Comments (2)
OK so one workaround to this is creating a specific way for these deprecated optimisers to work using a separate, tf_compat_optimiser
class, since these use compat
(https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdagradDAOptimizer).
This gets around the first issue of the iterations
object not being found, since this is something to do with the more recent TF2 work.
However there is a new error:
library(devtools)
#> Loading required package: usethis
library(testthat)
#>
#> Attaching package: 'testthat'
#> The following object is masked from 'package:devtools':
#>
#> test_file
load_all(".")
#> ℹ Loading greta
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#>
#> Loaded Tensorflow version 2.10.0
source(here::here("tests/testthat/helpers.R"))
x <- rnorm(5, 2, 0.1)
z <- variable(dim = 5)
distribution(x) <- normal(z, 0.1)
m <- model(z)
opt(m, optimiser = adagrad_da())
#> Warning: This optimiser is deprecated and will be removed in greta "0.6.0".
#> Please use a different optimiser.
#> Error in py_call_impl(callable, dots$args, dots$keywords): tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute ResourceApplyAdagradDA as input #7(zero-based) was expected to be a int64 tensor but is a int32 tensor [Op:ResourceApplyAdagradDA]
opt(m, optimiser = proximal_adagrad())
#> Warning: This optimiser is deprecated and will be removed in greta "0.6.0".
#> Please use a different optimiser.
#> Error in py_call_impl(callable, dots$args, dots$keywords): tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute ResourceApplyProximalAdagrad as input #5(zero-based) was expected to be a float tensor but is a double tensor [Op:ResourceApplyProximalAdagrad]
opt(m, optimiser = proximal_gradient_descent())
#> Warning: This optimiser is deprecated and will be removed in greta "0.6.0".
#> Please use a different optimiser.
#> Error in py_call_impl(callable, dots$args, dots$keywords): tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute ResourceApplyProximalGradientDescent as input #4(zero-based) was expected to be a float tensor but is a double tensor [Op:ResourceApplyProximalGradientDescent]
Created on 2022-12-15 with reprex v2.0.2
Session info
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It might have something to do with some of the argument inputs being the wrong data type, hopefully that is it.
from greta.
Turns out this was resolved in TF1 greta, had to borrow a few things from that but it was exactly to do with the data types that were being expected - this might provide some clues for some of these other TF errors
library(devtools)
#> Loading required package: usethis
library(testthat)
#>
#> Attaching package: 'testthat'
#> The following object is masked from 'package:devtools':
#>
#> test_file
load_all(".")
#> ℹ Loading greta
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#>
#> Loaded Tensorflow version 2.10.0
source(here::here("tests/testthat/helpers.R"))
x <- rnorm(5, 2, 0.1)
z <- variable(dim = 5)
distribution(x) <- normal(z, 0.1)
m <- model(z)
opt(m, optimiser = adagrad_da())
#> Warning: This optimiser is deprecated and will be removed in greta "0.6.0".
#> Please use a different optimiser.
#> $par
#> $par$z
#> [1] 2.045885 1.873639 1.978477 2.156678 1.992692
#>
#>
#> $value
#> [1] -6.918232
#>
#> $iterations
#> [1] 33
#>
#> $convergence
#> [1] 0
opt(m, optimiser = proximal_adagrad())
#> Warning: This optimiser is deprecated and will be removed in greta "0.6.0".
#> Please use a different optimiser.
#> $par
#> $par$z
#> [1] 2.045952 1.873644 1.978487 2.156708 1.992707
#>
#>
#> $value
#> [1] -6.918232
#>
#> $iterations
#> [1] 23
#>
#> $convergence
#> [1] 0
opt(m, optimiser = proximal_gradient_descent())
#> Warning: This optimiser is deprecated and will be removed in greta "0.6.0".
#> Please use a different optimiser.
#> $par
#> $par$z
#> [1] 2.045954 1.873645 1.978500 2.156809 1.992711
#>
#>
#> $value
#> [1] -6.918233
#>
#> $iterations
#> [1] 2
#>
#> $convergence
#> [1] 0
Created on 2022-12-16 with reprex v2.0.2
Session info
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#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────
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Related Issues (20)
- Implement new TFP optimisers?
- Extract convergence and iteration information from TF2 optimisers
- Deprecate optimisers accessed through compat?
- Improve documentation on non-independent sampling using greta_array()
- install_greta_deps() claims success when it has actually failed (windows) HOT 3
- Creating 'greta-env' conda environment using python v3.7 failed HOT 2
- TF2 Errors in `test_inference.R` - bad proposals are not rejected HOT 1
- TF2 error - Failure (`test_inference.R:293)`: mcmc supports rwmh sampler with normal/uniform proposals HOT 3
- TF2 error - Failure (`test_inference.R:317`): mcmc supports slice sampler with single precision models
- TF2 error - Failure (`test_inference.R:351`): numerical issues are handled in mcmc
- TF2 - Failure (`test_inference.R:375`): mcmc works in parallel - cannot find TF installation when using `future`
- TF2 error - "adam" optimiser doesn't converge
- Implement `outer` function in greta
- `dim<-` doesn't alway create an unknowns object
- error retrieving diagonal from rectangular matrix HOT 2
- Implement helper function to provide better initial values HOT 2
- M1 TF2 cholesky factors all out of whack in calculate HOT 4
- Alternative backends with greta (e.g., torch, xtensor) HOT 1
- Fix `compare_iid_samples` for wishart
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