Weighted samples in Tensorflow for convolutional neural networksRetrain final layer of Inception modelBinary classification of similar images with small region of interestmultiple digit detectionTensorFlow: Regression using Deep Neural NetworkDesign strategies for higher resolution images for Convolutional neural network?Structure of Convolutional Neural Network to analyze a sequence of framesHybrid Convolutional and Conventional Neural NetworksTensorflow CNN sometimes converges, sometimes notMulti-input Convolutional Neural Network for Images ClassificationFully convolutional networks with partially segmented data
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Weighted samples in Tensorflow for convolutional neural networks
Retrain final layer of Inception modelBinary classification of similar images with small region of interestmultiple digit detectionTensorFlow: Regression using Deep Neural NetworkDesign strategies for higher resolution images for Convolutional neural network?Structure of Convolutional Neural Network to analyze a sequence of framesHybrid Convolutional and Conventional Neural NetworksTensorflow CNN sometimes converges, sometimes notMulti-input Convolutional Neural Network for Images ClassificationFully convolutional networks with partially segmented data
$begingroup$
For my binary classification problem (A vs B), each image in either class has its individual weight. This means, for example, if I have 10000 images for A, not all of the images are equally important. Weight of "0.9" would mean it is very important, and a weight of "0.01" means that it is not that important.
Can this information be communicated to the training in Tensorflow?
Thanks for the help.
machine-learning tensorflow convnet
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
$endgroup$
add a comment |
$begingroup$
For my binary classification problem (A vs B), each image in either class has its individual weight. This means, for example, if I have 10000 images for A, not all of the images are equally important. Weight of "0.9" would mean it is very important, and a weight of "0.01" means that it is not that important.
Can this information be communicated to the training in Tensorflow?
Thanks for the help.
machine-learning tensorflow convnet
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
$endgroup$
add a comment |
$begingroup$
For my binary classification problem (A vs B), each image in either class has its individual weight. This means, for example, if I have 10000 images for A, not all of the images are equally important. Weight of "0.9" would mean it is very important, and a weight of "0.01" means that it is not that important.
Can this information be communicated to the training in Tensorflow?
Thanks for the help.
machine-learning tensorflow convnet
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
$endgroup$
For my binary classification problem (A vs B), each image in either class has its individual weight. This means, for example, if I have 10000 images for A, not all of the images are equally important. Weight of "0.9" would mean it is very important, and a weight of "0.01" means that it is not that important.
Can this information be communicated to the training in Tensorflow?
Thanks for the help.
machine-learning tensorflow convnet
machine-learning tensorflow convnet
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
asked 18 mins ago
AnshulKapoorAnshulKapoor
31
31
add a comment |
add a comment |
1 Answer
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$begingroup$
Weighted loss function is what you need.
If you use Tensorflow with Keras, you can simply use the class_weight argument of the fit function (e.g., model.fit(..., class_weight = 0: .9, 1: .1)).
As an alternative, you could just implement your own weighted loss function. For example:
CLASS_1_WEIGHT = .9
CLASS_0_WEIGHT = .1
def weighted_bce(y_true, y_pred):
w = y_true*CLASS_1_WEIGHT+ (1.0 - y_true)*CLASS_0_WEIGHT
return K.mean(K.binary_crossentropy(y_true, y_pred) * w, axis=-1)
answered just now
m0nzderrm0nzderr
363
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1 Answer
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1 Answer
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active
oldest
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$begingroup$
Weighted loss function is what you need.
If you use Tensorflow with Keras, you can simply use the class_weight argument of the fit function (e.g., model.fit(..., class_weight = 0: .9, 1: .1)).
As an alternative, you could just implement your own weighted loss function. For example:
CLASS_1_WEIGHT = .9
CLASS_0_WEIGHT = .1
def weighted_bce(y_true, y_pred):
w = y_true*CLASS_1_WEIGHT+ (1.0 - y_true)*CLASS_0_WEIGHT
return K.mean(K.binary_crossentropy(y_true, y_pred) * w, axis=-1)
answered just now
m0nzderrm0nzderr
363
$endgroup$
add a comment |
$begingroup$
Weighted loss function is what you need.
If you use Tensorflow with Keras, you can simply use the class_weight argument of the fit function (e.g., model.fit(..., class_weight = 0: .9, 1: .1)).
As an alternative, you could just implement your own weighted loss function. For example:
CLASS_1_WEIGHT = .9
CLASS_0_WEIGHT = .1
def weighted_bce(y_true, y_pred):
w = y_true*CLASS_1_WEIGHT+ (1.0 - y_true)*CLASS_0_WEIGHT
return K.mean(K.binary_crossentropy(y_true, y_pred) * w, axis=-1)
answered just now
m0nzderrm0nzderr
363
$endgroup$
add a comment |
$begingroup$
Weighted loss function is what you need.
If you use Tensorflow with Keras, you can simply use the class_weight argument of the fit function (e.g., model.fit(..., class_weight = 0: .9, 1: .1)).
As an alternative, you could just implement your own weighted loss function. For example:
CLASS_1_WEIGHT = .9
CLASS_0_WEIGHT = .1
def weighted_bce(y_true, y_pred):
w = y_true*CLASS_1_WEIGHT+ (1.0 - y_true)*CLASS_0_WEIGHT
return K.mean(K.binary_crossentropy(y_true, y_pred) * w, axis=-1)
answered just now
m0nzderrm0nzderr
363
$endgroup$
Weighted loss function is what you need.
If you use Tensorflow with Keras, you can simply use the class_weight argument of the fit function (e.g., model.fit(..., class_weight = 0: .9, 1: .1)).
As an alternative, you could just implement your own weighted loss function. For example:
CLASS_1_WEIGHT = .9
CLASS_0_WEIGHT = .1
def weighted_bce(y_true, y_pred):
w = y_true*CLASS_1_WEIGHT+ (1.0 - y_true)*CLASS_0_WEIGHT
return K.mean(K.binary_crossentropy(y_true, y_pred) * w, axis=-1)
answered just now
m0nzderrm0nzderr
363
answered just now
m0nzderrm0nzderr
363
answered just now
m0nzderrm0nzderr
363
answered just now
m0nzderrm0nzderr
363
363
add a comment |
add a comment |
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