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Score: 0.8028342127799988; Reported for: String similarity Open both answers

Possible Plagiarism

Plagiarized on 2021-09-04
by Sohaib Anwaar

Original Post

Original - Posted on 2018-09-17
by ezChx



            
Present in both answers; Present only in the new answer; Present only in the old answer;

Have you tried this? Make the same image data generator with a validation split argument. and then initilize Flow from directory data generator with different subsets, i.e subset='training', subset='validation'. You can see the ex
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)
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/

        
Present in both answers; Present only in the new answer; Present only in the old answer;