you for the feedback Matt! Keep up the great work!

  74. Avatar
    RAY August 10, 2022 at 6:01 pm #

    Hi Dr.James Carmichael
    I am a student from Taiwan
    Thank you for your articles that always inspire me a lot.

    I would like to ask you, how to concatenate the input data such as using (mean1, std1) two related values as one input data?


  75. Avatar
    Angelique September 9, 2023 at 3:24 am #

    Hi Jason,

    Your writing has helped me in learning tremendously. I am wondering if you will be making a version of this tutorial with Keras’s new, recommended version of Image Data Generator. When applying it, the deprecation warning is returned. A corollary question in terms of updating code with a new Class, how do you find all the instances where it has been used in your projects?

    • Avatar
      James Carmichael September 9, 2023 at 10:55 am #

      Hi Angelique…You are very welcome! Thank you for your suggestion! We will consider it going forward.

      Aside from the warning you noted, have you found any issues with the current version performance that you could share?

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