Keras split train test set when using ImageDataGenerator

I have a single directory which contains sub-folders (according to labels) of images. I want to split this data into train and test set while using ImageDataGenerator in Keras. Although model.fit() in keras has argument validation_split for specifying the split, I could not find the same for model.fit_generator(). How to do it ?

train_datagen = ImageDataGenerator(rescale=1./255,

shear_range=0.2,

zoom_range=0.2,

horizontal_flip=True)

 

train_generator = train_datagen.flow_from_directory(

train_data_dir,

target_size=(img_width, img_height),

batch_size=32,

class_mode='binary')

 

model.fit_generator(

train_generator,

samples_per_epoch=nb_train_samples,

nb_epoch=nb_epoch,

validation_data=??,

nb_val_samples=nb_validation_samples)

I don't have separate directory for validation data, need to split it from the training data

-----

Keras has now added Train / validation split from a single directory using ImageDataGenerator:

train_datagen = ImageDataGenerator(rescale=1./255,

    shear_range=0.2,

    zoom_range=0.2,

    horizontal_flip=True,

					validation_split=0.2) # set validation split

 

train_generator = train_datagen.flow_from_directory(

    train_data_dir,

    target_size=(img_height, img_width),

    batch_size=batch_size,

    class_mode='binary',

					subset='training') # set as training data

 

validation_generator = train_datagen.flow_from_directory(

    train_data_dir, # same directory as training data						

    target_size=(img_height, img_width),

    batch_size=batch_size,

    class_mode='binary',

					subset='validation') # set as validation data

 

model.fit_generator(

    train_generator,

    steps_per_epoch = train_generator.samples // batch_size,

    validation_data = validation_generator, 

    validation_steps = validation_generator.samples // batch_size,

    epochs = nb_epochs)

https://keras.io/preprocessing/image/

 

keras.preprocessing.image.ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format='channels_last', validation_split=0.0, interpolation_order=1, dtype='float32')

 

 

Does the validation_generator also augment data? After reading the comments from github.com/keras-team/keras/issues/5862 it seems like it does. – bitnahian May 9 at 13:54