tests/testthat/_snaps/callbacks.md
In mlverse/luz: Higher Level 'API' for 'torch'
callback lr scheduler
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(dl,
verbose = FALSE, epochs = 5, callbacks = list(luz_callback_lr_scheduler(
torch::lr_multiplicative, verbose = TRUE, lr_lambda = function(epoch) 0.5)))
})
Message
Adjusting learning rate of group 1 to 0.0005
Adjusting learning rate of group 1 to 0.0003
Adjusting learning rate of group 1 to 0.0001
Adjusting learning rate of group 1 to 0.0001
Adjusting learning rate of group 1 to 0.0000
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(dl,
verbose = FALSE, epochs = 20, callbacks = list(luz_callback_lr_scheduler(
torch::lr_reduce_on_plateau, verbose = TRUE, patience = 2, threshold = 0.1)))
})
Message
Epoch 7: reducing learning rate of group 1 to 1.0000e-05
Epoch 10: reducing learning rate of group 1 to 1.0000e-06
Epoch 13: reducing learning rate of group 1 to 1.0000e-07
Epoch 16: reducing learning rate of group 1 to 1.0000e-08
progressbar appears with training and validation
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(dl,
verbose = TRUE, epochs = 2, valid_data = dl)
})
Message
1/50 [>------------------------------------------------------] - Loss: 3.2392
2/50 [=>-----------------------------------------------------] - Loss: 2.5641
3/50 [==>----------------------------------------------------] - Loss: 2.6243
4/50 [===>---------------------------------------------------] - Loss: 2.3165
5/50 [=====>-------------------------------------------------] - Loss: 2.2277
6/50 [======>------------------------------------------------] - Loss: 2.2308
7/50 [=======>-----------------------------------------------] - Loss: 2.0433
8/50 [========>----------------------------------------------] - Loss: 2.0283
9/50 [=========>---------------------------------------------] - Loss: 1.9632
10/50 [==========>-------------------------------------------] - Loss: 1.9339
11/50 [===========>------------------------------------------] - Loss: 1.8714
12/50 [============>-----------------------------------------] - Loss: 1.8386
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20/50 [=====================>--------------------------------] - Loss: 1.6838
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28/50 [=============================>------------------------] - Loss: 1.6827
29/50 [==============================>-----------------------] - Loss: 1.6975
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48/50 [===================================================>--] - Loss: 1.5549
49/50 [====================================================>-] - Loss: 1.5511
50/50 [======================================================] - Loss: 1.5753
1/50 [>-------------------------------------------------------] - Loss: 3.058
2/50 [=>-----------------------------------------------------] - Loss: 2.3999
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29/50 [==============================>-----------------------] - Loss: 1.6095
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48/50 [====================================================>--] - Loss: 1.492
49/50 [====================================================>-] - Loss: 1.4894
50/50 [======================================================] - Loss: 1.5147
Train metrics: Loss: 1.5753
Valid metrics: Loss: 1.5147
Epoch 2/2
1/50 [>-------------------------------------------------------] - Loss: 3.058
2/50 [=>-----------------------------------------------------] - Loss: 2.3985
3/50 [==>------------------------------------------------------] - Loss: 2.44
4/50 [===>---------------------------------------------------] - Loss: 2.1585
5/50 [=====>-------------------------------------------------] - Loss: 2.0652
6/50 [======>------------------------------------------------] - Loss: 2.0794
7/50 [=======>-----------------------------------------------] - Loss: 1.8978
8/50 [========>----------------------------------------------] - Loss: 1.8849
9/50 [=========>---------------------------------------------] - Loss: 1.8173
10/50 [==========>-------------------------------------------] - Loss: 1.7974
11/50 [===========>------------------------------------------] - Loss: 1.7397
12/50 [============>-----------------------------------------] - Loss: 1.7136
13/50 [=============>----------------------------------------] - Loss: 1.7068
14/50 [==============>---------------------------------------] - Loss: 1.6291
15/50 [===============>--------------------------------------] - Loss: 1.6564
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17/50 [=================>------------------------------------] - Loss: 1.6159
18/50 [==================>-----------------------------------] - Loss: 1.5883
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21/50 [======================>-------------------------------] - Loss: 1.5415
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50/50 [======================================================] - Loss: 1.4667
1/50 [>------------------------------------------------------] - Loss: 2.9115
2/50 [=>-----------------------------------------------------] - Loss: 2.2618
3/50 [==>----------------------------------------------------] - Loss: 2.2873
4/50 [===>---------------------------------------------------] - Loss: 2.0295
5/50 [=====>-------------------------------------------------] - Loss: 1.9356
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25/50 [==========================>---------------------------] - Loss: 1.5585
26/50 [===========================>--------------------------] - Loss: 1.5498
27/50 [============================>-------------------------] - Loss: 1.5131
28/50 [=============================>------------------------] - Loss: 1.4901
29/50 [==============================>-----------------------] - Loss: 1.5067
30/50 [===============================>----------------------] - Loss: 1.5068
31/50 [=================================>---------------------] - Loss: 1.498
32/50 [==================================>-------------------] - Loss: 1.4695
33/50 [===================================>------------------] - Loss: 1.4631
34/50 [====================================>-----------------] - Loss: 1.4507
35/50 [=====================================>----------------] - Loss: 1.4318
36/50 [======================================>---------------] - Loss: 1.4442
37/50 [=======================================>--------------] - Loss: 1.4285
38/50 [========================================>-------------] - Loss: 1.4337
39/50 [=========================================>------------] - Loss: 1.4304
40/50 [===========================================>-----------] - Loss: 1.427
41/50 [============================================>----------] - Loss: 1.417
42/50 [============================================>---------] - Loss: 1.4135
43/50 [=============================================>--------] - Loss: 1.4081
44/50 [===============================================>------] - Loss: 1.4083
45/50 [================================================>-----] - Loss: 1.4054
46/50 [=================================================>----] - Loss: 1.4193
47/50 [==================================================>---] - Loss: 1.4052
48/50 [===================================================>--] - Loss: 1.3944
49/50 [====================================================>-] - Loss: 1.3916
50/50 [======================================================] - Loss: 1.4162
Train metrics: Loss: 1.4667
Valid metrics: Loss: 1.4162
improve error message when you provide a unitinitilized callback
x Callbacks must have class <LuzCallback> but got <function>
i Perhaps you forgot to initialize the callback?
can get progress when using iterable datasets
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(
get_iterable_ds(), verbose = TRUE, epochs = 2, valid_data = get_iterable_ds(),
)
})
Message
1/unk [-] - Loss: 1.776
2/unk [\] - Loss: 1.6358
3/unk [|] - Loss: 1.6954
1/unk [-] - Loss: 1.664
2/unk [\] - Loss: 1.4837
3/unk [|] - Loss: 1.6957
4/unk [/] - Loss: 1.3467
Train metrics: Loss: 1.6954
Valid metrics: Loss: 1.3467
Epoch 2/2
1/unk [-] - Loss: 1.763
2/unk [\] - Loss: 1.6215
3/unk [|] - Loss: 1.6819
1/unk [-] - Loss: 1.659
2/unk [\] - Loss: 1.4758
3/unk [|] - Loss: 1.6868
4/unk [/] - Loss: 1.34
Train metrics: Loss: 1.6819
Valid metrics: Loss: 1.34
mlverse/luz documentation built on Sept. 19, 2024, 11:20 p.m.
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callback lr scheduler
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(dl,
verbose = FALSE, epochs = 5, callbacks = list(luz_callback_lr_scheduler(
torch::lr_multiplicative, verbose = TRUE, lr_lambda = function(epoch) 0.5)))
})
Message
Adjusting learning rate of group 1 to 0.0005
Adjusting learning rate of group 1 to 0.0003
Adjusting learning rate of group 1 to 0.0001
Adjusting learning rate of group 1 to 0.0001
Adjusting learning rate of group 1 to 0.0000
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(dl,
verbose = FALSE, epochs = 20, callbacks = list(luz_callback_lr_scheduler(
torch::lr_reduce_on_plateau, verbose = TRUE, patience = 2, threshold = 0.1)))
})
Message
Epoch 7: reducing learning rate of group 1 to 1.0000e-05
Epoch 10: reducing learning rate of group 1 to 1.0000e-06
Epoch 13: reducing learning rate of group 1 to 1.0000e-07
Epoch 16: reducing learning rate of group 1 to 1.0000e-08
progressbar appears with training and validation
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(dl,
verbose = TRUE, epochs = 2, valid_data = dl)
})
Message
1/50 [>------------------------------------------------------] - Loss: 3.2392
2/50 [=>-----------------------------------------------------] - Loss: 2.5641
3/50 [==>----------------------------------------------------] - Loss: 2.6243
4/50 [===>---------------------------------------------------] - Loss: 2.3165
5/50 [=====>-------------------------------------------------] - Loss: 2.2277
6/50 [======>------------------------------------------------] - Loss: 2.2308
7/50 [=======>-----------------------------------------------] - Loss: 2.0433
8/50 [========>----------------------------------------------] - Loss: 2.0283
9/50 [=========>---------------------------------------------] - Loss: 1.9632
10/50 [==========>-------------------------------------------] - Loss: 1.9339
11/50 [===========>------------------------------------------] - Loss: 1.8714
12/50 [============>-----------------------------------------] - Loss: 1.8386
13/50 [=============>----------------------------------------] - Loss: 1.8343
14/50 [==============>---------------------------------------] - Loss: 1.7534
15/50 [===============>--------------------------------------] - Loss: 1.7802
16/50 [================>-------------------------------------] - Loss: 1.7656
17/50 [=================>------------------------------------] - Loss: 1.7414
18/50 [==================>-----------------------------------] - Loss: 1.7096
19/50 [====================>---------------------------------] - Loss: 1.7159
20/50 [=====================>--------------------------------] - Loss: 1.6838
21/50 [======================>--------------------------------] - Loss: 1.658
22/50 [=======================>------------------------------] - Loss: 1.7045
23/50 [========================>-----------------------------] - Loss: 1.7719
24/50 [=========================>-----------------------------] - Loss: 1.777
25/50 [==========================>---------------------------] - Loss: 1.7646
26/50 [============================>--------------------------] - Loss: 1.755
27/50 [============================>-------------------------] - Loss: 1.7117
28/50 [=============================>------------------------] - Loss: 1.6827
29/50 [==============================>-----------------------] - Loss: 1.6975
30/50 [===============================>----------------------] - Loss: 1.7005
31/50 [==================================>---------------------] - Loss: 1.69
32/50 [==================================>-------------------] - Loss: 1.6555
33/50 [===================================>------------------] - Loss: 1.6458
34/50 [====================================>-----------------] - Loss: 1.6328
35/50 [=====================================>----------------] - Loss: 1.6116
36/50 [======================================>---------------] - Loss: 1.6237
37/50 [=======================================>--------------] - Loss: 1.6044
38/50 [========================================>-------------] - Loss: 1.6068
39/50 [==========================================>------------] - Loss: 1.601
40/50 [==========================================>-----------] - Loss: 1.5955
41/50 [===========================================>----------] - Loss: 1.5831
42/50 [============================================>---------] - Loss: 1.5794
43/50 [=============================================>--------] - Loss: 1.5718
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45/50 [================================================>-----] - Loss: 1.5656
46/50 [=================================================>----] - Loss: 1.5815
47/50 [==================================================>---] - Loss: 1.5662
48/50 [===================================================>--] - Loss: 1.5549
49/50 [====================================================>-] - Loss: 1.5511
50/50 [======================================================] - Loss: 1.5753
1/50 [>-------------------------------------------------------] - Loss: 3.058
2/50 [=>-----------------------------------------------------] - Loss: 2.3999
3/50 [==>----------------------------------------------------] - Loss: 2.4435
4/50 [===>---------------------------------------------------] - Loss: 2.1623
5/50 [=====>-------------------------------------------------] - Loss: 2.0719
6/50 [======>------------------------------------------------] - Loss: 2.0865
7/50 [=======>-----------------------------------------------] - Loss: 1.9057
8/50 [========>-----------------------------------------------] - Loss: 1.894
9/50 [=========>---------------------------------------------] - Loss: 1.8281
10/50 [==========>-------------------------------------------] - Loss: 1.8074
11/50 [===========>------------------------------------------] - Loss: 1.7501
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29/50 [==============================>-----------------------] - Loss: 1.6095
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48/50 [====================================================>--] - Loss: 1.492
49/50 [====================================================>-] - Loss: 1.4894
50/50 [======================================================] - Loss: 1.5147
Train metrics: Loss: 1.5753
Valid metrics: Loss: 1.5147
Epoch 2/2
1/50 [>-------------------------------------------------------] - Loss: 3.058
2/50 [=>-----------------------------------------------------] - Loss: 2.3985
3/50 [==>------------------------------------------------------] - Loss: 2.44
4/50 [===>---------------------------------------------------] - Loss: 2.1585
5/50 [=====>-------------------------------------------------] - Loss: 2.0652
6/50 [======>------------------------------------------------] - Loss: 2.0794
7/50 [=======>-----------------------------------------------] - Loss: 1.8978
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9/50 [=========>---------------------------------------------] - Loss: 1.8173
10/50 [==========>-------------------------------------------] - Loss: 1.7974
11/50 [===========>------------------------------------------] - Loss: 1.7397
12/50 [============>-----------------------------------------] - Loss: 1.7136
13/50 [=============>----------------------------------------] - Loss: 1.7068
14/50 [==============>---------------------------------------] - Loss: 1.6291
15/50 [===============>--------------------------------------] - Loss: 1.6564
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17/50 [=================>------------------------------------] - Loss: 1.6159
18/50 [==================>-----------------------------------] - Loss: 1.5883
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20/50 [=====================>--------------------------------] - Loss: 1.5686
21/50 [======================>-------------------------------] - Loss: 1.5415
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48/50 [===================================================>--] - Loss: 1.4468
49/50 [=====================================================>-] - Loss: 1.443
50/50 [======================================================] - Loss: 1.4667
1/50 [>------------------------------------------------------] - Loss: 2.9115
2/50 [=>-----------------------------------------------------] - Loss: 2.2618
3/50 [==>----------------------------------------------------] - Loss: 2.2873
4/50 [===>---------------------------------------------------] - Loss: 2.0295
5/50 [=====>-------------------------------------------------] - Loss: 1.9356
6/50 [======>------------------------------------------------] - Loss: 1.9591
7/50 [=======>-----------------------------------------------] - Loss: 1.7846
8/50 [========>----------------------------------------------] - Loss: 1.7735
9/50 [=========>---------------------------------------------] - Loss: 1.7053
10/50 [==========>--------------------------------------------] - Loss: 1.693
11/50 [===========>------------------------------------------] - Loss: 1.6397
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14/50 [==============>---------------------------------------] - Loss: 1.5375
15/50 [===============>--------------------------------------] - Loss: 1.5657
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17/50 [=================>------------------------------------] - Loss: 1.5263
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19/50 [====================>---------------------------------] - Loss: 1.5122
20/50 [=====================>---------------------------------] - Loss: 1.488
21/50 [======================>-------------------------------] - Loss: 1.4613
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23/50 [========================>-----------------------------] - Loss: 1.5645
24/50 [=========================>----------------------------] - Loss: 1.5671
25/50 [==========================>---------------------------] - Loss: 1.5585
26/50 [===========================>--------------------------] - Loss: 1.5498
27/50 [============================>-------------------------] - Loss: 1.5131
28/50 [=============================>------------------------] - Loss: 1.4901
29/50 [==============================>-----------------------] - Loss: 1.5067
30/50 [===============================>----------------------] - Loss: 1.5068
31/50 [=================================>---------------------] - Loss: 1.498
32/50 [==================================>-------------------] - Loss: 1.4695
33/50 [===================================>------------------] - Loss: 1.4631
34/50 [====================================>-----------------] - Loss: 1.4507
35/50 [=====================================>----------------] - Loss: 1.4318
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40/50 [===========================================>-----------] - Loss: 1.427
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45/50 [================================================>-----] - Loss: 1.4054
46/50 [=================================================>----] - Loss: 1.4193
47/50 [==================================================>---] - Loss: 1.4052
48/50 [===================================================>--] - Loss: 1.3944
49/50 [====================================================>-] - Loss: 1.3916
50/50 [======================================================] - Loss: 1.4162
Train metrics: Loss: 1.4667
Valid metrics: Loss: 1.4162
improve error message when you provide a unitinitilized callback
x Callbacks must have class <LuzCallback> but got <function>
i Perhaps you forgot to initialize the callback?
can get progress when using iterable datasets
Code
expect_message({
output <- mod %>% set_hparams(input_size = 10, output_size = 1) %>% fit(
get_iterable_ds(), verbose = TRUE, epochs = 2, valid_data = get_iterable_ds(),
)
})
Message
1/unk [-] - Loss: 1.776
2/unk [\] - Loss: 1.6358
3/unk [|] - Loss: 1.6954
1/unk [-] - Loss: 1.664
2/unk [\] - Loss: 1.4837
3/unk [|] - Loss: 1.6957
4/unk [/] - Loss: 1.3467
Train metrics: Loss: 1.6954
Valid metrics: Loss: 1.3467
Epoch 2/2
1/unk [-] - Loss: 1.763
2/unk [\] - Loss: 1.6215
3/unk [|] - Loss: 1.6819
1/unk [-] - Loss: 1.659
2/unk [\] - Loss: 1.4758
3/unk [|] - Loss: 1.6868
4/unk [/] - Loss: 1.34
Train metrics: Loss: 1.6819
Valid metrics: Loss: 1.34
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