How can I get best val_acc and val_loss simultaneously in CNN using keras?
I've used ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max') to get the max val_acc. So, when i run my program I get a many of sample such as:
- val_acc = 0.92857 & val_loss = 0.2495,
- val_acc = 0.98750 & val_loss = 0.6820.
So, it takes 2 no., although it's val_loss in large.
How can I choose the no. 1 with lower val_loss?
Alternatively, how can i choose the best fitted model keeping val_acc and val_loss value in mind?
keras
asked Nov 12 at 14:23
Tariqul Islam
85
add a comment |
I've used ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max') to get the max val_acc. So, when i run my program I get a many of sample such as:
- val_acc = 0.92857 & val_loss = 0.2495,
- val_acc = 0.98750 & val_loss = 0.6820.
So, it takes 2 no., although it's val_loss in large.
How can I choose the no. 1 with lower val_loss?
Alternatively, how can i choose the best fitted model keeping val_acc and val_loss value in mind?
keras
asked Nov 12 at 14:23
Tariqul Islam
85
add a comment |
I've used ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max') to get the max val_acc. So, when i run my program I get a many of sample such as:
- val_acc = 0.92857 & val_loss = 0.2495,
- val_acc = 0.98750 & val_loss = 0.6820.
So, it takes 2 no., although it's val_loss in large.
How can I choose the no. 1 with lower val_loss?
Alternatively, how can i choose the best fitted model keeping val_acc and val_loss value in mind?
keras
asked Nov 12 at 14:23
Tariqul Islam
85
I've used ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max') to get the max val_acc. So, when i run my program I get a many of sample such as:
- val_acc = 0.92857 & val_loss = 0.2495,
- val_acc = 0.98750 & val_loss = 0.6820.
So, it takes 2 no., although it's val_loss in large.
How can I choose the no. 1 with lower val_loss?
Alternatively, how can i choose the best fitted model keeping val_acc and val_loss value in mind?
keras
keras
asked Nov 12 at 14:23
Tariqul Islam
85
asked Nov 12 at 14:23
Tariqul Islam
85
asked Nov 12 at 14:23
Tariqul Islam
85
asked Nov 12 at 14:23
Tariqul Islam
85
asked Nov 12 at 14:23
Tariqul Islam
85
85
add a comment |
add a comment |
1 Answer
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You should really just factor one of them for your checkpoint. However, you could just create two callbacks and save the network based on both metrics.
call_acc = ModelCheckpoint(weights.{epoch:02d}-{val_acc:.2f}.hdf5, monitor='val_acc', save_best_only=True, mode='max')
call_loss = ModelCheckpoint(weights.{epoch:02d}-{val_loss:.2f}.hdf5, monitor='val_loss', save_best_only=True, mode='min')
model.fit(X, Y, epochs, batch_siz, callbacks=[call_loss, call_acc])
answered Nov 12 at 20:14
Kurtis Streutker
48028
add a comment |
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1 Answer
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active
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1 Answer
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active
oldest
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active
oldest
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active
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You should really just factor one of them for your checkpoint. However, you could just create two callbacks and save the network based on both metrics.
call_acc = ModelCheckpoint(weights.{epoch:02d}-{val_acc:.2f}.hdf5, monitor='val_acc', save_best_only=True, mode='max')
call_loss = ModelCheckpoint(weights.{epoch:02d}-{val_loss:.2f}.hdf5, monitor='val_loss', save_best_only=True, mode='min')
model.fit(X, Y, epochs, batch_siz, callbacks=[call_loss, call_acc])
answered Nov 12 at 20:14
Kurtis Streutker
48028
add a comment |
You should really just factor one of them for your checkpoint. However, you could just create two callbacks and save the network based on both metrics.
call_acc = ModelCheckpoint(weights.{epoch:02d}-{val_acc:.2f}.hdf5, monitor='val_acc', save_best_only=True, mode='max')
call_loss = ModelCheckpoint(weights.{epoch:02d}-{val_loss:.2f}.hdf5, monitor='val_loss', save_best_only=True, mode='min')
model.fit(X, Y, epochs, batch_siz, callbacks=[call_loss, call_acc])
answered Nov 12 at 20:14
Kurtis Streutker
48028
add a comment |
You should really just factor one of them for your checkpoint. However, you could just create two callbacks and save the network based on both metrics.
call_acc = ModelCheckpoint(weights.{epoch:02d}-{val_acc:.2f}.hdf5, monitor='val_acc', save_best_only=True, mode='max')
call_loss = ModelCheckpoint(weights.{epoch:02d}-{val_loss:.2f}.hdf5, monitor='val_loss', save_best_only=True, mode='min')
model.fit(X, Y, epochs, batch_siz, callbacks=[call_loss, call_acc])
answered Nov 12 at 20:14
Kurtis Streutker
48028
You should really just factor one of them for your checkpoint. However, you could just create two callbacks and save the network based on both metrics.
call_acc = ModelCheckpoint(weights.{epoch:02d}-{val_acc:.2f}.hdf5, monitor='val_acc', save_best_only=True, mode='max')
call_loss = ModelCheckpoint(weights.{epoch:02d}-{val_loss:.2f}.hdf5, monitor='val_loss', save_best_only=True, mode='min')
model.fit(X, Y, epochs, batch_siz, callbacks=[call_loss, call_acc])
answered Nov 12 at 20:14
Kurtis Streutker
48028
answered Nov 12 at 20:14
Kurtis Streutker
48028
answered Nov 12 at 20:14
Kurtis Streutker
48028
answered Nov 12 at 20:14
Kurtis Streutker
48028
48028
add a comment |
add a comment |
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