Multi task learning with missing labels in Keras tutorial question
$begingroup$
https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/
I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:
from keras import backend as K
def masked_loss_function(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
return K.binary_crossentropy(y_true * mask, y_pred * mask)
model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])
Thanks so much!
classification keras beginner multitask-learning
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$begingroup$
https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/
I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:
from keras import backend as K
def masked_loss_function(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
return K.binary_crossentropy(y_true * mask, y_pred * mask)
model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])
Thanks so much!
classification keras beginner multitask-learning
edited 1 hour ago
oW_
3,271932
asked 1 hour ago
scouts9scouts9
11
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/
I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:
from keras import backend as K
def masked_loss_function(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
return K.binary_crossentropy(y_true * mask, y_pred * mask)
model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])
Thanks so much!
classification keras beginner multitask-learning
edited 1 hour ago
oW_
3,271932
asked 1 hour ago
scouts9scouts9
11
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
https://www.dlology.com/blog/how-to-multi-task-learning-with-missing-labels-in-keras/
I followed this tutorial to create a multi task model for binary classification with missing labels in keras. The dataset that I'm using has inputs in the form of 167 length bit strings consisting of ones and zeroes (represent chemical structures), and the output is 12 binary labels representing toxicity of assays. I pretty much copied the tutorial exactly except for replacing their data with my curated dataset, and I'm getting 11% accuracy on my test set. I'm extremely confused about how the tutorial's masked_loss_function works, and I would appreciate any input on how to improve my model:
from keras import backend as K
def masked_loss_function(y_true, y_pred):
mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
return K.binary_crossentropy(y_true * mask, y_pred * mask)
model.compile(loss=masked_loss_function, optimizer='adam', metrics=['accuracy'])
Thanks so much!
classification keras beginner multitask-learning
classification keras beginner multitask-learning
edited 1 hour ago
oW_
3,271932
asked 1 hour ago
scouts9scouts9
11
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 1 hour ago
oW_
3,271932
asked 1 hour ago
scouts9scouts9
11
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 1 hour ago
oW_
3,271932
edited 1 hour ago
oW_
3,271932
edited 1 hour ago
oW_
3,271932
3,271932
asked 1 hour ago
scouts9scouts9
11
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
scouts9scouts9
11
asked 1 hour ago
scouts9scouts9
11
11
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
scouts9 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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