![]() ![]() # Provide the same seed and keyword arguments to the fit and flow methods Mask_datagen = ImageDataGenerator(**data_gen_args) Image_datagen = ImageDataGenerator(**data_gen_args) # we create two instances with the same argumentsĭata_gen_args = dict(featurewise_center=True, Validation_generator = test_datagen.flow_from_directory(Įxample of transforming images and masks together. Train_generator = train_datagen.flow_from_directory( Test_datagen = ImageDataGenerator(rescale=1./255) flow_from_directory(directory): train_datagen = ImageDataGenerator( # we need to break the loop by hand becauseĮxample of using. Samples_per_epoch=len(X_train), nb_epoch=nb_epoch)įor X_batch, Y_batch in datagen.flow(X_train, Y_train, batch_size=32): Model.fit_generator(datagen.flow(X_train, Y_train, batch_size=32), # fits the model on batches with real-time data augmentation: # (std, mean, and principal components if ZCA whitening is applied) # compute quantities required for featurewise normalization Y_test = np_utils.to_categorical(y_test, nb_classes) Y_train = np_utils.to_categorical(y_train, nb_classes) flow(X, y): (X_train, y_train), (X_test, y_test) = cifar10.load_data() Prefix to use for filenames of saved pictures (only relevant if save_to_dir is set).Įxample of using.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |