I figured out the answer. This seems to work: from sklearn.model_selection import GroupShuffleSplit splitter = GroupShuffleSplit(test_size=.20, n_splits=2, random_state = 7) split = splitter.split(df, groups=df[‘Group_Id’]) train_inds, test_inds = next(split) train = df.iloc[train_inds] test = df.iloc[test_inds]
You first need to split the data into training and test set (validation set could be useful too). Don’t forget that testing data points represent real-world data. Feature normalization (or data standardization) of the explanatory (or predictor) variables is a technique used to center and normalise the data by subtracting the mean and dividing by … Read more
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”) # … Read more