directory- Path to the folder which contains all the images, None if x_col contains absolute paths pointing to each image instead of just filenames. The flow_from_dataframe accepts all the arguments that flow_from_directory accepts,and obvious mandatory arguments likeĭataframe- Pandas DataFrame which contains the filenames and classes or numeric data to be treated as target values. You can even do regression tasks that takes images as inputs and target values as outputs and it also supports multiple numerical target columns, so now you can create multi output neural networks easily. The best part about the flow_from_dataframe function is that, you can pass any column/s as target data(with class_mode=”raw”, any data that is provided on a column or list of columns will be treated as raw target Numpy arrays).
In former case, we already have the flow_from_directory method that helps you read the images from the folders, but in the later case you will need to write either a custom generator or move the image files to their respective class name folders and use flow_from_directory to utilize ImageDataGenerator, but now with the help of flow_from_dataframe method you can directly pass the Pandas DataFrame which has the mapping between filenames of the images and their labels. Most often the Image datasets available on the internet are either has images placed under folders which has their respective class names or placed under a single folder along with a CSV or JSON file which maps the image filenames with their corresponding classes. Now you can utilize Keras’s ImageDataGenerator to perform image augmentation by directly reading the CSV files through pandas dataframe.
Keras flow_from_dataframe example article.Ī more detailed tutorial can be found at of keras-preprocessing library: Keras seems like taking time to migrate changes from keras-preprocessing library to Keras itself, So if you wish to use this flow_from_dataframe feature I suggest you do the following after you have installed keras, pip uninstall keras-preprocessing pip install git+ and import ImageDataGenerator from keras_preprocessing instead of keras.preprocessing from keras_preprocessing.image import ImageDataGenerator WITH from .y.Tutorial on Keras ImageDataGenerator with flow_from_dataframe
If you use Keras from Tensorflow (Keras inside Tensorflow), then for both the code presented and the tutorials you consult, ensure that you replace the import/neural network creation snippets: from keras.x.y.z import A The solution to your problem lies in this line of code(either simple flow or flow_from_directory): # prepare iteratorįor creating your own DataGenerator, one should have a look at this link(for a starting point): I would suggest reading this excellent article about ImageDataGenenerator and Augmentation: Steps_per_epoch=len(x_train) // 32, epochs=epochs) Model.fit(datagen.flow(x_train, y_train, batch_size=32), # (std, mean, and principal components if ZCA whitening is applied) # compute quantities required for featurewise normalization fit()Įxample taken from Keras official documentation: datagen = ImageDataGenerator( fit_generator() has been deprecated and you should use. ImageDataGenerator() provides you with the possibility of loading the data into batches You can actually use in your fit_generator() method the parameter batch_size, which works with ImageDataGenerator() there is no need (only for good practice if you want) to write a generator from scratch. Image_datagen = ImageDataGenerator(featurewise_center=True, rotation_range=90)
# Code loading training data subsets X_batch However, I am not sure whether the fit function of ImageDataGenerator can be used in this way as it might reset on each fitting approach.Īs an example of how it might work: def custom_train_generator(): One potential solution that came to my mind is to load up batches of my training data using a custom generator function and fitting the image generator multiple times in a loop. However, my training data set is too large to fit into memory when loaded up at once.Ĭonsequently, I would like to fit the generator in several steps using subsets of my training data. Model.fit_generator(train_generator, steps_per_epoch=2000, epochs=50) Train_generator = image_datagen.flow_from_directory('data/images') image_datagen = ImageDataGenerator(featurewise_center=True, rotation_range=90) fit() function on the instantiated ImageDataGenerator object using my training data as parameter as shown below. I want to use the Keras ImageDataGenerator for data augmentation.