Arguments in Keras for image preprocessing
On the keras image preprocessing page it is not explained and I can't figure out what I am doing wrong. There is one comment here on stackoverflow about it but to me it still makes no sense. For the following code:
# 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/train_images',
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
'...data/train_labels',
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=20,
epochs=1)
I can not figure out what the purpose is of the line image_datagen.fit(images, augment=True, seed=seed) and what the 'images' argument entails. Is it supposed to be a matrix of all images? How should it be formatted? Same goes for the masks argument below that line. I cannot grasp the purpose of those lines and arguments.
I have my images in a numpy array of dtype uint8 and shape (625, 256, 256, 4), and the labels as dtype uint8 and shape (625, 256,256). Furthermore they are stored as 625 seperate images and masks in the directory given in the code.
I constantly get a ValueError: Input to.fit()should have rank 4. Got array with shape: (625, 256, 256). I understand I have to add another dimension. When I reshape it to (625, 256, 256, 1) (of which I'm not certain it is correct or affects the model) I get the following error:
TypeError: fit_generator() missing 1 required positional argument: 'generator'.
Is there anyone who can explain the concepts of these arguments and perhaps even tell me how to format/shape my code for this to work properly?
Thanks in advance
python image-processing keras semantic-segmentation
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
add a comment |
On the keras image preprocessing page it is not explained and I can't figure out what I am doing wrong. There is one comment here on stackoverflow about it but to me it still makes no sense. For the following code:
# 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/train_images',
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
'...data/train_labels',
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=20,
epochs=1)
I can not figure out what the purpose is of the line image_datagen.fit(images, augment=True, seed=seed) and what the 'images' argument entails. Is it supposed to be a matrix of all images? How should it be formatted? Same goes for the masks argument below that line. I cannot grasp the purpose of those lines and arguments.
I have my images in a numpy array of dtype uint8 and shape (625, 256, 256, 4), and the labels as dtype uint8 and shape (625, 256,256). Furthermore they are stored as 625 seperate images and masks in the directory given in the code.
I constantly get a ValueError: Input to.fit()should have rank 4. Got array with shape: (625, 256, 256). I understand I have to add another dimension. When I reshape it to (625, 256, 256, 1) (of which I'm not certain it is correct or affects the model) I get the following error:
TypeError: fit_generator() missing 1 required positional argument: 'generator'.
Is there anyone who can explain the concepts of these arguments and perhaps even tell me how to format/shape my code for this to work properly?
Thanks in advance
python image-processing keras semantic-segmentation
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
below link might be of use. stackoverflow.com/questions/51656000/…
– teng
Nov 19 '18 at 23:26
add a comment |
On the keras image preprocessing page it is not explained and I can't figure out what I am doing wrong. There is one comment here on stackoverflow about it but to me it still makes no sense. For the following code:
# 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/train_images',
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
'...data/train_labels',
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=20,
epochs=1)
I can not figure out what the purpose is of the line image_datagen.fit(images, augment=True, seed=seed) and what the 'images' argument entails. Is it supposed to be a matrix of all images? How should it be formatted? Same goes for the masks argument below that line. I cannot grasp the purpose of those lines and arguments.
I have my images in a numpy array of dtype uint8 and shape (625, 256, 256, 4), and the labels as dtype uint8 and shape (625, 256,256). Furthermore they are stored as 625 seperate images and masks in the directory given in the code.
I constantly get a ValueError: Input to.fit()should have rank 4. Got array with shape: (625, 256, 256). I understand I have to add another dimension. When I reshape it to (625, 256, 256, 1) (of which I'm not certain it is correct or affects the model) I get the following error:
TypeError: fit_generator() missing 1 required positional argument: 'generator'.
Is there anyone who can explain the concepts of these arguments and perhaps even tell me how to format/shape my code for this to work properly?
Thanks in advance
python image-processing keras semantic-segmentation
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
On the keras image preprocessing page it is not explained and I can't figure out what I am doing wrong. There is one comment here on stackoverflow about it but to me it still makes no sense. For the following code:
# 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/train_images',
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
'...data/train_labels',
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=20,
epochs=1)
I can not figure out what the purpose is of the line image_datagen.fit(images, augment=True, seed=seed) and what the 'images' argument entails. Is it supposed to be a matrix of all images? How should it be formatted? Same goes for the masks argument below that line. I cannot grasp the purpose of those lines and arguments.
I have my images in a numpy array of dtype uint8 and shape (625, 256, 256, 4), and the labels as dtype uint8 and shape (625, 256,256). Furthermore they are stored as 625 seperate images and masks in the directory given in the code.
I constantly get a ValueError: Input to.fit()should have rank 4. Got array with shape: (625, 256, 256). I understand I have to add another dimension. When I reshape it to (625, 256, 256, 1) (of which I'm not certain it is correct or affects the model) I get the following error:
TypeError: fit_generator() missing 1 required positional argument: 'generator'.
Is there anyone who can explain the concepts of these arguments and perhaps even tell me how to format/shape my code for this to work properly?
Thanks in advance
python image-processing keras semantic-segmentation
python image-processing keras semantic-segmentation
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
asked Nov 19 '18 at 22:48
Eeuwigestudent1Eeuwigestudent1
417
417
below link might be of use. stackoverflow.com/questions/51656000/…
– teng
Nov 19 '18 at 23:26
add a comment |
below link might be of use. stackoverflow.com/questions/51656000/…
– teng
Nov 19 '18 at 23:26
below link might be of use. stackoverflow.com/questions/51656000/…
– teng
Nov 19 '18 at 23:26
below link might be of use. stackoverflow.com/questions/51656000/…
– teng
Nov 19 '18 at 23:26
add a comment |
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below link might be of use. stackoverflow.com/questions/51656000/…
– teng
Nov 19 '18 at 23:26