Keras: How to use predict_generator with ImageDataGenerator?
You can change the value of batch_size
in flow_from_directory
from default value (which is batch_size=32
) to batch_size=1
. Then set the steps
of predict_generator
to the total number of your test images. Something like this:
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(200, 200),
color_mode="rgb",
shuffle = False,
class_mode='categorical',
batch_size=1)
filenames = test_generator.filenames
nb_samples = len(filenames)
predict = model.predict_generator(test_generator,steps = nb_samples)
Default batch_size
in generator is 32. If you want to make 1 prediction for every sample of total nb_samples you should devide your nb_samples with the batch_size
. Thus with a batch_size
of 7 you only need 14/7=2 steps for your 14 images
desired_batch_size=7
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(200, 200),
color_mode="rgb",
shuffle = False,
class_mode='categorical',
batch_size=desired_batch_size)
filenames = test_generator.filenames
nb_samples = len(filenames)
predict = model.predict_generator(test_generator,steps =
np.ceil(nb_samples/desired_batch_size))
The problem is the inclusion of nb_samples
in the predict_generator
which is creating 14 batches of 14 images
14*14 = 196