TensorFlow 1 version
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View source on GitHub
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Generate batches of tensor image data with real-time data augmentation.
tf.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=None, validation_split=0.0, dtype=None
)
The data will be looped over (in batches).
Arguments | |
|---|---|
featurewise_center
|
Boolean. Set input mean to 0 over the dataset, feature-wise. |
samplewise_center
|
Boolean. Set each sample mean to 0. |
featurewise_std_normalization
|
Boolean. Divide inputs by std of the dataset, feature-wise. |
samplewise_std_normalization
|
Boolean. Divide each input by its std. |
zca_epsilon
|
epsilon for ZCA whitening. Default is 1e-6. |
zca_whitening
|
Boolean. Apply ZCA whitening. |
rotation_range
|
Int. Degree range for random rotations. |
width_shift_range
|
Float, 1-D array-like or int
|
height_shift_range
|
Float, 1-D array-like or int
(-height_shift_range, +height_shift_range)height_shift_range=2 possible values
are integers [-1, 0, +1],
same as with height_shift_range=[-1, 0, +1],
while with height_shift_range=1.0 possible values are floats
in the interval [-1.0, +1.0).
|
brightness_range
|
Tuple or list of two floats. Range for picking a brightness shift value from. |
shear_range
|
Float. Shear Intensity (Shear angle in counter-clockwise direction in degrees) |
zoom_range
|
Float or [lower, upper]. Range for random zoom.
If a float, [lower, upper] = [1-zoom_range, 1+zoom_range].
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channel_shift_range
|
Float. Range for random channel shifts. |
fill_mode
|
One of {"constant", "nearest", "reflect" or "wrap"}.
Default is 'nearest'.
Points outside the boundaries of the input are filled
according to the given mode:
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cval
|
Float or Int.
Value used for points outside the boundaries
when fill_mode = "constant".
|
horizontal_flip
|
Boolean. Randomly flip inputs horizontally. |
vertical_flip
|
Boolean. Randomly flip inputs vertically. |
rescale
|
rescaling factor. Defaults to None. If None or 0, no rescaling is applied, otherwise we multiply the data by the value provided (after applying all other transformations). |
preprocessing_function
|
function that will be applied on each input. The function will run after the image is resized and augmented. The function should take one argument: one image (Numpy tensor with rank 3), and should output a Numpy tensor with the same shape. |
data_format
|
Image data format,
either "channels_first" or "channels_last".
"channels_last" mode means that the images should have shape
(samples, height, width, channels),
"channels_first" mode means that the images should have shape
(samples, channels, height, width).
It defaults to the image_data_format value found in your
Keras config file at ~/.keras/keras.json.
If you never set it, then it will be "channels_last".
|
validation_split
|
Float. Fraction of images reserved for validation (strictly between 0 and 1). |
dtype
|
Dtype to use for the generated arrays. |
Examples:
Example of using .flow(x, y):
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
datagen = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(x_train)
# fits the model on batches with real-time data augmentation:
model.fit(datagen.flow(x_train, y_train, batch_size=32),
steps_per_epoch=len(x_train) / 32, epochs=epochs)
# here's a more "manual" example
for e in range(epochs):
print('Epoch', e)
batches = 0
for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
model.fit(x_batch, y_batch)
batches += 1
if batches >= len(x_train) / 32:
# we need to break the loop by hand because
# the generator loops indefinitely
break
Example of using .flow_from_directory(directory):
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size=(150, 150),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
'data/validation',
target_size=(150, 150),
batch_size=32,
class_mode='binary')
model.fit(
train_generator,
steps_per_epoch=2000,
epochs=50,
validation_data=validation_generator,
validation_steps=800)
Example of transforming images and masks together.
# we create two instances with the same arguments
data_gen_args = dict(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=90,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.2)
image_datagen = ImageDataGenerator(**data_gen_args)
mask_datagen = ImageDataGenerator(**data_gen_args)
# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
image_datagen.fit(images, augment=True, seed=seed)
mask_datagen.fit(masks, augment=True, seed=seed)
image_generator = image_datagen.flow_from_directory(
'data/images',
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
'data/masks',
class_mode=None,
seed=seed)
# combine generators into one which yields image and masks
train_generator = zip(image_generator, mask_generator)
model.fit_generator(
train_generator,
steps_per_epoch=2000,
epochs=50)
Methods
apply_transform
apply_transform(
x, transform_parameters
)
Applies a transformation to an image according to given parameters.