- github
- ocaml
- opam-repository
- e26ca3
- compilers,4.14,conf-m4.1,revdeps,owl-opt.0.0.1
(not at the head of any monitored branch or PR)
2026-03-21 18:50.21: New job: test owl-opt.0.0.1 with conf-m4.1, using opam dev
from https://github.com/ocaml/opam-repository.git#refs/pull/29579/head (e26ca330660bcb49f8bb8e3d37703d278be8c4bd)
on debian-13-ocaml-4.14/amd64
To reproduce locally:
cd $(mktemp -d)
git clone --recursive "https://github.com/ocaml/opam-repository.git" && cd "opam-repository" && git fetch origin "refs/pull/29579/head" && git reset --hard e26ca330
git fetch origin master
git merge --no-edit 76bf2ed9443fdee37e6f046c6295d358be3f8598
cat > ../Dockerfile <<'END-OF-DOCKERFILE'
FROM ocaml/opam:debian-13-ocaml-4.14@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e
USER 1000:1000
WORKDIR /home/opam
RUN sudo ln -f /usr/bin/opam-dev /usr/bin/opam
RUN opam init --reinit -ni
RUN opam option solver=builtin-0install && opam config report
ENV OPAMDOWNLOADJOBS="1"
ENV OPAMERRLOGLEN="0"
ENV OPAMPRECISETRACKING="1"
ENV CI="true"
ENV OPAM_REPO_CI="true"
RUN rm -rf opam-repository/
COPY --chown=1000:1000 . opam-repository/
RUN opam repository set-url --strict default opam-repository/
RUN opam update --depexts || true
RUN opam pin add -k version -yn conf-m4.1 1
RUN opam reinstall conf-m4.1; \
res=$?; \
test "$res" != 31 && exit "$res"; \
export OPAMCLI=2.0; \
build_dir=$(opam var prefix)/.opam-switch/build; \
failed=$(ls "$build_dir"); \
partial_fails=""; \
for pkg in $failed; do \
if opam show -f x-ci-accept-failures: "$pkg" | grep -qF "\"debian-13\""; then \
echo "A package failed and has been disabled for CI using the 'x-ci-accept-failures' field."; \
fi; \
test "$pkg" != 'conf-m4.1' && partial_fails="$partial_fails $pkg"; \
done; \
test "${partial_fails}" != "" && echo "opam-repo-ci detected dependencies failing: ${partial_fails}"; \
exit 1
RUN opam reinstall owl-opt.0.0.1; \
res=$?; \
test "$res" != 31 && exit "$res"; \
export OPAMCLI=2.0; \
build_dir=$(opam var prefix)/.opam-switch/build; \
failed=$(ls "$build_dir"); \
partial_fails=""; \
for pkg in $failed; do \
if opam show -f x-ci-accept-failures: "$pkg" | grep -qF "\"debian-13\""; then \
echo "A package failed and has been disabled for CI using the 'x-ci-accept-failures' field."; \
fi; \
test "$pkg" != 'owl-opt.0.0.1' && partial_fails="$partial_fails $pkg"; \
done; \
test "${partial_fails}" != "" && echo "opam-repo-ci detected dependencies failing: ${partial_fails}"; \
exit 1
RUN (opam reinstall --with-test owl-opt.0.0.1) || true
RUN opam reinstall --with-test --verbose owl-opt.0.0.1; \
res=$?; \
test "$res" != 31 && exit "$res"; \
export OPAMCLI=2.0; \
build_dir=$(opam var prefix)/.opam-switch/build; \
failed=$(ls "$build_dir"); \
partial_fails=""; \
for pkg in $failed; do \
if opam show -f x-ci-accept-failures: "$pkg" | grep -qF "\"debian-13\""; then \
echo "A package failed and has been disabled for CI using the 'x-ci-accept-failures' field."; \
fi; \
test "$pkg" != 'owl-opt.0.0.1' && partial_fails="$partial_fails $pkg"; \
done; \
test "${partial_fails}" != "" && echo "opam-repo-ci detected dependencies failing: ${partial_fails}"; \
exit 1
END-OF-DOCKERFILE
docker build -f ../Dockerfile .
2026-03-21 18:50.21: Using cache hint "ocaml/opam:debian-13-ocaml-4.14@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e-conf-m4.1-owl-opt.0.0.1-e26ca330660bcb49f8bb8e3d37703d278be8c4bd"
2026-03-21 18:50.21: Using OBuilder spec:
((from ocaml/opam:debian-13-ocaml-4.14@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e)
(user (uid 1000) (gid 1000))
(workdir /home/opam)
(run (shell "sudo ln -f /usr/bin/opam-dev /usr/bin/opam"))
(run (network host)
(shell "opam init --reinit --config .opamrc-sandbox -ni"))
(run (shell "opam option solver=builtin-0install && opam config report"))
(env OPAMDOWNLOADJOBS 1)
(env OPAMERRLOGLEN 0)
(env OPAMPRECISETRACKING 1)
(env CI true)
(env OPAM_REPO_CI true)
(run (shell "rm -rf opam-repository/"))
(copy (src .) (dst opam-repository/))
(run (shell "opam repository set-url --strict default opam-repository/"))
(run (network host)
(shell "opam update --depexts || true"))
(run (shell "opam pin add -k version -yn conf-m4.1 1"))
(run (cache (opam-archives (target /home/opam/.opam/download-cache)))
(network host)
(shell "opam reinstall conf-m4.1;\
\n res=$?;\
\n test \"$res\" != 31 && exit \"$res\";\
\n export OPAMCLI=2.0;\
\n build_dir=$(opam var prefix)/.opam-switch/build;\
\n failed=$(ls \"$build_dir\");\
\n partial_fails=\"\";\
\n for pkg in $failed; do\
\n if opam show -f x-ci-accept-failures: \"$pkg\" | grep -qF \"\\\"debian-13\\\"\"; then\
\n echo \"A package failed and has been disabled for CI using the 'x-ci-accept-failures' field.\";\
\n fi;\
\n test \"$pkg\" != 'conf-m4.1' && partial_fails=\"$partial_fails $pkg\";\
\n done;\
\n test \"${partial_fails}\" != \"\" && echo \"opam-repo-ci detected dependencies failing: ${partial_fails}\";\
\n exit 1"))
(run (cache (opam-archives (target /home/opam/.opam/download-cache)))
(network host)
(shell "opam reinstall owl-opt.0.0.1;\
\n res=$?;\
\n test \"$res\" != 31 && exit \"$res\";\
\n export OPAMCLI=2.0;\
\n build_dir=$(opam var prefix)/.opam-switch/build;\
\n failed=$(ls \"$build_dir\");\
\n partial_fails=\"\";\
\n for pkg in $failed; do\
\n if opam show -f x-ci-accept-failures: \"$pkg\" | grep -qF \"\\\"debian-13\\\"\"; then\
\n echo \"A package failed and has been disabled for CI using the 'x-ci-accept-failures' field.\";\
\n fi;\
\n test \"$pkg\" != 'owl-opt.0.0.1' && partial_fails=\"$partial_fails $pkg\";\
\n done;\
\n test \"${partial_fails}\" != \"\" && echo \"opam-repo-ci detected dependencies failing: ${partial_fails}\";\
\n exit 1"))
(run (network host)
(shell "(opam reinstall --with-test owl-opt.0.0.1) || true"))
(run (shell "opam reinstall --with-test --verbose owl-opt.0.0.1;\
\n res=$?;\
\n test \"$res\" != 31 && exit \"$res\";\
\n export OPAMCLI=2.0;\
\n build_dir=$(opam var prefix)/.opam-switch/build;\
\n failed=$(ls \"$build_dir\");\
\n partial_fails=\"\";\
\n for pkg in $failed; do\
\n if opam show -f x-ci-accept-failures: \"$pkg\" | grep -qF \"\\\"debian-13\\\"\"; then\
\n echo \"A package failed and has been disabled for CI using the 'x-ci-accept-failures' field.\";\
\n fi;\
\n test \"$pkg\" != 'owl-opt.0.0.1' && partial_fails=\"$partial_fails $pkg\";\
\n done;\
\n test \"${partial_fails}\" != \"\" && echo \"opam-repo-ci detected dependencies failing: ${partial_fails}\";\
\n exit 1"))
)
2026-03-21 18:50.21: Waiting for resource in pool OCluster
2026-03-21 21:02.03: Waiting for worker…
2026-03-21 21:04.26: Got resource from pool OCluster
Building on doris.caelum.ci.dev
All commits already cached
HEAD is now at 76bf2ed944 Merge pull request #29572 from hannesm/release-letsencrypt-v2.0.0
Updating 76bf2ed944..e26ca33066
Fast-forward
packages/conf-findutils/conf-findutils.1/opam | 2 ++
packages/conf-m4/conf-m4.1/opam | 1 +
.../conf-mingw-w64-bzip2-x86_64/conf-mingw-w64-bzip2-x86_64.1/opam | 4 ++--
packages/conf-mingw-w64-gcc-x86_64/conf-mingw-w64-gcc-x86_64.1/opam | 4 ++--
packages/conf-mingw-w64-gmp-x86_64/conf-mingw-w64-gmp-x86_64.1/opam | 4 ++--
.../conf-mingw-w64-pkgconf-x86_64.1/opam | 6 +++---
.../conf-mingw-w64-zlib-x86_64/conf-mingw-w64-zlib-x86_64.1/opam | 4 ++--
.../conf-mingw-w64-zstd-x86_64/conf-mingw-w64-zstd-x86_64.1/opam | 4 ++--
packages/conf-perl/conf-perl.2/opam | 1 +
9 files changed, 17 insertions(+), 13 deletions(-)
(from ocaml/opam:debian-13-ocaml-4.14@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e)
Unable to find image 'ocaml/opam:debian-13-ocaml-4.14@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e' locally
docker.io/ocaml/opam@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e: Pulling from ocaml/opam
866771c43bf5: Already exists
1e49bea09367: Already exists
e793768537e6: Already exists
ed323d3d481a: Already exists
7df34a5cd5f1: Already exists
fd712d3eb935: Already exists
4b9fb8c99118: Already exists
9d9a01948b94: Already exists
0f1514f90b32: Already exists
e1ec5a753447: Already exists
03cc323e2f71: Already exists
c09c08ea9749: Already exists
b36b619f8e6b: Already exists
195344ca5274: Already exists
228ee78582a6: Already exists
504bde1c25b3: Already exists
9d8b1356c89f: Already exists
9d8b1356c89f: Already exists
568fb6dda155: Already exists
c499c9198aea: Already exists
048e5e358118: Already exists
871ca48eb45d: Already exists
4f4fb700ef54: Already exists
a5a2568b9df9: Already exists
068cf3106ac8: Already exists
559f54ec9b29: Already exists
798ffd96fde5: Already exists
e9a891bf80d7: Already exists
d720cfe12674: Already exists
c81c932f4a91: Already exists
79f24fa3bb11: Already exists
8c1debcd8c20: Already exists
1bb2cfea7250: Already exists
2b3d3ca75e4c: Already exists
557cacaf263c: Already exists
d10483022eef: Already exists
7b62a90d8223: Already exists
28ce8ea66e72: Already exists
d975909ea717: Already exists
5c215c69c247: Already exists
e7c082452a54: Already exists
d1a4c61b613c: Pulling fs layer
0195a6679dc6: Pulling fs layer
d1a4c61b613c: Waiting
0195a6679dc6: Waiting
410dfa46d7be: Pulling fs layer
b7b221f39cbe: Pulling fs layer
410dfa46d7be: Waiting
b7b221f39cbe: Waiting
0195a6679dc6: Verifying Checksum
0195a6679dc6: Download complete
410dfa46d7be: Download complete
b7b221f39cbe: Download complete
d1a4c61b613c: Download complete
d1a4c61b613c: Pull complete
0195a6679dc6: Pull complete
410dfa46d7be: Pull complete
b7b221f39cbe: Pull complete
Digest: sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e
Status: Downloaded newer image for ocaml/opam@sha256:37323dc71cac48a3e4688e16b45b95486f3cc440c55ab3f83114e8973362f41e
2026-03-21 21:04.34 ---> using "32cd5b5baf995c02200cf270da597dbb25becd220af2c200c00b8b241a742195" from cache
/: (user (uid 1000) (gid 1000))
/: (workdir /home/opam)
/home/opam: (run (shell "sudo ln -f /usr/bin/opam-dev /usr/bin/opam"))
2026-03-21 21:04.34 ---> using "f3ed7bdbef828c9c0b079b10505c5f05c3c9adcca11ce5bf2dac2a4183e099d8" from cache
/home/opam: (run (network host)
(shell "opam init --reinit --config .opamrc-sandbox -ni"))
Configuring from /home/opam/.opamrc-sandbox, then /home/opam/.opamrc, and finally from built-in defaults.
Checking for available remotes: rsync and local, git.
- you won't be able to use mercurial repositories unless you install the hg command on your system.
- you won't be able to use darcs repositories unless you install the darcs command on your system.
This version of opam requires an update to the layout of /home/opam/.opam from version 2.0 to version 2.2, which can't be reverted.
You may want to back it up before going further.
Continue? [Y/n] y
[NOTE] The 'jobs' option was reset, its value was 71 and its new value will vary according to the current number of cores on your machine. You can restore the fixed value using:
opam option jobs=71 --global
Format upgrade done.
<><> Updating repositories ><><><><><><><><><><><><><><><><><><><><><><><><><><>
[default] Initialised
2026-03-21 21:04.34 ---> using "5bf6adb7b45bb7e0c215b8f509c71a8dae73a9a2060efcc27df9d4ef6c6d3350" from cache
/home/opam: (run (shell "opam option solver=builtin-0install && opam config report"))
Set to 'builtin-0install' the field solver in global configuration
# opam config report
# opam-version 2.5.0
# self-upgrade no
# system arch=x86_64 os=linux os-distribution=debian os-version=13
# solver builtin-0install
# install-criteria -changed,-count[avoid-version,solution]
# upgrade-criteria -count[avoid-version,solution]
# jobs 255
# repositories 1 (version-controlled)
# pinned 1 (version)
# current-switch 4.14
# invariant ["ocaml-base-compiler" {= "4.14.2"}]
# compiler-packages ocaml-base-compiler.4.14.2, ocaml-options-vanilla.1
# ocaml:native true
# ocaml:native-tools true
# ocaml:native-dynlink true
# ocaml:stubsdir /home/opam/.opam/4.14/lib/ocaml/stublibs:/home/opam/.opam/4.14/lib/ocaml
# ocaml:preinstalled false
# ocaml:compiler 4.14.2
2026-03-21 21:04.34 ---> using "0546f18fa5979677ee22eb9f2fcf19ab371564e845d317c2c70e41dd97dc22dd" from cache
/home/opam: (env OPAMDOWNLOADJOBS 1)
/home/opam: (env OPAMERRLOGLEN 0)
/home/opam: (env OPAMPRECISETRACKING 1)
/home/opam: (env CI true)
/home/opam: (env OPAM_REPO_CI true)
/home/opam: (run (shell "rm -rf opam-repository/"))
2026-03-21 21:04.34 ---> using "0a7188cbe95f4fe0ff26694977eb8794c975bdb534078da98a06d6c7373289b4" from cache
/home/opam: (copy (src .) (dst opam-repository/))
2026-03-21 21:04.35 ---> using "64b0b1c5956282be62155632b73d49a512770a013b8177e3bd97cfd5e30573f3" from cache
/home/opam: (run (shell "opam repository set-url --strict default opam-repository/"))
[default] Initialised
2026-03-21 21:04.35 ---> using "5bbc18b187922ee43ab28303966f314ef55d58cc243daaa1a62d891c4651643d" from cache
/home/opam: (run (network host)
(shell "opam update --depexts || true"))
+ /usr/bin/sudo "apt-get" "update"
- Get:1 http://deb.debian.org/debian trixie InRelease [140 kB]
- Get:2 http://deb.debian.org/debian trixie-updates InRelease [47.3 kB]
- Get:3 http://deb.debian.org/debian-security trixie-security InRelease [43.4 kB]
- Get:4 http://deb.debian.org/debian trixie/main amd64 Packages [9671 kB]
- Get:5 http://deb.debian.org/debian-security trixie-security/main amd64 Packages [113 kB]
- Fetched 10.0 MB in 3min 6s (53.7 kB/s)
- Reading package lists...
-
2026-03-21 21:04.35 ---> using "26c4cf2d2932a5613ac2db8ae671186642ac802d5db4f44c87661f5676362dec" from cache
/home/opam: (run (shell "opam pin add -k version -yn conf-m4.1 1"))
conf-m4 is now pinned to version 1
2026-03-21 21:04.35 ---> using "1f550c38f5bac1e80ecbaf30222372f1ba650bb720c00e72e38ddd72856eafa1" from cache
/home/opam: (run (cache (opam-archives (target /home/opam/.opam/download-cache)))
(network host)
(shell "opam reinstall conf-m4.1;\
\n res=$?;\
\n test \"$res\" != 31 && exit \"$res\";\
\n export OPAMCLI=2.0;\
\n build_dir=$(opam var prefix)/.opam-switch/build;\
\n failed=$(ls \"$build_dir\");\
\n partial_fails=\"\";\
\n for pkg in $failed; do\
\n if opam show -f x-ci-accept-failures: \"$pkg\" | grep -qF \"\\\"debian-13\\\"\"; then\
\n echo \"A package failed and has been disabled for CI using the 'x-ci-accept-failures' field.\";\
\n fi;\
\n test \"$pkg\" != 'conf-m4.1' && partial_fails=\"$partial_fails $pkg\";\
\n done;\
\n test \"${partial_fails}\" != \"\" && echo \"opam-repo-ci detected dependencies failing: ${partial_fails}\";\
\n exit 1"))
conf-m4.1 is not installed. Install it? [Y/n] y
The following actions will be performed:
=== install 1 package
- install conf-m4 1 (pinned)
The following system packages will first need to be installed:
m4
<><> Handling external dependencies <><><><><><><><><><><><><><><><><><><><><><>
opam believes some required external dependencies are missing. opam can:
> 1. Run apt-get to install them (may need root/sudo access)
2. Display the recommended apt-get command and wait while you run it manually (e.g. in another terminal)
3. Continue anyway, and, upon success, permanently register that this external dependency is present, but not detectable
4. Abort the installation
[1/2/3/4] 1
+ /usr/bin/sudo "apt-get" "install" "-qq" "-yy" "m4"
- Selecting previously unselected package m4.
- (Reading database ...
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 20623 files and directories currently installed.)
- Preparing to unpack .../archives/m4_1.4.19-8_amd64.deb ...
- Unpacking m4 (1.4.19-8) ...
- Setting up m4 (1.4.19-8) ...
<><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><>
-> installed conf-m4.1
Done.
# To update the current shell environment, run: eval $(opam env)
2026-03-21 21:04.35 ---> using "e0baf65572c68338ca7ad6c8769e7aeb1e3d03e85ea3541782477400153674e3" from cache
/home/opam: (run (cache (opam-archives (target /home/opam/.opam/download-cache)))
(network host)
(shell "opam reinstall owl-opt.0.0.1;\
\n res=$?;\
\n test \"$res\" != 31 && exit \"$res\";\
\n export OPAMCLI=2.0;\
\n build_dir=$(opam var prefix)/.opam-switch/build;\
\n failed=$(ls \"$build_dir\");\
\n partial_fails=\"\";\
\n for pkg in $failed; do\
\n if opam show -f x-ci-accept-failures: \"$pkg\" | grep -qF \"\\\"debian-13\\\"\"; then\
\n echo \"A package failed and has been disabled for CI using the 'x-ci-accept-failures' field.\";\
\n fi;\
\n test \"$pkg\" != 'owl-opt.0.0.1' && partial_fails=\"$partial_fails $pkg\";\
\n done;\
\n test \"${partial_fails}\" != \"\" && echo \"opam-repo-ci detected dependencies failing: ${partial_fails}\";\
\n exit 1"))
owl-opt.0.0.1 is not installed. Install it? [Y/n] y
The following actions will be performed:
=== install 21 packages
- install base v0.16.4 [required by owl, ppx-owl-opt]
- install camlzip 1.14 [required by npy]
- install conf-openblas 0.2.3 [required by owl]
- install conf-pkg-config 4 [required by conf-zlib]
- install conf-zlib 1 [required by camlzip]
- install csexp 1.5.2 [required by dune-configurator]
- install ctypes 0.24.0 [required by owl]
- install dune 3.22.0 [required by owl-opt]
- install dune-configurator 3.22.0 [required by owl]
- install integers 0.7.0 [required by ctypes]
- install npy 0.0.9 [required by owl]
- install ocaml-compiler-libs v0.12.4 [required by ppxlib]
- install ocamlfind 1.9.8 [required by camlzip]
- install owl 1.2 [required by owl-opt]
- install owl-base 1.2 [required by owl]
- install owl-opt 0.0.1
- install ppx-owl-opt 0.0.1 [required by owl-opt]
- install ppx_derivers 1.2.1 [required by ppxlib]
- install ppxlib 0.37.0 [required by ppx-owl-opt]
- install sexplib0 v0.16.0 [required by base, ppxlib]
- install stdlib-shims 0.3.0 [required by ppxlib]
The following system packages will first need to be installed:
liblapacke-dev libopenblas-dev pkg-config zlib1g-dev
<><> Handling external dependencies <><><><><><><><><><><><><><><><><><><><><><>
opam believes some required external dependencies are missing. opam can:
> 1. Run apt-get to install them (may need root/sudo access)
2. Display the recommended apt-get command and wait while you run it manually (e.g. in another terminal)
3. Continue anyway, and, upon success, permanently register that this external dependency is present, but not detectable
4. Abort the installation
[1/2/3/4] 1
+ /usr/bin/sudo "apt-get" "install" "-qq" "-yy" "liblapacke-dev" "libopenblas-dev" "pkg-config" "zlib1g-dev"
- Selecting previously unselected package libblas3:amd64.
- (Reading database ...
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 20710 files and directories currently installed.)
- Preparing to unpack .../00-libblas3_3.12.1-6_amd64.deb ...
- Unpacking libblas3:amd64 (3.12.1-6) ...
- Selecting previously unselected package libblas-dev:amd64.
- Preparing to unpack .../01-libblas-dev_3.12.1-6_amd64.deb ...
- Unpacking libblas-dev:amd64 (3.12.1-6) ...
- Selecting previously unselected package libgfortran5:amd64.
- Preparing to unpack .../02-libgfortran5_14.2.0-19_amd64.deb ...
- Unpacking libgfortran5:amd64 (14.2.0-19) ...
- Selecting previously unselected package libopenblas0-pthread:amd64.
- Preparing to unpack .../03-libopenblas0-pthread_0.3.29+ds-3_amd64.deb ...
- Unpacking libopenblas0-pthread:amd64 (0.3.29+ds-3) ...
- Selecting previously unselected package liblapack3:amd64.
- Preparing to unpack .../04-liblapack3_3.12.1-6_amd64.deb ...
- Unpacking liblapack3:amd64 (3.12.1-6) ...
- Selecting previously unselected package libopenblas-pthread-dev:amd64.
- Preparing to unpack .../05-libopenblas-pthread-dev_0.3.29+ds-3_amd64.deb ...
- Unpacking libopenblas-pthread-dev:amd64 (0.3.29+ds-3) ...
- Selecting previously unselected package liblapack-dev:amd64.
- Preparing to unpack .../06-liblapack-dev_3.12.1-6_amd64.deb ...
- Unpacking liblapack-dev:amd64 (3.12.1-6) ...
- Selecting previously unselected package libtmglib3:amd64.
- Preparing to unpack .../07-libtmglib3_3.12.1-6_amd64.deb ...
- Unpacking libtmglib3:amd64 (3.12.1-6) ...
- Selecting previously unselected package liblapacke:amd64.
- Preparing to unpack .../08-liblapacke_3.12.1-6_amd64.deb ...
- Unpacking liblapacke:amd64 (3.12.1-6) ...
- Selecting previously unselected package libtmglib-dev:amd64.
- Preparing to unpack .../09-libtmglib-dev_3.12.1-6_amd64.deb ...
- Unpacking libtmglib-dev:amd64 (3.12.1-6) ...
- Selecting previously unselected package liblapacke-dev:amd64.
- Preparing to unpack .../10-liblapacke-dev_3.12.1-6_amd64.deb ...
- Unpacking liblapacke-dev:amd64 (3.12.1-6) ...
- Selecting previously unselected package libopenblas0:amd64.
- Preparing to unpack .../11-libopenblas0_0.3.29+ds-3_amd64.deb ...
- Unpacking libopenblas0:amd64 (0.3.29+ds-3) ...
- Selecting previously unselected package libopenblas-dev:amd64.
- Preparing to unpack .../12-libopenblas-dev_0.3.29+ds-3_amd64.deb ...
- Unpacking libopenblas-dev:amd64 (0.3.29+ds-3) ...
- Selecting previously unselected package libpkgconf3:amd64.
- Preparing to unpack .../13-libpkgconf3_1.8.1-4_amd64.deb ...
- Unpacking libpkgconf3:amd64 (1.8.1-4) ...
- Selecting previously unselected package pkgconf-bin.
- Preparing to unpack .../14-pkgconf-bin_1.8.1-4_amd64.deb ...
- Unpacking pkgconf-bin (1.8.1-4) ...
- Selecting previously unselected package pkgconf:amd64.
- Preparing to unpack .../15-pkgconf_1.8.1-4_amd64.deb ...
- Unpacking pkgconf:amd64 (1.8.1-4) ...
- Selecting previously unselected package pkg-config:amd64.
- Preparing to unpack .../16-pkg-config_1.8.1-4_amd64.deb ...
- Unpacking pkg-config:amd64 (1.8.1-4) ...
- Selecting previously unselected package zlib1g-dev:amd64.
- Preparing to unpack .../17-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1+b1_amd64.deb ...
- Unpacking zlib1g-dev:amd64 (1:1.3.dfsg+really1.3.1-1+b1) ...
- Setting up libblas3:amd64 (3.12.1-6) ...
- update-alternatives: using /usr/lib/x86_64-linux-gnu/blas/libblas.so.3 to provide /usr/lib/x86_64-linux-gnu/libblas.so.3 (libblas.so.3-x86_64-linux-gnu) in auto mode
- Setting up libpkgconf3:amd64 (1.8.1-4) ...
- Setting up pkgconf-bin (1.8.1-4) ...
- Setting up libgfortran5:amd64 (14.2.0-19) ...
- Setting up zlib1g-dev:amd64 (1:1.3.dfsg+really1.3.1-1+b1) ...
- Setting up libblas-dev:amd64 (3.12.1-6) ...
- update-alternatives: using /usr/lib/x86_64-linux-gnu/blas/libblas.so to provide /usr/lib/x86_64-linux-gnu/libblas.so (libblas.so-x86_64-linux-gnu) in auto mode
- Setting up liblapack3:amd64 (3.12.1-6) ...
- update-alternatives: using /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/x86_64-linux-gnu/liblapack.so.3 (liblapack.so.3-x86_64-linux-gnu) in auto mode
- Setting up libopenblas0-pthread:amd64 (0.3.29+ds-3) ...
- update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 to provide /usr/lib/x86_64-linux-gnu/libblas.so.3 (libblas.so.3-x86_64-linux-gnu) in auto mode
- update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3 to provide /usr/lib/x86_64-linux-gnu/liblapack.so.3 (liblapack.so.3-x86_64-linux-gnu) in auto mode
- update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblas.so.0 to provide /usr/lib/x86_64-linux-gnu/libopenblas.so.0 (libopenblas.so.0-x86_64-linux-gnu) in auto mode
- Setting up pkgconf:amd64 (1.8.1-4) ...
- Setting up libtmglib3:amd64 (3.12.1-6) ...
- Setting up liblapack-dev:amd64 (3.12.1-6) ...
- update-alternatives: using /usr/lib/x86_64-linux-gnu/lapack/liblapack.so to provide /usr/lib/x86_64-linux-gnu/liblapack.so (liblapack.so-x86_64-linux-gnu) in auto mode
- Setting up pkg-config:amd64 (1.8.1-4) ...
- Setting up libopenblas0:amd64 (0.3.29+ds-3) ...
- Setting up liblapacke:amd64 (3.12.1-6) ...
- Setting up libtmglib-dev:amd64 (3.12.1-6) ...
- Setting up libopenblas-pthread-dev:amd64 (0.3.29+ds-3) ...
- update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so to provide /usr/lib/x86_64-linux-gnu/libblas.so (libblas.so-x86_64-linux-gnu) in auto mode
- update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so to provide /usr/lib/x86_64-linux-gnu/liblapack.so (liblapack.so-x86_64-linux-gnu) in auto mode
- update-alternatives: using /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblas.so to provide /usr/lib/x86_64-linux-gnu/libopenblas.so (libopenblas.so-x86_64-linux-gnu) in auto mode
- Setting up liblapacke-dev:amd64 (3.12.1-6) ...
- Setting up libopenblas-dev:amd64 (0.3.29+ds-3) ...
- Processing triggers for libc-bin (2.41-12+deb13u1) ...
<><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><>
-> retrieved base.v0.16.4 (cached)
-> retrieved camlzip.1.14 (cached)
-> retrieved conf-openblas.0.2.3 (cached)
-> retrieved csexp.1.5.2 (cached)
-> retrieved ctypes.0.24.0 (cached)
-> installed conf-pkg-config.4
-> installed conf-zlib.1
-> installed conf-openblas.0.2.3
-> retrieved dune.3.22.0, dune-configurator.3.22.0 (cached)
-> retrieved integers.0.7.0 (cached)
-> retrieved npy.0.0.9 (cached)
-> retrieved ocaml-compiler-libs.v0.12.4 (cached)
-> retrieved ocamlfind.1.9.8 (cached)
-> retrieved owl.1.2, owl-base.1.2 (cached)
-> retrieved owl-opt.0.0.1, ppx-owl-opt.0.0.1 (https://opam.ocaml.org/cache)
-> retrieved ppx_derivers.1.2.1 (cached)
-> retrieved ppxlib.0.37.0 (cached)
-> retrieved sexplib0.v0.16.0 (cached)
-> retrieved stdlib-shims.0.3.0 (cached)
-> installed ocamlfind.1.9.8
-> installed camlzip.1.14
-> installed dune.3.22.0
-> installed csexp.1.5.2
-> installed npy.0.0.9
-> installed ocaml-compiler-libs.v0.12.4
-> installed ppx_derivers.1.2.1
-> installed sexplib0.v0.16.0
-> installed stdlib-shims.0.3.0
-> installed integers.0.7.0
-> installed dune-configurator.3.22.0
-> installed owl-base.1.2
-> installed base.v0.16.4
-> installed ctypes.0.24.0
-> installed ppxlib.0.37.0
-> installed ppx-owl-opt.0.0.1
-> installed owl.1.2
-> installed owl-opt.0.0.1
Done.
# To update the current shell environment, run: eval $(opam env)
2026-03-21 21:06.30 ---> saved as "9c16888f6fcdbe6084d5196720d028694a480c6feafc7435ccb74be835c99a6a"
/home/opam: (run (network host)
(shell "(opam reinstall --with-test owl-opt.0.0.1) || true"))
The following actions will be performed:
=== recompile 1 package
- recompile owl-opt 0.0.1
<><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><>
-> retrieved owl-opt.0.0.1 (https://opam.ocaml.org/cache)
-> removed owl-opt.0.0.1
-> installed owl-opt.0.0.1
Done.
# To update the current shell environment, run: eval $(opam env)
2026-03-21 21:06.37 ---> saved as "a43882056701e4460f463e14b48d0739d5e7c3710a021d0e028a98e4afcdc08a"
/home/opam: (run (shell "opam reinstall --with-test --verbose owl-opt.0.0.1;\
\n res=$?;\
\n test \"$res\" != 31 && exit \"$res\";\
\n export OPAMCLI=2.0;\
\n build_dir=$(opam var prefix)/.opam-switch/build;\
\n failed=$(ls \"$build_dir\");\
\n partial_fails=\"\";\
\n for pkg in $failed; do\
\n if opam show -f x-ci-accept-failures: \"$pkg\" | grep -qF \"\\\"debian-13\\\"\"; then\
\n echo \"A package failed and has been disabled for CI using the 'x-ci-accept-failures' field.\";\
\n fi;\
\n test \"$pkg\" != 'owl-opt.0.0.1' && partial_fails=\"$partial_fails $pkg\";\
\n done;\
\n test \"${partial_fails}\" != \"\" && echo \"opam-repo-ci detected dependencies failing: ${partial_fails}\";\
\n exit 1"))
The following actions will be performed:
=== recompile 1 package
- recompile owl-opt 0.0.1
<><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Processing 1/4: [owl-opt.0.0.1: extract]
-> retrieved owl-opt.0.0.1 (cached)
Processing 2/4: [owl-opt: dune build]
+ /home/opam/.opam/opam-init/hooks/sandbox.sh "build" "dune" "build" "-p" "owl-opt" "-j" "255" (CWD=/home/opam/.opam/4.14/.opam-switch/build/owl-opt.0.0.1)
- File "dune-project", line 2, characters 11-14:
- 2 | (using fmt 1.1)
- ^^^
- Warning: Version 1.1 of integration with automatic formatters is not
- supported until version 1.7 of the dune language.
- Supported versions of this extension in version 1.5 of the dune language:
- - 1.0
Processing 2/4: [owl-opt: dune runtest]
+ /home/opam/.opam/opam-init/hooks/sandbox.sh "build" "dune" "runtest" "examples/opt" "-p" "owl-opt" "-j" "255" (CWD=/home/opam/.opam/4.14/.opam-switch/build/owl-opt.0.0.1)
- File "dune-project", line 2, characters 11-14:
- 2 | (using fmt 1.1)
- ^^^
- Warning: Version 1.1 of integration with automatic formatters is not
- supported until version 1.7 of the dune language.
- Supported versions of this extension in version 1.5 of the dune language:
- - 1.0
- (cd _build/default/examples/opt && ./gd.exe)
-
iter: 0 | loss: 6.874058
iter: 1 | loss: 6.874058
iter: 2 | loss: 6.873710
iter: 3 | loss: 6.873363
iter: 4 | loss: 6.873015
iter: 5 | loss: 6.872668
iter: 6 | loss: 6.872320
iter: 7 | loss: 6.871973
iter: 8 | loss: 6.871625
iter: 9 | loss: 6.871278
iter: 10 | loss: 6.870930
iter: 11 | loss: 6.870583
iter: 12 | loss: 6.870235
iter: 13 | loss: 6.869888
iter: 14 | loss: 6.869540
iter: 15 | loss: 6.869193
iter: 16 | loss: 6.868845
iter: 17 | loss: 6.868498
iter: 18 | loss: 6.868150
iter: 19 | loss: 6.867803
iter: 20 | loss: 6.867455
iter: 21 | loss: 6.867108
iter: 22 | loss: 6.866760
iter: 23 | loss: 6.866413
iter: 24 | loss: 6.866065
iter: 25 | loss: 6.865718
iter: 26 | loss: 6.865370
iter: 27 | loss: 6.865023
iter: 28 | loss: 6.864675
iter: 29 | loss: 6.864328
iter: 30 | loss: 6.863980
iter: 31 | loss: 6.863633
iter: 32 | loss: 6.863285
iter: 33 | loss: 6.862938
iter: 34 | loss: 6.862591
iter: 35 | loss: 6.862243
iter: 36 | loss: 6.861896
iter: 37 | loss: 6.861548
iter: 38 | loss: 6.861201
iter: 39 | loss: 6.860853
iter: 40 | loss: 6.860506
iter: 41 | loss: 6.860158
iter: 42 | loss: 6.859811
iter: 43 | loss: 6.859463
iter: 44 | loss: 6.859116
iter: 45 | loss: 6.858768
iter: 46 | loss: 6.858421
iter: 47 | loss: 6.858073
iter: 48 | loss: 6.857726
iter: 49 | loss: 6.857378
iter: 50 | loss: 6.857031
iter: 51 | loss: 6.856683
iter: 52 | loss: 6.856336
iter: 53 | loss: 6.855988
iter: 54 | loss: 6.855641
iter: 55 | loss: 6.855293
iter: 56 | loss: 6.854946
iter: 57 | loss: 6.854598
iter: 58 | loss: 6.854251
iter: 59 | loss: 6.853903
iter: 60 | loss: 6.853556
iter: 61 | loss: 6.853208
iter: 62 | loss: 6.852861
iter: 63 | loss: 6.852513
iter: 64 | loss: 6.852166
iter: 65 | loss: 6.851818
iter: 66 | loss: 6.851471
iter: 67 | loss: 6.851123
iter: 68 | loss: 6.850776
iter: 69 | loss: 6.850428
iter: 70 | loss: 6.850081
iter: 71 | loss: 6.849733
iter: 72 | loss: 6.849386
iter: 73 | loss: 6.849038
iter: 74 | loss: 6.848691
iter: 75 | loss: 6.848343
iter: 76 | loss: 6.847996
iter: 77 | loss: 6.847648
iter: 78 | loss: 6.847301
iter: 79 | loss: 6.846954
iter: 80 | loss: 6.846606
iter: 81 | loss: 6.846259
iter: 82 | loss: 6.845911
iter: 83 | loss: 6.845564
iter: 84 | loss: 6.845216
iter: 85 | loss: 6.844869
iter: 86 | loss: 6.844521
iter: 87 | loss: 6.844174
iter: 88 | loss: 6.843826
iter: 89 | loss: 6.843479
iter: 90 | loss: 6.843131
iter: 91 | loss: 6.842784
iter: 92 | loss: 6.842436
iter: 93 | loss: 6.842089
iter: 94 | loss: 6.841741
iter: 95 | loss: 6.841394
iter: 96 | loss: 6.841046
iter: 97 | loss: 6.840699
iter: 98 | loss: 6.840351
iter: 99 | loss: 6.840004
iter: 100 | loss: 6.839656
iter: 101 | loss: 6.839309
iter: 102 | loss: 6.838961
iter: 103 | loss: 6.838614
iter: 104 | loss: 6.838266
iter: 105 | loss: 6.837919
iter: 106 | loss: 6.837571
iter: 107 | loss: 6.837224
iter: 108 | loss: 6.836876
iter: 109 | loss: 6.836529
iter: 110 | loss: 6.836181
iter: 111 | loss: 6.835834
iter: 112 | loss: 6.835486
iter: 113 | loss: 6.835139
iter: 114 | loss: 6.834791
iter: 115 | loss: 6.834444
iter: 116 | loss: 6.834096
iter: 117 | loss: 6.833749
iter: 118 | loss: 6.833401
iter: 119 | loss: 6.833054
iter: 120 | loss: 6.832706
iter: 121 | loss: 6.832359
iter: 122 | loss: 6.832012
iter: 123 | loss: 6.831664
iter: 124 | loss: 6.831317
iter: 125 | loss: 6.830969
iter: 126 | loss: 6.830622
iter: 127 | loss: 6.830274
iter: 128 | loss: 6.829927
iter: 129 | loss: 6.829579
iter: 130 | loss: 6.829232
iter: 131 | loss: 6.828884
iter: 132 | loss: 6.828537
iter: 133 | loss: 6.828189
iter: 134 | loss: 6.827842
iter: 135 | loss: 6.827494
iter: 136 | loss: 6.827147
iter: 137 | loss: 6.826799
iter: 138 | loss: 6.826452
iter: 139 | loss: 6.826104
iter: 140 | loss: 6.825757
iter: 141 | loss: 6.825409
iter: 142 | loss: 6.825062
iter: 143 | loss: 6.824714
iter: 144 | loss: 6.824367
iter: 145 | loss: 6.824019
iter: 146 | loss: 6.823672
iter: 147 | loss: 6.823324
iter: 148 | loss: 6.822977
iter: 149 | loss: 6.822629
iter: 150 | loss: 6.822282
iter: 151 | loss: 6.821934
iter: 152 | loss: 6.821587
iter: 153 | loss: 6.821239
iter: 154 | loss: 6.820892
iter: 155 | loss: 6.820544
iter: 156 | loss: 6.820197
iter: 157 | loss: 6.819849
iter: 158 | loss: 6.819502
iter: 159 | loss: 6.819154
iter: 160 | loss: 6.818807
iter: 161 | loss: 6.818459
iter: 162 | loss: 6.818112
iter: 163 | loss: 6.817764
iter: 164 | loss: 6.817417
iter: 165 | loss: 6.817069
iter: 166 | loss: 6.816722
iter: 167 | loss: 6.816375
iter: 168 | loss: 6.816027
iter: 169 | loss: 6.815680
iter: 170 | loss: 6.815332
iter: 171 | loss: 6.814985
iter: 172 | loss: 6.814637
iter: 173 | loss: 6.814290
iter: 174 | loss: 6.813942
iter: 175 | loss: 6.813595
iter: 176 | loss: 6.813247
iter: 177 | loss: 6.812900
iter: 178 | loss: 6.812552
iter: 179 | loss: 6.812205
iter: 180 | loss: 6.811857
iter: 181 | loss: 6.811510
iter: 182 | loss: 6.811162
iter: 183 | loss: 6.810815
iter: 184 | loss: 6.810467
iter: 185 | loss: 6.810120
iter: 186 | loss: 6.809772
iter: 187 | loss: 6.809425
iter: 188 | loss: 6.809077
iter: 189 | loss: 6.808730
iter: 190 | loss: 6.808382
iter: 191 | loss: 6.808035
iter: 192 | loss: 6.807687
iter: 193 | loss: 6.807340
iter: 194 | loss: 6.806992
iter: 195 | loss: 6.806645
iter: 196 | loss: 6.806297
iter: 197 | loss: 6.805950
iter: 198 | loss: 6.805602
iter: 199 | loss: 6.805255
iter: 200 | loss: 6.804907
iter: 201 | loss: 6.804560
iter: 202 | loss: 6.804212
iter: 203 | loss: 6.803865
iter: 204 | loss: 6.803517
iter: 205 | loss: 6.803170
iter: 206 | loss: 6.802822
iter: 207 | loss: 6.802475
iter: 208 | loss: 6.802127
iter: 209 | loss: 6.801780
iter: 210 | loss: 6.801433
iter: 211 | loss: 6.801085
iter: 212 | loss: 6.800738
iter: 213 | loss: 6.800390
iter: 214 | loss: 6.800043
iter: 215 | loss: 6.799695
iter: 216 | loss: 6.799348
iter: 217 | loss: 6.799000
iter: 218 | loss: 6.798653
iter: 219 | loss: 6.798305
iter: 220 | loss: 6.797958
iter: 221 | loss: 6.797610
iter: 222 | loss: 6.797263
iter: 223 | loss: 6.796915
iter: 224 | loss: 6.796568
iter: 225 | loss: 6.796220
iter: 226 | loss: 6.795873
iter: 227 | loss: 6.795525
iter: 228 | loss: 6.795178
iter: 229 | loss: 6.794830
iter: 230 | loss: 6.794483
iter: 231 | loss: 6.794135
iter: 232 | loss: 6.793788
iter: 233 | loss: 6.793440
iter: 234 | loss: 6.793093
iter: 235 | loss: 6.792745
iter: 236 | loss: 6.792398
iter: 237 | loss: 6.792050
iter: 238 | loss: 6.791703
iter: 239 | loss: 6.791355
iter: 240 | loss: 6.791008
iter: 241 | loss: 6.790660
iter: 242 | loss: 6.790313
iter: 243 | loss: 6.789965
iter: 244 | loss: 6.789618
iter: 245 | loss: 6.789270
iter: 246 | loss: 6.788923
iter: 247 | loss: 6.788575
iter: 248 | loss: 6.788228
iter: 249 | loss: 6.787880
iter: 250 | loss: 6.787533
iter: 251 | loss: 6.787185
iter: 252 | loss: 6.786838
iter: 253 | loss: 6.786491
iter: 254 | loss: 6.786143
iter: 255 | loss: 6.785796
iter: 256 | loss: 6.785448
iter: 257 | loss: 6.785101
iter: 258 | loss: 6.784753
iter: 259 | loss: 6.784406
iter: 260 | loss: 6.784058
iter: 261 | loss: 6.783711
iter: 262 | loss: 6.783363
iter: 263 | loss: 6.783016
iter: 264 | loss: 6.782668
iter: 265 | loss: 6.782321
iter: 266 | loss: 6.781973
iter: 267 | loss: 6.781626
iter: 268 | loss: 6.781278
iter: 269 | loss: 6.780931
iter: 270 | loss: 6.780583
iter: 271 | loss: 6.780236
iter: 272 | loss: 6.779888
iter: 273 | loss: 6.779541
iter: 274 | loss: 6.779193
iter: 275 | loss: 6.778846
iter: 276 | loss: 6.778498
iter: 277 | loss: 6.778151
iter: 278 | loss: 6.777803
iter: 279 | loss: 6.777456
iter: 280 | loss: 6.777108
iter: 281 | loss: 6.776761
iter: 282 | loss: 6.776413
iter: 283 | loss: 6.776066
iter: 284 | loss: 6.775718
iter: 285 | loss: 6.775371
iter: 286 | loss: 6.775023
iter: 287 | loss: 6.774676
iter: 288 | loss: 6.774328
iter: 289 | loss: 6.773981
iter: 290 | loss: 6.773633
iter: 291 | loss: 6.773286
iter: 292 | loss: 6.772938
iter: 293 | loss: 6.772591
iter: 294 | loss: 6.772243
iter: 295 | loss: 6.771896
iter: 296 | loss: 6.771548
iter: 297 | loss: 6.771201
iter: 298 | loss: 6.770854
iter: 299 | loss: 6.770506
iter: 300 | loss: 6.770159
iter: 301 | loss: 6.769811
iter: 302 | loss: 6.769464
iter: 303 | loss: 6.769116
iter: 304 | loss: 6.768769
iter: 305 | loss: 6.768421
iter: 306 | loss: 6.768074
iter: 307 | loss: 6.767726
iter: 308 | loss: 6.767379
iter: 309 | loss: 6.767031
iter: 310 | loss: 6.766684
iter: 311 | loss: 6.766336
iter: 312 | loss: 6.765989
iter: 313 | loss: 6.765641
iter: 314 | loss: 6.765294
iter: 315 | loss: 6.764946
iter: 316 | loss: 6.764599
iter: 317 | loss: 6.764251
iter: 318 | loss: 6.763904
iter: 319 | loss: 6.763556
iter: 320 | loss: 6.763209
iter: 321 | loss: 6.762861
iter: 322 | loss: 6.762514
iter: 323 | loss: 6.762166
iter: 324 | loss: 6.761819
iter: 325 | loss: 6.761471
iter: 326 | loss: 6.761124
iter: 327 | loss: 6.760776
iter: 328 | loss: 6.760429
iter: 329 | loss: 6.760081
iter: 330 | loss: 6.759734
iter: 331 | loss: 6.759386
iter: 332 | loss: 6.759039
iter: 333 | loss: 6.758691
iter: 334 | loss: 6.758344
iter: 335 | loss: 6.757996
iter: 336 | loss: 6.757649
iter: 337 | loss: 6.757301
iter: 338 | loss: 6.756954
iter: 339 | loss: 6.756606
iter: 340 | loss: 6.756259
iter: 341 | loss: 6.755912
iter: 342 | loss: 6.755564
iter: 343 | loss: 6.755217
iter: 344 | loss: 6.754869
iter: 345 | loss: 6.754522
iter: 346 | loss: 6.754174
iter: 347 | loss: 6.753827
iter: 348 | loss: 6.753479
iter: 349 | loss: 6.753132
iter: 350 | loss: 6.752784
iter: 351 | loss: 6.752437
iter: 352 | loss: 6.752089
iter: 353 | loss: 6.751742
iter: 354 | loss: 6.751394
iter: 355 | loss: 6.751047
iter: 356 | loss: 6.750699
iter: 357 | loss: 6.750352
iter: 358 | loss: 6.750004
iter: 359 | loss: 6.749657
iter: 360 | loss: 6.749309
iter: 361 | loss: 6.748962
iter: 362 | loss: 6.748614
iter: 363 | loss: 6.748267
iter: 364 | loss: 6.747919
iter: 365 | loss: 6.747572
iter: 366 | loss: 6.747224
iter: 367 | loss: 6.746877
iter: 368 | loss: 6.746529
iter: 369 | loss: 6.746182
iter: 370 | loss: 6.745834
iter: 371 | loss: 6.745487
iter: 372 | loss: 6.745139
iter: 373 | loss: 6.744792
iter: 374 | loss: 6.744444
iter: 375 | loss: 6.744097
iter: 376 | loss: 6.743749
iter: 377 | loss: 6.743402
iter: 378 | loss: 6.743054
iter: 379 | loss: 6.742707
iter: 380 | loss: 6.742359
iter: 381 | loss: 6.742012
iter: 382 | loss: 6.741664
iter: 383 | loss: 6.741317
iter: 384 | loss: 6.740969
iter: 385 | loss: 6.740622
iter: 386 | loss: 6.740275
iter: 387 | loss: 6.739927
iter: 388 | loss: 6.739580
iter: 389 | loss: 6.739232
iter: 390 | loss: 6.738885
iter: 391 | loss: 6.738537
iter: 392 | loss: 6.738190
iter: 393 | loss: 6.737842
iter: 394 | loss: 6.737495
iter: 395 | loss: 6.737147
iter: 396 | loss: 6.736800
iter: 397 | loss: 6.736452
iter: 398 | loss: 6.736105
iter: 399 | loss: 6.735757
iter: 400 | loss: 6.735410
iter: 401 | loss: 6.735062
iter: 402 | loss: 6.734715
iter: 403 | loss: 6.734367
iter: 404 | loss: 6.734020
iter: 405 | loss: 6.733672
iter: 406 | loss: 6.733325
iter: 407 | loss: 6.732977
iter: 408 | loss: 6.732630
iter: 409 | loss: 6.732282
iter: 410 | loss: 6.731935
iter: 411 | loss: 6.731587
iter: 412 | loss: 6.731240
iter: 413 | loss: 6.730892
iter: 414 | loss: 6.730545
iter: 415 | loss: 6.730197
iter: 416 | loss: 6.729850
iter: 417 | loss: 6.729502
iter: 418 | loss: 6.729155
iter: 419 | loss: 6.728807
iter: 420 | loss: 6.728460
iter: 421 | loss: 6.728112
iter: 422 | loss: 6.727765
iter: 423 | loss: 6.727417
iter: 424 | loss: 6.727070
iter: 425 | loss: 6.726722
iter: 426 | loss: 6.726375
iter: 427 | loss: 6.726027
iter: 428 | loss: 6.725680
iter: 429 | loss: 6.725333
iter: 430 | loss: 6.724985
iter: 431 | loss: 6.724638
iter: 432 | loss: 6.724290
iter: 433 | loss: 6.723943
iter: 434 | loss: 6.723595
iter: 435 | loss: 6.723248
iter: 436 | loss: 6.722900
iter: 437 | loss: 6.722553
iter: 438 | loss: 6.722205
iter: 439 | loss: 6.721858
iter: 440 | loss: 6.721510
iter: 441 | loss: 6.721163
iter: 442 | loss: 6.720815
iter: 443 | loss: 6.720468
iter: 444 | loss: 6.720120
iter: 445 | loss: 6.719773
iter: 446 | loss: 6.719425
iter: 447 | loss: 6.719078
iter: 448 | loss: 6.718730
iter: 449 | loss: 6.718383
iter: 450 | loss: 6.718035
iter: 451 | loss: 6.717688
iter: 452 | loss: 6.717340
iter: 453 | loss: 6.716993
iter: 454 | loss: 6.716645
iter: 455 | loss: 6.716298
iter: 456 | loss: 6.715950
iter: 457 | loss: 6.715603
iter: 458 | loss: 6.715255
iter: 459 | loss: 6.714908
iter: 460 | loss: 6.714560
iter: 461 | loss: 6.714213
iter: 462 | loss: 6.713865
iter: 463 | loss: 6.713518
iter: 464 | loss: 6.713170
iter: 465 | loss: 6.712823
iter: 466 | loss: 6.712475
iter: 467 | loss: 6.712128
iter: 468 | loss: 6.711780
iter: 469 | loss: 6.711433
iter: 470 | loss: 6.711085
iter: 471 | loss: 6.710738
iter: 472 | loss: 6.710391
iter: 473 | loss: 6.710043
iter: 474 | loss: 6.709696
iter: 475 | loss: 6.709348
iter: 476 | loss: 6.709001
iter: 477 | loss: 6.708653
iter: 478 | loss: 6.708306
iter: 479 | loss: 6.707958
iter: 480 | loss: 6.707611
iter: 481 | loss: 6.707263
iter: 482 | loss: 6.706916
iter: 483 | loss: 6.706568
iter: 484 | loss: 6.706221
iter: 485 | loss: 6.705873
iter: 486 | loss: 6.705526
iter: 487 | loss: 6.705178
iter: 488 | loss: 6.704831
iter: 489 | loss: 6.704483
iter: 490 | loss: 6.704136
iter: 491 | loss: 6.703788
iter: 492 | loss: 6.703441
iter: 493 | loss: 6.703093
iter: 494 | loss: 6.702746
iter: 495 | loss: 6.702398
iter: 496 | loss: 6.702051
iter: 497 | loss: 6.701703
iter: 498 | loss: 6.701356
iter: 499 | loss: 6.701008
iter: 500 | loss: 6.700661
iter: 501 | loss: 6.700313
iter: 502 | loss: 6.699966
iter: 503 | loss: 6.699618
iter: 504 | loss: 6.699271
iter: 505 | loss: 6.698923
iter: 506 | loss: 6.698576
iter: 507 | loss: 6.698228
iter: 508 | loss: 6.697881
iter: 509 | loss: 6.697533
iter: 510 | loss: 6.697186
iter: 511 | loss: 6.696838
iter: 512 | loss: 6.696491
iter: 513 | loss: 6.696143
iter: 514 | loss: 6.695796
iter: 515 | loss: 6.695448
iter: 516 | loss: 6.695101
iter: 517 | loss: 6.694754
iter: 518 | loss: 6.694406
iter: 519 | loss: 6.694059
iter: 520 | loss: 6.693711
iter: 521 | loss: 6.693364
iter: 522 | loss: 6.693016
iter: 523 | loss: 6.692669
iter: 524 | loss: 6.692321
iter: 525 | loss: 6.691974
iter: 526 | loss: 6.691626
iter: 527 | loss: 6.691279
iter: 528 | loss: 6.690931
iter: 529 | loss: 6.690584
iter: 530 | loss: 6.690236
iter: 531 | loss: 6.689889
iter: 532 | loss: 6.689541
iter: 533 | loss: 6.689194
iter: 534 | loss: 6.688846
iter: 535 | loss: 6.688499
iter: 536 | loss: 6.688151
iter: 537 | loss: 6.687804
iter: 538 | loss: 6.687456
iter: 539 | loss: 6.687109
iter: 540 | loss: 6.686761
iter: 541 | loss: 6.686414
iter: 542 | loss: 6.686066
iter: 543 | loss: 6.685719
iter: 544 | loss: 6.685371
iter: 545 | loss: 6.685024
iter: 546 | loss: 6.684676
iter: 547 | loss: 6.684329
iter: 548 | loss: 6.683981
iter: 549 | loss: 6.683634
iter: 550 | loss: 6.683286
iter: 551 | loss: 6.682939
iter: 552 | loss: 6.682591
iter: 553 | loss: 6.682244
iter: 554 | loss: 6.681896
iter: 555 | loss: 6.681549
iter: 556 | loss: 6.681201
iter: 557 | loss: 6.680854
iter: 558 | loss: 6.680506
iter: 559 | loss: 6.680159
iter: 560 | loss: 6.679812
iter: 561 | loss: 6.679464
iter: 562 | loss: 6.679117
iter: 563 | loss: 6.678769
iter: 564 | loss: 6.678422
iter: 565 | loss: 6.678074
iter: 566 | loss: 6.677727
iter: 567 | loss: 6.677379
iter: 568 | loss: 6.677032
iter: 569 | loss: 6.676684
iter: 570 | loss: 6.676337
iter: 571 | loss: 6.675989
iter: 572 | loss: 6.675642
iter: 573 | loss: 6.675294
iter: 574 | loss: 6.674947
iter: 575 | loss: 6.674599
iter: 576 | loss: 6.674252
iter: 577 | loss: 6.673904
iter: 578 | loss: 6.673557
iter: 579 | loss: 6.673209
iter: 580 | loss: 6.672862
iter: 581 | loss: 6.672514
iter: 582 | loss: 6.672167
iter: 583 | loss: 6.671819
iter: 584 | loss: 6.671472
iter: 585 | loss: 6.671124
iter: 586 | loss: 6.670777
iter: 587 | loss: 6.670429
iter: 588 | loss: 6.670082
iter: 589 | loss: 6.669734
iter: 590 | loss: 6.669387
iter: 591 | loss: 6.669039
iter: 592 | loss: 6.668692
iter: 593 | loss: 6.668344
iter: 594 | loss: 6.667997
iter: 595 | loss: 6.667649
iter: 596 | loss: 6.667302
iter: 597 | loss: 6.666954
iter: 598 | loss: 6.666607
iter: 599 | loss: 6.666259
iter: 600 | loss: 6.665912
iter: 601 | loss: 6.665564
iter: 602 | loss: 6.665217
iter: 603 | loss: 6.664869
iter: 604 | loss: 6.664522
iter: 605 | loss: 6.664175
iter: 606 | loss: 6.663827
iter: 607 | loss: 6.663480
iter: 608 | loss: 6.663132
iter: 609 | loss: 6.662785
iter: 610 | loss: 6.662437
iter: 611 | loss: 6.662090
iter: 612 | loss: 6.661742
iter: 613 | loss: 6.661395
iter: 614 | loss: 6.661047
iter: 615 | loss: 6.660700
iter: 616 | loss: 6.660352
iter: 617 | loss: 6.660005
iter: 618 | loss: 6.659657
iter: 619 | loss: 6.659310
iter: 620 | loss: 6.658962
iter: 621 | loss: 6.658615
iter: 622 | loss: 6.658267
iter: 623 | loss: 6.657920
iter: 624 | loss: 6.657572
iter: 625 | loss: 6.657225
iter: 626 | loss: 6.656877
iter: 627 | loss: 6.656530
iter: 628 | loss: 6.656182
iter: 629 | loss: 6.655835
iter: 630 | loss: 6.655487
iter: 631 | loss: 6.655140
iter: 632 | loss: 6.654792
iter: 633 | loss: 6.654445
iter: 634 | loss: 6.654097
iter: 635 | loss: 6.653750
iter: 636 | loss: 6.653402
iter: 637 | loss: 6.653055
iter: 638 | loss: 6.652707
iter: 639 | loss: 6.652360
iter: 640 | loss: 6.652012
iter: 641 | loss: 6.651665
iter: 642 | loss: 6.651317
iter: 643 | loss: 6.650970
iter: 644 | loss: 6.650622
iter: 645 | loss: 6.650275
iter: 646 | loss: 6.649927
iter: 647 | loss: 6.649580
iter: 648 | loss: 6.649233
iter: 649 | loss: 6.648885
iter: 650 | loss: 6.648538
iter: 651 | loss: 6.648190
iter: 652 | loss: 6.647843
iter: 653 | loss: 6.647495
iter: 654 | loss: 6.647148
iter: 655 | loss: 6.646800
iter: 656 | loss: 6.646453
iter: 657 | loss: 6.646105
iter: 658 | loss: 6.645758
iter: 659 | loss: 6.645410
iter: 660 | loss: 6.645063
iter: 661 | loss: 6.644715
iter: 662 | loss: 6.644368
iter: 663 | loss: 6.644020
iter: 664 | loss: 6.643673
iter: 665 | loss: 6.643325
iter: 666 | loss: 6.642978
iter: 667 | loss: 6.642630
iter: 668 | loss: 6.642283
iter: 669 | loss: 6.641935
iter: 670 | loss: 6.641588
iter: 671 | loss: 6.641240
iter: 672 | loss: 6.640893
iter: 673 | loss: 6.640545
iter: 674 | loss: 6.640198
iter: 675 | loss: 6.639850
iter: 676 | loss: 6.639503
iter: 677 | loss: 6.639155
iter: 678 | loss: 6.638808
iter: 679 | loss: 6.638460
iter: 680 | loss: 6.638113
iter: 681 | loss: 6.637765
iter: 682 | loss: 6.637418
iter: 683 | loss: 6.637070
iter: 684 | loss: 6.636723
iter: 685 | loss: 6.636375
iter: 686 | loss: 6.636028
iter: 687 | loss: 6.635680
iter: 688 | loss: 6.635333
iter: 689 | loss: 6.634985
iter: 690 | loss: 6.634638
iter: 691 | loss: 6.634291
iter: 692 | loss: 6.633943
iter: 693 | loss: 6.633596
iter: 694 | loss: 6.633248
iter: 695 | loss: 6.632901
iter: 696 | loss: 6.632553
iter: 697 | loss: 6.632206
iter: 698 | loss: 6.631858
iter: 699 | loss: 6.631511
iter: 700 | loss: 6.631163
iter: 701 | loss: 6.630816
iter: 702 | loss: 6.630468
iter: 703 | loss: 6.630121
iter: 704 | loss: 6.629773
iter: 705 | loss: 6.629426
iter: 706 | loss: 6.629078
iter: 707 | loss: 6.628731
iter: 708 | loss: 6.628383
iter: 709 | loss: 6.628036
iter: 710 | loss: 6.627688
iter: 711 | loss: 6.627341
iter: 712 | loss: 6.626993
iter: 713 | loss: 6.626646
iter: 714 | loss: 6.626298
iter: 715 | loss: 6.625951
iter: 716 | loss: 6.625603
iter: 717 | loss: 6.625256
iter: 718 | loss: 6.624908
iter: 719 | loss: 6.624561
iter: 720 | loss: 6.624213
iter: 721 | loss: 6.623866
iter: 722 | loss: 6.623518
iter: 723 | loss: 6.623171
iter: 724 | loss: 6.622823
iter: 725 | loss: 6.622476
iter: 726 | loss: 6.622128
iter: 727 | loss: 6.621781
iter: 728 | loss: 6.621433
iter: 729 | loss: 6.621086
iter: 730 | loss: 6.620738
iter: 731 | loss: 6.620391
iter: 732 | loss: 6.620043
iter: 733 | loss: 6.619696
iter: 734 | loss: 6.619348
iter: 735 | loss: 6.619001
iter: 736 | loss: 6.618654
iter: 737 | loss: 6.618306
iter: 738 | loss: 6.617959
iter: 739 | loss: 6.617611
iter: 740 | loss: 6.617264
iter: 741 | loss: 6.616916
iter: 742 | loss: 6.616569
iter: 743 | loss: 6.616221
iter: 744 | loss: 6.615874
iter: 745 | loss: 6.615526
iter: 746 | loss: 6.615179
iter: 747 | loss: 6.614831
iter: 748 | loss: 6.614484
iter: 749 | loss: 6.614136
iter: 750 | loss: 6.613789
iter: 751 | loss: 6.613441
iter: 752 | loss: 6.613094
iter: 753 | loss: 6.612746
iter: 754 | loss: 6.612399
iter: 755 | loss: 6.612051
iter: 756 | loss: 6.611704
iter: 757 | loss: 6.611356
iter: 758 | loss: 6.611009
iter: 759 | loss: 6.610661
iter: 760 | loss: 6.610314
iter: 761 | loss: 6.609966
iter: 762 | loss: 6.609619
iter: 763 | loss: 6.609271
iter: 764 | loss: 6.608924
iter: 765 | loss: 6.608576
iter: 766 | loss: 6.608229
iter: 767 | loss: 6.607881
iter: 768 | loss: 6.607534
iter: 769 | loss: 6.607186
iter: 770 | loss: 6.606839
iter: 771 | loss: 6.606491
iter: 772 | loss: 6.606144
iter: 773 | loss: 6.605796
iter: 774 | loss: 6.605449
iter: 775 | loss: 6.605101
iter: 776 | loss: 6.604754
iter: 777 | loss: 6.604406
iter: 778 | loss: 6.604059
iter: 779 | loss: 6.603712
iter: 780 | loss: 6.603364
iter: 781 | loss: 6.603017
iter: 782 | loss: 6.602669
iter: 783 | loss: 6.602322
iter: 784 | loss: 6.601974
iter: 785 | loss: 6.601627
iter: 786 | loss: 6.601279
iter: 787 | loss: 6.600932
iter: 788 | loss: 6.600584
iter: 789 | loss: 6.600237
iter: 790 | loss: 6.599889
iter: 791 | loss: 6.599542
iter: 792 | loss: 6.599194
iter: 793 | loss: 6.598847
iter: 794 | loss: 6.598499
iter: 795 | loss: 6.598152
iter: 796 | loss: 6.597804
iter: 797 | loss: 6.597457
iter: 798 | loss: 6.597109
iter: 799 | loss: 6.596762
iter: 800 | loss: 6.596414
iter: 801 | loss: 6.596067
iter: 802 | loss: 6.595719
iter: 803 | loss: 6.595372
iter: 804 | loss: 6.595024
iter: 805 | loss: 6.594677
iter: 806 | loss: 6.594329
iter: 807 | loss: 6.593982
iter: 808 | loss: 6.593634
iter: 809 | loss: 6.593287
iter: 810 | loss: 6.592939
iter: 811 | loss: 6.592592
iter: 812 | loss: 6.592244
iter: 813 | loss: 6.591897
iter: 814 | loss: 6.591549
iter: 815 | loss: 6.591202
iter: 816 | loss: 6.590854
iter: 817 | loss: 6.590507
iter: 818 | loss: 6.590159
iter: 819 | loss: 6.589812
iter: 820 | loss: 6.589464
iter: 821 | loss: 6.589117
iter: 822 | loss: 6.588769
iter: 823 | loss: 6.588422
iter: 824 | loss: 6.588075
iter: 825 | loss: 6.587727
iter: 826 | loss: 6.587380
iter: 827 | loss: 6.587032
iter: 828 | loss: 6.586685
iter: 829 | loss: 6.586337
iter: 830 | loss: 6.585990
iter: 831 | loss: 6.585642
iter: 832 | loss: 6.585295
iter: 833 | loss: 6.584947
iter: 834 | loss: 6.584600
iter: 835 | loss: 6.584252
iter: 836 | loss: 6.583905
iter: 837 | loss: 6.583557
iter: 838 | loss: 6.583210
iter: 839 | loss: 6.582862
iter: 840 | loss: 6.582515
iter: 841 | loss: 6.582167
iter: 842 | loss: 6.581820
iter: 843 | loss: 6.581472
iter: 844 | loss: 6.581125
iter: 845 | loss: 6.580777
iter: 846 | loss: 6.580430
iter: 847 | loss: 6.580082
iter: 848 | loss: 6.579735
iter: 849 | loss: 6.579387
iter: 850 | loss: 6.579040
iter: 851 | loss: 6.578692
iter: 852 | loss: 6.578345
iter: 853 | loss: 6.577997
iter: 854 | loss: 6.577650
iter: 855 | loss: 6.577302
iter: 856 | loss: 6.576955
iter: 857 | loss: 6.576607
iter: 858 | loss: 6.576260
iter: 859 | loss: 6.575912
iter: 860 | loss: 6.575565
iter: 861 | loss: 6.575217
iter: 862 | loss: 6.574870
iter: 863 | loss: 6.574522
iter: 864 | loss: 6.574175
iter: 865 | loss: 6.573827
iter: 866 | loss: 6.573480
iter: 867 | loss: 6.573133
iter: 868 | loss: 6.572785
iter: 869 | loss: 6.572438
iter: 870 | loss: 6.572090
iter: 871 | loss: 6.571743
iter: 872 | loss: 6.571395
iter: 873 | loss: 6.571048
iter: 874 | loss: 6.570700
iter: 875 | loss: 6.570353
iter: 876 | loss: 6.570005
iter: 877 | loss: 6.569658
iter: 878 | loss: 6.569310
iter: 879 | loss: 6.568963
iter: 880 | loss: 6.568615
iter: 881 | loss: 6.568268
iter: 882 | loss: 6.567920
iter: 883 | loss: 6.567573
iter: 884 | loss: 6.567225
iter: 885 | loss: 6.566878
iter: 886 | loss: 6.566530
iter: 887 | loss: 6.566183
iter: 888 | loss: 6.565835
iter: 889 | loss: 6.565488
iter: 890 | loss: 6.565140
iter: 891 | loss: 6.564793
iter: 892 | loss: 6.564445
iter: 893 | loss: 6.564098
iter: 894 | loss: 6.563750
iter: 895 | loss: 6.563403
iter: 896 | loss: 6.563055
iter: 897 | loss: 6.562708
iter: 898 | loss: 6.562360
iter: 899 | loss: 6.562013
iter: 900 | loss: 6.561665
iter: 901 | loss: 6.561318
iter: 902 | loss: 6.560970
iter: 903 | loss: 6.560623
iter: 904 | loss: 6.560275
iter: 905 | loss: 6.559928
iter: 906 | loss: 6.559580
iter: 907 | loss: 6.559233
iter: 908 | loss: 6.558885
iter: 909 | loss: 6.558538
iter: 910 | loss: 6.558190
iter: 911 | loss: 6.557843
iter: 912 | loss: 6.557496
iter: 913 | loss: 6.557148
iter: 914 | loss: 6.556801
iter: 915 | loss: 6.556453
iter: 916 | loss: 6.556106
iter: 917 | loss: 6.555758
iter: 918 | loss: 6.555411
iter: 919 | loss: 6.555063
iter: 920 | loss: 6.554716
iter: 921 | loss: 6.554368
iter: 922 | loss: 6.554021
iter: 923 | loss: 6.553673
iter: 924 | loss: 6.553326
iter: 925 | loss: 6.552978
iter: 926 | loss: 6.552631
iter: 927 | loss: 6.552283
iter: 928 | loss: 6.551936
iter: 929 | loss: 6.551588
iter: 930 | loss: 6.551241
iter: 931 | loss: 6.550893
iter: 932 | loss: 6.550546
iter: 933 | loss: 6.550198
iter: 934 | loss: 6.549851
iter: 935 | loss: 6.549503
iter: 936 | loss: 6.549156
iter: 937 | loss: 6.548808
iter: 938 | loss: 6.548461
iter: 939 | loss: 6.548113
iter: 940 | loss: 6.547766
iter: 941 | loss: 6.547418
iter: 942 | loss: 6.547071
iter: 943 | loss: 6.546723
iter: 944 | loss: 6.546376
iter: 945 | loss: 6.546028
iter: 946 | loss: 6.545681
iter: 947 | loss: 6.545333
iter: 948 | loss: 6.544986
iter: 949 | loss: 6.544638
iter: 950 | loss: 6.544291
iter: 951 | loss: 6.543943
iter: 952 | loss: 6.543596
iter: 953 | loss: 6.543248
iter: 954 | loss: 6.542901
iter: 955 | loss: 6.542554
iter: 956 | loss: 6.542206
iter: 957 | loss: 6.541859
iter: 958 | loss: 6.541511
iter: 959 | loss: 6.541164
iter: 960 | loss: 6.540816
iter: 961 | loss: 6.540469
iter: 962 | loss: 6.540121
iter: 963 | loss: 6.539774
iter: 964 | loss: 6.539426
iter: 965 | loss: 6.539079
iter: 966 | loss: 6.538731
iter: 967 | loss: 6.538384
iter: 968 | loss: 6.538036
iter: 969 | loss: 6.537689
iter: 970 | loss: 6.537341
iter: 971 | loss: 6.536994
iter: 972 | loss: 6.536646
iter: 973 | loss: 6.536299
iter: 974 | loss: 6.535951
iter: 975 | loss: 6.535604
iter: 976 | loss: 6.535256
iter: 977 | loss: 6.534909
iter: 978 | loss: 6.534561
iter: 979 | loss: 6.534214
iter: 980 | loss: 6.533866
iter: 981 | loss: 6.533519
iter: 982 | loss: 6.533171
iter: 983 | loss: 6.532824
iter: 984 | loss: 6.532476
iter: 985 | loss: 6.532129
iter: 986 | loss: 6.531781
iter: 987 | loss: 6.531434
iter: 988 | loss: 6.531086
iter: 989 | loss: 6.530739
iter: 990 | loss: 6.530391
iter: 991 | loss: 6.530044
iter: 992 | loss: 6.529696
iter: 993 | loss: 6.529349
iter: 994 | loss: 6.529001
iter: 995 | loss: 6.528654
iter: 996 | loss: 6.528306
iter: 997 | loss: 6.527959
iter: 998 | loss: 6.527612
iter: 999 | loss: 6.527264
iter: 1000 | loss: 6.526917
iter: 1001 | loss: 6.526569
iter: 1002 | loss: 6.526222
iter: 1003 | loss: 6.525874
iter: 1004 | loss: 6.525527
iter: 1005 | loss: 6.525179
iter: 1006 | loss: 6.524832
iter: 1007 | loss: 6.524484
iter: 1008 | loss: 6.524137
iter: 1009 | loss: 6.523789
iter: 1010 | loss: 6.523442
iter: 1011 | loss: 6.523094
iter: 1012 | loss: 6.522747
iter: 1013 | loss: 6.522399
iter: 1014 | loss: 6.522052
iter: 1015 | loss: 6.521704
iter: 1016 | loss: 6.521357
iter: 1017 | loss: 6.521009
iter: 1018 | loss: 6.520662
iter: 1019 | loss: 6.520314
iter: 1020 | loss: 6.519967
iter: 1021 | loss: 6.519619
iter: 1022 | loss: 6.519272
iter: 1023 | loss: 6.518924
iter: 1024 | loss: 6.518577
iter: 1025 | loss: 6.518229
iter: 1026 | loss: 6.517882
iter: 1027 | loss: 6.517534
iter: 1028 | loss: 6.517187
iter: 1029 | loss: 6.516839
iter: 1030 | loss: 6.516492
iter: 1031 | loss: 6.516144
iter: 1032 | loss: 6.515797
iter: 1033 | loss: 6.515449
iter: 1034 | loss: 6.515102
iter: 1035 | loss: 6.514754
iter: 1036 | loss: 6.514407
iter: 1037 | loss: 6.514059
iter: 1038 | loss: 6.513712
iter: 1039 | loss: 6.513364
iter: 1040 | loss: 6.513017
iter: 1041 | loss: 6.512669
iter: 1042 | loss: 6.512322
iter: 1043 | loss: 6.511975
iter: 1044 | loss: 6.511627
iter: 1045 | loss: 6.511280
iter: 1046 | loss: 6.510932
iter: 1047 | loss: 6.510585
iter: 1048 | loss: 6.510237
iter: 1049 | loss: 6.509890
iter: 1050 | loss: 6.509542
iter: 1051 | loss: 6.509195
iter: 1052 | loss: 6.508847
iter: 1053 | loss: 6.508500
iter: 1054 | loss: 6.508152
iter: 1055 | loss: 6.507805
iter: 1056 | loss: 6.507457
iter: 1057 | loss: 6.507110
iter: 1058 | loss: 6.506762
iter: 1059 | loss: 6.506415
iter: 1060 | loss: 6.506067
iter: 1061 | loss: 6.505720
iter: 1062 | loss: 6.505372
iter: 1063 | loss: 6.505025
iter: 1064 | loss: 6.504677
iter: 1065 | loss: 6.504330
iter: 1066 | loss: 6.503982
iter: 1067 | loss: 6.503635
iter: 1068 | loss: 6.503287
iter: 1069 | loss: 6.502940
iter: 1070 | loss: 6.502592
iter: 1071 | loss: 6.502245
iter: 1072 | loss: 6.501897
iter: 1073 | loss: 6.501550
iter: 1074 | loss: 6.501202
iter: 1075 | loss: 6.500855
iter: 1076 | loss: 6.500507
iter: 1077 | loss: 6.500160
iter: 1078 | loss: 6.499812
iter: 1079 | loss: 6.499465
iter: 1080 | loss: 6.499117
iter: 1081 | loss: 6.498770
iter: 1082 | loss: 6.498422
iter: 1083 | loss: 6.498075
iter: 1084 | loss: 6.497727
iter: 1085 | loss: 6.497380
iter: 1086 | loss: 6.497033
iter: 1087 | loss: 6.496685
iter: 1088 | loss: 6.496338
iter: 1089 | loss: 6.495990
iter: 1090 | loss: 6.495643
iter: 1091 | loss: 6.495295
iter: 1092 | loss: 6.494948
iter: 1093 | loss: 6.494600
iter: 1094 | loss: 6.494253
iter: 1095 | loss: 6.493905
iter: 1096 | loss: 6.493558
iter: 1097 | loss: 6.493210
iter: 1098 | loss: 6.492863
iter: 1099 | loss: 6.492515
iter: 1100 | loss: 6.492168
iter: 1101 | loss: 6.491820
iter: 1102 | loss: 6.491473
iter: 1103 | loss: 6.491125
iter: 1104 | loss: 6.490778
iter: 1105 | loss: 6.490430
iter: 1106 | loss: 6.490083
iter: 1107 | loss: 6.489735
iter: 1108 | loss: 6.489388
iter: 1109 | loss: 6.489040
iter: 1110 | loss: 6.488693
iter: 1111 | loss: 6.488345
iter: 1112 | loss: 6.487998
iter: 1113 | loss: 6.487650
iter: 1114 | loss: 6.487303
iter: 1115 | loss: 6.486955
iter: 1116 | loss: 6.486608
iter: 1117 | loss: 6.486260
iter: 1118 | loss: 6.485913
iter: 1119 | loss: 6.485565
iter: 1120 | loss: 6.485218
iter: 1121 | loss: 6.484870
iter: 1122 | loss: 6.484523
iter: 1123 | loss: 6.484175
iter: 1124 | loss: 6.483828
iter: 1125 | loss: 6.483480
iter: 1126 | loss: 6.483133
iter: 1127 | loss: 6.482785
iter: 1128 | loss: 6.482438
iter: 1129 | loss: 6.482090
iter: 1130 | loss: 6.481743
iter: 1131 | loss: 6.481396
iter: 1132 | loss: 6.481048
iter: 1133 | loss: 6.480701
iter: 1134 | loss: 6.480353
iter: 1135 | loss: 6.480006
iter: 1136 | loss: 6.479658
iter: 1137 | loss: 6.479311
iter: 1138 | loss: 6.478963
iter: 1139 | loss: 6.478616
iter: 1140 | loss: 6.478268
iter: 1141 | loss: 6.477921
iter: 1142 | loss: 6.477573
iter: 1143 | loss: 6.477226
iter: 1144 | loss: 6.476878
iter: 1145 | loss: 6.476531
iter: 1146 | loss: 6.476183
iter: 1147 | loss: 6.475836
iter: 1148 | loss: 6.475488
iter: 1149 | loss: 6.475141
iter: 1150 | loss: 6.474793
iter: 1151 | loss: 6.474446
iter: 1152 | loss: 6.474098
iter: 1153 | loss: 6.473751
iter: 1154 | loss: 6.473403
iter: 1155 | loss: 6.473056
iter: 1156 | loss: 6.472708
iter: 1157 | loss: 6.472361
iter: 1158 | loss: 6.472013
iter: 1159 | loss: 6.471666
iter: 1160 | loss: 6.471318
iter: 1161 | loss: 6.470971
iter: 1162 | loss: 6.470623
iter: 1163 | loss: 6.470276
iter: 1164 | loss: 6.469928
iter: 1165 | loss: 6.469581
iter: 1166 | loss: 6.469233
iter: 1167 | loss: 6.468886
iter: 1168 | loss: 6.468538
iter: 1169 | loss: 6.468191
iter: 1170 | loss: 6.467843
iter: 1171 | loss: 6.467496
iter: 1172 | loss: 6.467148
iter: 1173 | loss: 6.466801
iter: 1174 | loss: 6.466454
iter: 1175 | loss: 6.466106
iter: 1176 | loss: 6.465759
iter: 1177 | loss: 6.465411
iter: 1178 | loss: 6.465064
iter: 1179 | loss: 6.464716
iter: 1180 | loss: 6.464369
iter: 1181 | loss: 6.464021
iter: 1182 | loss: 6.463674
iter: 1183 | loss: 6.463326
iter: 1184 | loss: 6.462979
iter: 1185 | loss: 6.462631
iter: 1186 | loss: 6.462284
iter: 1187 | loss: 6.461936
iter: 1188 | loss: 6.461589
iter: 1189 | loss: 6.461241
iter: 1190 | loss: 6.460894
iter: 1191 | loss: 6.460546
iter: 1192 | loss: 6.460199
iter: 1193 | loss: 6.459851
iter: 1194 | loss: 6.459504
iter: 1195 | loss: 6.459156
iter: 1196 | loss: 6.458809
iter: 1197 | loss: 6.458461
iter: 1198 | loss: 6.458114
iter: 1199 | loss: 6.457766
iter: 1200 | loss: 6.457419
iter: 1201 | loss: 6.457071
iter: 1202 | loss: 6.456724
iter: 1203 | loss: 6.456376
iter: 1204 | loss: 6.456029
iter: 1205 | loss: 6.455681
iter: 1206 | loss: 6.455334
iter: 1207 | loss: 6.454986
iter: 1208 | loss: 6.454639
iter: 1209 | loss: 6.454291
iter: 1210 | loss: 6.453944
iter: 1211 | loss: 6.453596
iter: 1212 | loss: 6.453249
iter: 1213 | loss: 6.452901
iter: 1214 | loss: 6.452554
iter: 1215 | loss: 6.452206
iter: 1216 | loss: 6.451859
iter: 1217 | loss: 6.451512
iter: 1218 | loss: 6.451164
iter: 1219 | loss: 6.450817
iter: 1220 | loss: 6.450469
iter: 1221 | loss: 6.450122
iter: 1222 | loss: 6.449774
iter: 1223 | loss: 6.449427
iter: 1224 | loss: 6.449079
iter: 1225 | loss: 6.448732
iter: 1226 | loss: 6.448384
iter: 1227 | loss: 6.448037
iter: 1228 | loss: 6.447689
iter: 1229 | loss: 6.447342
iter: 1230 | loss: 6.446994
iter: 1231 | loss: 6.446647
iter: 1232 | loss: 6.446299
iter: 1233 | loss: 6.445952
iter: 1234 | loss: 6.445604
iter: 1235 | loss: 6.445257
iter: 1236 | loss: 6.444909
iter: 1237 | loss: 6.444562
iter: 1238 | loss: 6.444214
iter: 1239 | loss: 6.443867
iter: 1240 | loss: 6.443519
iter: 1241 | loss: 6.443172
iter: 1242 | loss: 6.442824
iter: 1243 | loss: 6.442477
iter: 1244 | loss: 6.442129
iter: 1245 | loss: 6.441782
iter: 1246 | loss: 6.441434
iter: 1247 | loss: 6.441087
iter: 1248 | loss: 6.440739
iter: 1249 | loss: 6.440392
iter: 1250 | loss: 6.440044
iter: 1251 | loss: 6.439697
iter: 1252 | loss: 6.439349
iter: 1253 | loss: 6.439002
iter: 1254 | loss: 6.438654
iter: 1255 | loss: 6.438307
iter: 1256 | loss: 6.437959
iter: 1257 | loss: 6.437612
iter: 1258 | loss: 6.437264
iter: 1259 | loss: 6.436917
iter: 1260 | loss: 6.436569
iter: 1261 | loss: 6.436222
iter: 1262 | loss: 6.435875
iter: 1263 | loss: 6.435527
iter: 1264 | loss: 6.435180
iter: 1265 | loss: 6.434832
iter: 1266 | loss: 6.434485
iter: 1267 | loss: 6.434137
iter: 1268 | loss: 6.433790
iter: 1269 | loss: 6.433442
iter: 1270 | loss: 6.433095
iter: 1271 | loss: 6.432747
iter: 1272 | loss: 6.432400
iter: 1273 | loss: 6.432052
iter: 1274 | loss: 6.431705
iter: 1275 | loss: 6.431357
iter: 1276 | loss: 6.431010
iter: 1277 | loss: 6.430662
iter: 1278 | loss: 6.430315
iter: 1279 | loss: 6.429967
iter: 1280 | loss: 6.429620
iter: 1281 | loss: 6.429272
iter: 1282 | loss: 6.428925
iter: 1283 | loss: 6.428577
iter: 1284 | loss: 6.428230
iter: 1285 | loss: 6.427882
iter: 1286 | loss: 6.427535
iter: 1287 | loss: 6.427187
iter: 1288 | loss: 6.426840
iter: 1289 | loss: 6.426492
iter: 1290 | loss: 6.426145
iter: 1291 | loss: 6.425797
iter: 1292 | loss: 6.425450
iter: 1293 | loss: 6.425102
iter: 1294 | loss: 6.424755
iter: 1295 | loss: 6.424407
iter: 1296 | loss: 6.424060
iter: 1297 | loss: 6.423712
iter: 1298 | loss: 6.423365
iter: 1299 | loss: 6.423017
iter: 1300 | loss: 6.422670
iter: 1301 | loss: 6.422322
iter: 1302 | loss: 6.421975
iter: 1303 | loss: 6.421627
iter: 1304 | loss: 6.421280
iter: 1305 | loss: 6.420933
iter: 1306 | loss: 6.420585
iter: 1307 | loss: 6.420238
iter: 1308 | loss: 6.419890
iter: 1309 | loss: 6.419543
iter: 1310 | loss: 6.419195
iter: 1311 | loss: 6.418848
iter: 1312 | loss: 6.418500
iter: 1313 | loss: 6.418153
iter: 1314 | loss: 6.417805
iter: 1315 | loss: 6.417458
iter: 1316 | loss: 6.417110
iter: 1317 | loss: 6.416763
iter: 1318 | loss: 6.416415
iter: 1319 | loss: 6.416068
iter: 1320 | loss: 6.415720
iter: 1321 | loss: 6.415373
iter: 1322 | loss: 6.415025
iter: 1323 | loss: 6.414678
iter: 1324 | loss: 6.414330
iter: 1325 | loss: 6.413983
iter: 1326 | loss: 6.413635
iter: 1327 | loss: 6.413288
iter: 1328 | loss: 6.412940
iter: 1329 | loss: 6.412593
iter: 1330 | loss: 6.412245
iter: 1331 | loss: 6.411898
iter: 1332 | loss: 6.411550
iter: 1333 | loss: 6.411203
iter: 1334 | loss: 6.410855
iter: 1335 | loss: 6.410508
iter: 1336 | loss: 6.410160
iter: 1337 | loss: 6.409813
iter: 1338 | loss: 6.409465
iter: 1339 | loss: 6.409118
iter: 1340 | loss: 6.408770
iter: 1341 | loss: 6.408423
iter: 1342 | loss: 6.408075
iter: 1343 | loss: 6.407728
iter: 1344 | loss: 6.407380
iter: 1345 | loss: 6.407033
iter: 1346 | loss: 6.406685
iter: 1347 | loss: 6.406338
iter: 1348 | loss: 6.405990
iter: 1349 | loss: 6.405643
iter: 1350 | loss: 6.405296
iter: 1351 | loss: 6.404948
iter: 1352 | loss: 6.404601
iter: 1353 | loss: 6.404253
iter: 1354 | loss: 6.403906
iter: 1355 | loss: 6.403558
iter: 1356 | loss: 6.403211
iter: 1357 | loss: 6.402863
iter: 1358 | loss: 6.402516
iter: 1359 | loss: 6.402168
iter: 1360 | loss: 6.401821
iter: 1361 | loss: 6.401473
iter: 1362 | loss: 6.401126
iter: 1363 | loss: 6.400778
iter: 1364 | loss: 6.400431
iter: 1365 | loss: 6.400083
iter: 1366 | loss: 6.399736
iter: 1367 | loss: 6.399388
iter: 1368 | loss: 6.399041
iter: 1369 | loss: 6.398693
iter: 1370 | loss: 6.398346
iter: 1371 | loss: 6.397998
iter: 1372 | loss: 6.397651
iter: 1373 | loss: 6.397303
iter: 1374 | loss: 6.396956
iter: 1375 | loss: 6.396608
iter: 1376 | loss: 6.396261
iter: 1377 | loss: 6.395913
iter: 1378 | loss: 6.395566
iter: 1379 | loss: 6.395218
iter: 1380 | loss: 6.394871
iter: 1381 | loss: 6.394523
iter: 1382 | loss: 6.394176
iter: 1383 | loss: 6.393828
iter: 1384 | loss: 6.393481
iter: 1385 | loss: 6.393133
iter: 1386 | loss: 6.392786
iter: 1387 | loss: 6.392438
iter: 1388 | loss: 6.392091
iter: 1389 | loss: 6.391743
iter: 1390 | loss: 6.391396
iter: 1391 | loss: 6.391048
iter: 1392 | loss: 6.390701
iter: 1393 | loss: 6.390354
iter: 1394 | loss: 6.390006
iter: 1395 | loss: 6.389659
iter: 1396 | loss: 6.389311
iter: 1397 | loss: 6.388964
iter: 1398 | loss: 6.388616
iter: 1399 | loss: 6.388269
iter: 1400 | loss: 6.387921
iter: 1401 | loss: 6.387574
iter: 1402 | loss: 6.387226
iter: 1403 | loss: 6.386879
iter: 1404 | loss: 6.386531
iter: 1405 | loss: 6.386184
iter: 1406 | loss: 6.385836
iter: 1407 | loss: 6.385489
iter: 1408 | loss: 6.385141
iter: 1409 | loss: 6.384794
iter: 1410 | loss: 6.384446
iter: 1411 | loss: 6.384099
iter: 1412 | loss: 6.383751
iter: 1413 | loss: 6.383404
iter: 1414 | loss: 6.383056
iter: 1415 | loss: 6.382709
iter: 1416 | loss: 6.382361
iter: 1417 | loss: 6.382014
iter: 1418 | loss: 6.381666
iter: 1419 | loss: 6.381319
iter: 1420 | loss: 6.380971
iter: 1421 | loss: 6.380624
iter: 1422 | loss: 6.380276
iter: 1423 | loss: 6.379929
iter: 1424 | loss: 6.379581
iter: 1425 | loss: 6.379234
iter: 1426 | loss: 6.378886
iter: 1427 | loss: 6.378539
iter: 1428 | loss: 6.378191
iter: 1429 | loss: 6.377844
iter: 1430 | loss: 6.377496
iter: 1431 | loss: 6.377149
iter: 1432 | loss: 6.376801
iter: 1433 | loss: 6.376454
iter: 1434 | loss: 6.376106
iter: 1435 | loss: 6.375759
iter: 1436 | loss: 6.375411
iter: 1437 | loss: 6.375064
iter: 1438 | loss: 6.374717
iter: 1439 | loss: 6.374369
iter: 1440 | loss: 6.374022
iter: 1441 | loss: 6.373674
iter: 1442 | loss: 6.373327
iter: 1443 | loss: 6.372979
iter: 1444 | loss: 6.372632
iter: 1445 | loss: 6.372284
iter: 1446 | loss: 6.371937
iter: 1447 | loss: 6.371589
iter: 1448 | loss: 6.371242
iter: 1449 | loss: 6.370894
iter: 1450 | loss: 6.370547
iter: 1451 | loss: 6.370199
iter: 1452 | loss: 6.369852
iter: 1453 | loss: 6.369504
iter: 1454 | loss: 6.369157
iter: 1455 | loss: 6.368809
iter: 1456 | loss: 6.368462
iter: 1457 | loss: 6.368114
iter: 1458 | loss: 6.367767
iter: 1459 | loss: 6.367419
iter: 1460 | loss: 6.367072
iter: 1461 | loss: 6.366724
iter: 1462 | loss: 6.366377
iter: 1463 | loss: 6.366029
iter: 1464 | loss: 6.365682
iter: 1465 | loss: 6.365334
iter: 1466 | loss: 6.364987
iter: 1467 | loss: 6.364639
iter: 1468 | loss: 6.364292
iter: 1469 | loss: 6.363944
iter: 1470 | loss: 6.363597
iter: 1471 | loss: 6.363249
iter: 1472 | loss: 6.362902
iter: 1473 | loss: 6.362554
iter: 1474 | loss: 6.362207
iter: 1475 | loss: 6.361859
iter: 1476 | loss: 6.361512
iter: 1477 | loss: 6.361164
iter: 1478 | loss: 6.360817
iter: 1479 | loss: 6.360469
iter: 1480 | loss: 6.360122
iter: 1481 | loss: 6.359775
iter: 1482 | loss: 6.359427
iter: 1483 | loss: 6.359080
iter: 1484 | loss: 6.358732
iter: 1485 | loss: 6.358385
iter: 1486 | loss: 6.358037
iter: 1487 | loss: 6.357690
iter: 1488 | loss: 6.357342
iter: 1489 | loss: 6.356995
iter: 1490 | loss: 6.356647
iter: 1491 | loss: 6.356300
iter: 1492 | loss: 6.355952
iter: 1493 | loss: 6.355605
iter: 1494 | loss: 6.355257
iter: 1495 | loss: 6.354910
iter: 1496 | loss: 6.354562
iter: 1497 | loss: 6.354215
iter: 1498 | loss: 6.353867
iter: 1499 | loss: 6.353520
iter: 1500 | loss: 6.353172
iter: 1501 | loss: 6.352825
iter: 1502 | loss: 6.352477
iter: 1503 | loss: 6.352130
iter: 1504 | loss: 6.351782
iter: 1505 | loss: 6.351435
iter: 1506 | loss: 6.351087
iter: 1507 | loss: 6.350740
iter: 1508 | loss: 6.350392
iter: 1509 | loss: 6.350045
iter: 1510 | loss: 6.349697
iter: 1511 | loss: 6.349350
iter: 1512 | loss: 6.349002
iter: 1513 | loss: 6.348655
iter: 1514 | loss: 6.348307
iter: 1515 | loss: 6.347960
iter: 1516 | loss: 6.347612
iter: 1517 | loss: 6.347265
iter: 1518 | loss: 6.346917
iter: 1519 | loss: 6.346570
iter: 1520 | loss: 6.346222
iter: 1521 | loss: 6.345875
iter: 1522 | loss: 6.345527
iter: 1523 | loss: 6.345180
iter: 1524 | loss: 6.344833
iter: 1525 | loss: 6.344485
iter: 1526 | loss: 6.344138
iter: 1527 | loss: 6.343790
iter: 1528 | loss: 6.343443
iter: 1529 | loss: 6.343095
iter: 1530 | loss: 6.342748
iter: 1531 | loss: 6.342400
iter: 1532 | loss: 6.342053
iter: 1533 | loss: 6.341705
iter: 1534 | loss: 6.341358
iter: 1535 | loss: 6.341010
iter: 1536 | loss: 6.340663
iter: 1537 | loss: 6.340315
iter: 1538 | loss: 6.339968
iter: 1539 | loss: 6.339620
iter: 1540 | loss: 6.339273
iter: 1541 | loss: 6.338925
iter: 1542 | loss: 6.338578
iter: 1543 | loss: 6.338230
iter: 1544 | loss: 6.337883
iter: 1545 | loss: 6.337535
iter: 1546 | loss: 6.337188
iter: 1547 | loss: 6.336840
iter: 1548 | loss: 6.336493
iter: 1549 | loss: 6.336145
iter: 1550 | loss: 6.335798
iter: 1551 | loss: 6.335450
iter: 1552 | loss: 6.335103
iter: 1553 | loss: 6.334755
iter: 1554 | loss: 6.334408
iter: 1555 | loss: 6.334060
iter: 1556 | loss: 6.333713
iter: 1557 | loss: 6.333365
iter: 1558 | loss: 6.333018
iter: 1559 | loss: 6.332670
iter: 1560 | loss: 6.332323
iter: 1561 | loss: 6.331975
iter: 1562 | loss: 6.331628
iter: 1563 | loss: 6.331280
iter: 1564 | loss: 6.330933
iter: 1565 | loss: 6.330585
iter: 1566 | loss: 6.330238
iter: 1567 | loss: 6.329890
iter: 1568 | loss: 6.329543
iter: 1569 | loss: 6.329196
iter: 1570 | loss: 6.328848
iter: 1571 | loss: 6.328501
iter: 1572 | loss: 6.328153
iter: 1573 | loss: 6.327806
iter: 1574 | loss: 6.327458
iter: 1575 | loss: 6.327111
iter: 1576 | loss: 6.326763
iter: 1577 | loss: 6.326416
iter: 1578 | loss: 6.326068
iter: 1579 | loss: 6.325721
iter: 1580 | loss: 6.325373
iter: 1581 | loss: 6.325026
iter: 1582 | loss: 6.324678
iter: 1583 | loss: 6.324331
iter: 1584 | loss: 6.323983
iter: 1585 | loss: 6.323636
iter: 1586 | loss: 6.323288
iter: 1587 | loss: 6.322941
iter: 1588 | loss: 6.322593
iter: 1589 | loss: 6.322246
iter: 1590 | loss: 6.321898
iter: 1591 | loss: 6.321551
iter: 1592 | loss: 6.321203
iter: 1593 | loss: 6.320856
iter: 1594 | loss: 6.320508
iter: 1595 | loss: 6.320161
iter: 1596 | loss: 6.319813
iter: 1597 | loss: 6.319466
iter: 1598 | loss: 6.319118
iter: 1599 | loss: 6.318771
iter: 1600 | loss: 6.318423
iter: 1601 | loss: 6.318076
iter: 1602 | loss: 6.317728
iter: 1603 | loss: 6.317381
iter: 1604 | loss: 6.317033
iter: 1605 | loss: 6.316686
iter: 1606 | loss: 6.316338
iter: 1607 | loss: 6.315991
iter: 1608 | loss: 6.315643
iter: 1609 | loss: 6.315296
iter: 1610 | loss: 6.314948
iter: 1611 | loss: 6.314601
iter: 1612 | loss: 6.314254
iter: 1613 | loss: 6.313906
iter: 1614 | loss: 6.313559
iter: 1615 | loss: 6.313211
iter: 1616 | loss: 6.312864
iter: 1617 | loss: 6.312516
iter: 1618 | loss: 6.312169
iter: 1619 | loss: 6.311821
iter: 1620 | loss: 6.311474
iter: 1621 | loss: 6.311126
iter: 1622 | loss: 6.310779
iter: 1623 | loss: 6.310431
iter: 1624 | loss: 6.310084
iter: 1625 | loss: 6.309736
iter: 1626 | loss: 6.309389
iter: 1627 | loss: 6.309041
iter: 1628 | loss: 6.308694
iter: 1629 | loss: 6.308346
iter: 1630 | loss: 6.307999
iter: 1631 | loss: 6.307651
iter: 1632 | loss: 6.307304
iter: 1633 | loss: 6.306956
iter: 1634 | loss: 6.306609
iter: 1635 | loss: 6.306261
iter: 1636 | loss: 6.305914
iter: 1637 | loss: 6.305566
iter: 1638 | loss: 6.305219
iter: 1639 | loss: 6.304871
iter: 1640 | loss: 6.304524
iter: 1641 | loss: 6.304176
iter: 1642 | loss: 6.303829
iter: 1643 | loss: 6.303481
iter: 1644 | loss: 6.303134
iter: 1645 | loss: 6.302786
iter: 1646 | loss: 6.302439
iter: 1647 | loss: 6.302091
iter: 1648 | loss: 6.301744
iter: 1649 | loss: 6.301396
iter: 1650 | loss: 6.301049
iter: 1651 | loss: 6.300701
iter: 1652 | loss: 6.300354
iter: 1653 | loss: 6.300006
iter: 1654 | loss: 6.299659
iter: 1655 | loss: 6.299311
iter: 1656 | loss: 6.298964
iter: 1657 | loss: 6.298617
iter: 1658 | loss: 6.298269
iter: 1659 | loss: 6.297922
iter: 1660 | loss: 6.297574
iter: 1661 | loss: 6.297227
iter: 1662 | loss: 6.296879
iter: 1663 | loss: 6.296532
iter: 1664 | loss: 6.296184
iter: 1665 | loss: 6.295837
iter: 1666 | loss: 6.295489
iter: 1667 | loss: 6.295142
iter: 1668 | loss: 6.294794
iter: 1669 | loss: 6.294447
iter: 1670 | loss: 6.294099
iter: 1671 | loss: 6.293752
iter: 1672 | loss: 6.293404
iter: 1673 | loss: 6.293057
iter: 1674 | loss: 6.292709
iter: 1675 | loss: 6.292362
iter: 1676 | loss: 6.292014
iter: 1677 | loss: 6.291667
iter: 1678 | loss: 6.291319
iter: 1679 | loss: 6.290972
iter: 1680 | loss: 6.290624
iter: 1681 | loss: 6.290277
iter: 1682 | loss: 6.289929
iter: 1683 | loss: 6.289582
iter: 1684 | loss: 6.289234
iter: 1685 | loss: 6.288887
iter: 1686 | loss: 6.288539
iter: 1687 | loss: 6.288192
iter: 1688 | loss: 6.287844
iter: 1689 | loss: 6.287497
iter: 1690 | loss: 6.287149
iter: 1691 | loss: 6.286802
iter: 1692 | loss: 6.286454
iter: 1693 | loss: 6.286107
iter: 1694 | loss: 6.285759
iter: 1695 | loss: 6.285412
iter: 1696 | loss: 6.285064
iter: 1697 | loss: 6.284717
iter: 1698 | loss: 6.284369
iter: 1699 | loss: 6.284022
iter: 1700 | loss: 6.283675
iter: 1701 | loss: 6.283327
iter: 1702 | loss: 6.282980
iter: 1703 | loss: 6.282632
iter: 1704 | loss: 6.282285
iter: 1705 | loss: 6.281937
iter: 1706 | loss: 6.281590
iter: 1707 | loss: 6.281242
iter: 1708 | loss: 6.280895
iter: 1709 | loss: 6.280547
iter: 1710 | loss: 6.280200
iter: 1711 | loss: 6.279852
iter: 1712 | loss: 6.279505
iter: 1713 | loss: 6.279157
iter: 1714 | loss: 6.278810
iter: 1715 | loss: 6.278462
iter: 1716 | loss: 6.278115
iter: 1717 | loss: 6.277767
iter: 1718 | loss: 6.277420
iter: 1719 | loss: 6.277072
iter: 1720 | loss: 6.276725
iter: 1721 | loss: 6.276377
iter: 1722 | loss: 6.276030
iter: 1723 | loss: 6.275682
iter: 1724 | loss: 6.275335
iter: 1725 | loss: 6.274987
iter: 1726 | loss: 6.274640
iter: 1727 | loss: 6.274292
iter: 1728 | loss: 6.273945
iter: 1729 | loss: 6.273597
iter: 1730 | loss: 6.273250
iter: 1731 | loss: 6.272902
iter: 1732 | loss: 6.272555
iter: 1733 | loss: 6.272207
iter: 1734 | loss: 6.271860
iter: 1735 | loss: 6.271512
iter: 1736 | loss: 6.271165
iter: 1737 | loss: 6.270817
iter: 1738 | loss: 6.270470
iter: 1739 | loss: 6.270122
iter: 1740 | loss: 6.269775
iter: 1741 | loss: 6.269427
iter: 1742 | loss: 6.269080
iter: 1743 | loss: 6.268733
iter: 1744 | loss: 6.268385
iter: 1745 | loss: 6.268038
iter: 1746 | loss: 6.267690
iter: 1747 | loss: 6.267343
iter: 1748 | loss: 6.266995
iter: 1749 | loss: 6.266648
iter: 1750 | loss: 6.266300
iter: 1751 | loss: 6.265953
iter: 1752 | loss: 6.265605
iter: 1753 | loss: 6.265258
iter: 1754 | loss: 6.264910
iter: 1755 | loss: 6.264563
iter: 1756 | loss: 6.264215
iter: 1757 | loss: 6.263868
iter: 1758 | loss: 6.263520
iter: 1759 | loss: 6.263173
iter: 1760 | loss: 6.262825
iter: 1761 | loss: 6.262478
iter: 1762 | loss: 6.262130
iter: 1763 | loss: 6.261783
iter: 1764 | loss: 6.261435
iter: 1765 | loss: 6.261088
iter: 1766 | loss: 6.260740
iter: 1767 | loss: 6.260393
iter: 1768 | loss: 6.260045
iter: 1769 | loss: 6.259698
iter: 1770 | loss: 6.259350
iter: 1771 | loss: 6.259003
iter: 1772 | loss: 6.258655
iter: 1773 | loss: 6.258308
iter: 1774 | loss: 6.257960
iter: 1775 | loss: 6.257613
iter: 1776 | loss: 6.257265
iter: 1777 | loss: 6.256918
iter: 1778 | loss: 6.256570
iter: 1779 | loss: 6.256223
iter: 1780 | loss: 6.255875
iter: 1781 | loss: 6.255528
iter: 1782 | loss: 6.255180
iter: 1783 | loss: 6.254833
iter: 1784 | loss: 6.254485
iter: 1785 | loss: 6.254138
iter: 1786 | loss: 6.253790
iter: 1787 | loss: 6.253443
iter: 1788 | loss: 6.253096
iter: 1789 | loss: 6.252748
iter: 1790 | loss: 6.252401
iter: 1791 | loss: 6.252053
iter: 1792 | loss: 6.251706
iter: 1793 | loss: 6.251358
iter: 1794 | loss: 6.251011
iter: 1795 | loss: 6.250663
iter: 1796 | loss: 6.250316
iter: 1797 | loss: 6.249968
iter: 1798 | loss: 6.249621
iter: 1799 | loss: 6.249273
iter: 1800 | loss: 6.248926
iter: 1801 | loss: 6.248578
iter: 1802 | loss: 6.248231
iter: 1803 | loss: 6.247883
iter: 1804 | loss: 6.247536
iter: 1805 | loss: 6.247188
iter: 1806 | loss: 6.246841
iter: 1807 | loss: 6.246493
iter: 1808 | loss: 6.246146
iter: 1809 | loss: 6.245798
iter: 1810 | loss: 6.245451
iter: 1811 | loss: 6.245103
iter: 1812 | loss: 6.244756
iter: 1813 | loss: 6.244408
iter: 1814 | loss: 6.244061
iter: 1815 | loss: 6.243713
iter: 1816 | loss: 6.243366
iter: 1817 | loss: 6.243018
iter: 1818 | loss: 6.242671
iter: 1819 | loss: 6.242323
iter: 1820 | loss: 6.241976
iter: 1821 | loss: 6.241628
iter: 1822 | loss: 6.241281
iter: 1823 | loss: 6.240933
iter: 1824 | loss: 6.240586
iter: 1825 | loss: 6.240238
iter: 1826 | loss: 6.239891
iter: 1827 | loss: 6.239543
iter: 1828 | loss: 6.239196
iter: 1829 | loss: 6.238848
iter: 1830 | loss: 6.238501
iter: 1831 | loss: 6.238154
iter: 1832 | loss: 6.237806
iter: 1833 | loss: 6.237459
iter: 1834 | loss: 6.237111
iter: 1835 | loss: 6.236764
iter: 1836 | loss: 6.236416
iter: 1837 | loss: 6.236069
iter: 1838 | loss: 6.235721
iter: 1839 | loss: 6.235374
iter: 1840 | loss: 6.235026
iter: 1841 | loss: 6.234679
iter: 1842 | loss: 6.234331
iter: 1843 | loss: 6.233984
iter: 1844 | loss: 6.233636
iter: 1845 | loss: 6.233289
iter: 1846 | loss: 6.232941
iter: 1847 | loss: 6.232594
iter: 1848 | loss: 6.232246
iter: 1849 | loss: 6.231899
iter: 1850 | loss: 6.231551
iter: 1851 | loss: 6.231204
iter: 1852 | loss: 6.230856
iter: 1853 | loss: 6.230509
iter: 1854 | loss: 6.230161
iter: 1855 | loss: 6.229814
iter: 1856 | loss: 6.229466
iter: 1857 | loss: 6.229119
iter: 1858 | loss: 6.228771
iter: 1859 | loss: 6.228424
iter: 1860 | loss: 6.228076
iter: 1861 | loss: 6.227729
iter: 1862 | loss: 6.227381
iter: 1863 | loss: 6.227034
iter: 1864 | loss: 6.226686
iter: 1865 | loss: 6.226339
iter: 1866 | loss: 6.225991
iter: 1867 | loss: 6.225644
iter: 1868 | loss: 6.225296
iter: 1869 | loss: 6.224949
iter: 1870 | loss: 6.224601
iter: 1871 | loss: 6.224254
iter: 1872 | loss: 6.223906
iter: 1873 | loss: 6.223559
iter: 1874 | loss: 6.223211
iter: 1875 | loss: 6.222864
iter: 1876 | loss: 6.222517
iter: 1877 | loss: 6.222169
iter: 1878 | loss: 6.221822
iter: 1879 | loss: 6.221474
iter: 1880 | loss: 6.221127
iter: 1881 | loss: 6.220779
iter: 1882 | loss: 6.220432
iter: 1883 | loss: 6.220084
iter: 1884 | loss: 6.219737
iter: 1885 | loss: 6.219389
iter: 1886 | loss: 6.219042
iter: 1887 | loss: 6.218694
iter: 1888 | loss: 6.218347
iter: 1889 | loss: 6.217999
iter: 1890 | loss: 6.217652
iter: 1891 | loss: 6.217304
iter: 1892 | loss: 6.216957
iter: 1893 | loss: 6.216609
iter: 1894 | loss: 6.216262
iter: 1895 | loss: 6.215914
iter: 1896 | loss: 6.215567
iter: 1897 | loss: 6.215219
iter: 1898 | loss: 6.214872
iter: 1899 | loss: 6.214524
iter: 1900 | loss: 6.214177
iter: 1901 | loss: 6.213829
iter: 1902 | loss: 6.213482
iter: 1903 | loss: 6.213134
iter: 1904 | loss: 6.212787
iter: 1905 | loss: 6.212439
iter: 1906 | loss: 6.212092
iter: 1907 | loss: 6.211744
iter: 1908 | loss: 6.211397
iter: 1909 | loss: 6.211049
iter: 1910 | loss: 6.210702
iter: 1911 | loss: 6.210354
iter: 1912 | loss: 6.210007
iter: 1913 | loss: 6.209659
iter: 1914 | loss: 6.209312
iter: 1915 | loss: 6.208964
iter: 1916 | loss: 6.208617
iter: 1917 | loss: 6.208269
iter: 1918 | loss: 6.207922
iter: 1919 | loss: 6.207575
iter: 1920 | loss: 6.207227
iter: 1921 | loss: 6.206880
iter: 1922 | loss: 6.206532
iter: 1923 | loss: 6.206185
iter: 1924 | loss: 6.205837
iter: 1925 | loss: 6.205490
iter: 1926 | loss: 6.205142
iter: 1927 | loss: 6.204795
iter: 1928 | loss: 6.204447
iter: 1929 | loss: 6.204100
iter: 1930 | loss: 6.203752
iter: 1931 | loss: 6.203405
iter: 1932 | loss: 6.203057
iter: 1933 | loss: 6.202710
iter: 1934 | loss: 6.202362
iter: 1935 | loss: 6.202015
iter: 1936 | loss: 6.201667
iter: 1937 | loss: 6.201320
iter: 1938 | loss: 6.200972
iter: 1939 | loss: 6.200625
iter: 1940 | loss: 6.200277
iter: 1941 | loss: 6.199930
iter: 1942 | loss: 6.199582
iter: 1943 | loss: 6.199235
iter: 1944 | loss: 6.198887
iter: 1945 | loss: 6.198540
iter: 1946 | loss: 6.198192
iter: 1947 | loss: 6.197845
iter: 1948 | loss: 6.197497
iter: 1949 | loss: 6.197150
iter: 1950 | loss: 6.196802
iter: 1951 | loss: 6.196455
iter: 1952 | loss: 6.196107
iter: 1953 | loss: 6.195760
iter: 1954 | loss: 6.195412
iter: 1955 | loss: 6.195065
iter: 1956 | loss: 6.194717
iter: 1957 | loss: 6.194370
iter: 1958 | loss: 6.194022
iter: 1959 | loss: 6.193675
iter: 1960 | loss: 6.193327
iter: 1961 | loss: 6.192980
iter: 1962 | loss: 6.192632
iter: 1963 | loss: 6.192285
iter: 1964 | loss: 6.191938
iter: 1965 | loss: 6.191590
iter: 1966 | loss: 6.191243
iter: 1967 | loss: 6.190895
iter: 1968 | loss: 6.190548
iter: 1969 | loss: 6.190200
iter: 1970 | loss: 6.189853
iter: 1971 | loss: 6.189505
iter: 1972 | loss: 6.189158
iter: 1973 | loss: 6.188810
iter: 1974 | loss: 6.188463
iter: 1975 | loss: 6.188115
iter: 1976 | loss: 6.187768
iter: 1977 | loss: 6.187420
iter: 1978 | loss: 6.187073
iter: 1979 | loss: 6.186725
iter: 1980 | loss: 6.186378
iter: 1981 | loss: 6.186030
iter: 1982 | loss: 6.185683
iter: 1983 | loss: 6.185335
iter: 1984 | loss: 6.184988
iter: 1985 | loss: 6.184640
iter: 1986 | loss: 6.184293
iter: 1987 | loss: 6.183945
iter: 1988 | loss: 6.183598
iter: 1989 | loss: 6.183250
iter: 1990 | loss: 6.182903
iter: 1991 | loss: 6.182555
iter: 1992 | loss: 6.182208
iter: 1993 | loss: 6.181860
iter: 1994 | loss: 6.181513
iter: 1995 | loss: 6.181165
iter: 1996 | loss: 6.180818
iter: 1997 | loss: 6.180470
iter: 1998 | loss: 6.180123
iter: 1999 | loss: 6.179775
iter: 2000 | loss: 6.179428
iter: 2001 | loss: 6.179080
iter: 2002 | loss: 6.178733
iter: 2003 | loss: 6.178385
iter: 2004 | loss: 6.178038
iter: 2005 | loss: 6.177690
iter: 2006 | loss: 6.177343
iter: 2007 | loss: 6.176996
iter: 2008 | loss: 6.176648
iter: 2009 | loss: 6.176301
iter: 2010 | loss: 6.175953
iter: 2011 | loss: 6.175606
iter: 2012 | loss: 6.175258
iter: 2013 | loss: 6.174911
iter: 2014 | loss: 6.174563
iter: 2015 | loss: 6.174216
iter: 2016 | loss: 6.173868
iter: 2017 | loss: 6.173521
iter: 2018 | loss: 6.173173
iter: 2019 | loss: 6.172826
iter: 2020 | loss: 6.172478
iter: 2021 | loss: 6.172131
iter: 2022 | loss: 6.171783
iter: 2023 | loss: 6.171436
iter: 2024 | loss: 6.171088
iter: 2025 | loss: 6.170741
iter: 2026 | loss: 6.170393
iter: 2027 | loss: 6.170046
iter: 2028 | loss: 6.169698
iter: 2029 | loss: 6.169351
iter: 2030 | loss: 6.169003
iter: 2031 | loss: 6.168656
iter: 2032 | loss: 6.168308
iter: 2033 | loss: 6.167961
iter: 2034 | loss: 6.167613
iter: 2035 | loss: 6.167266
iter: 2036 | loss: 6.166918
iter: 2037 | loss: 6.166571
iter: 2038 | loss: 6.166223
iter: 2039 | loss: 6.165876
iter: 2040 | loss: 6.165528
iter: 2041 | loss: 6.165181
iter: 2042 | loss: 6.164833
iter: 2043 | loss: 6.164486
iter: 2044 | loss: 6.164138
iter: 2045 | loss: 6.163791
iter: 2046 | loss: 6.163443
iter: 2047 | loss: 6.163096
iter: 2048 | loss: 6.162748
iter: 2049 | loss: 6.162401
iter: 2050 | loss: 6.162054
iter: 2051 | loss: 6.161706
iter: 2052 | loss: 6.161359
iter: 2053 | loss: 6.161011
iter: 2054 | loss: 6.160664
iter: 2055 | loss: 6.160316
iter: 2056 | loss: 6.159969
iter: 2057 | loss: 6.159621
iter: 2058 | loss: 6.159274
iter: 2059 | loss: 6.158926
iter: 2060 | loss: 6.158579
iter: 2061 | loss: 6.158231
iter: 2062 | loss: 6.157884
iter: 2063 | loss: 6.157536
iter: 2064 | loss: 6.157189
iter: 2065 | loss: 6.156841
iter: 2066 | loss: 6.156494
iter: 2067 | loss: 6.156146
iter: 2068 | loss: 6.155799
iter: 2069 | loss: 6.155451
iter: 2070 | loss: 6.155104
iter: 2071 | loss: 6.154756
iter: 2072 | loss: 6.154409
iter: 2073 | loss: 6.154061
iter: 2074 | loss: 6.153714
iter: 2075 | loss: 6.153366
iter: 2076 | loss: 6.153019
iter: 2077 | loss: 6.152671
iter: 2078 | loss: 6.152324
iter: 2079 | loss: 6.151976
iter: 2080 | loss: 6.151629
iter: 2081 | loss: 6.151281
iter: 2082 | loss: 6.150934
iter: 2083 | loss: 6.150586
iter: 2084 | loss: 6.150239
iter: 2085 | loss: 6.149891
iter: 2086 | loss: 6.149544
iter: 2087 | loss: 6.149196
iter: 2088 | loss: 6.148849
iter: 2089 | loss: 6.148501
iter: 2090 | loss: 6.148154
iter: 2091 | loss: 6.147806
iter: 2092 | loss: 6.147459
iter: 2093 | loss: 6.147111
iter: 2094 | loss: 6.146764
iter: 2095 | loss: 6.146417
iter: 2096 | loss: 6.146069
iter: 2097 | loss: 6.145722
iter: 2098 | loss: 6.145374
iter: 2099 | loss: 6.145027
iter: 2100 | loss: 6.144679
iter: 2101 | loss: 6.144332
iter: 2102 | loss: 6.143984
iter: 2103 | loss: 6.143637
iter: 2104 | loss: 6.143289
iter: 2105 | loss: 6.142942
iter: 2106 | loss: 6.142594
iter: 2107 | loss: 6.142247
iter: 2108 | loss: 6.141899
iter: 2109 | loss: 6.141552
iter: 2110 | loss: 6.141204
iter: 2111 | loss: 6.140857
iter: 2112 | loss: 6.140509
iter: 2113 | loss: 6.140162
iter: 2114 | loss: 6.139814
iter: 2115 | loss: 6.139467
iter: 2116 | loss: 6.139119
iter: 2117 | loss: 6.138772
iter: 2118 | loss: 6.138424
iter: 2119 | loss: 6.138077
iter: 2120 | loss: 6.137729
iter: 2121 | loss: 6.137382
iter: 2122 | loss: 6.137034
iter: 2123 | loss: 6.136687
iter: 2124 | loss: 6.136339
iter: 2125 | loss: 6.135992
iter: 2126 | loss: 6.135644
iter: 2127 | loss: 6.135297
iter: 2128 | loss: 6.134949
iter: 2129 | loss: 6.134602
iter: 2130 | loss: 6.134254
iter: 2131 | loss: 6.133907
iter: 2132 | loss: 6.133559
iter: 2133 | loss: 6.133212
iter: 2134 | loss: 6.132864
iter: 2135 | loss: 6.132517
iter: 2136 | loss: 6.132169
iter: 2137 | loss: 6.131822
iter: 2138 | loss: 6.131475
iter: 2139 | loss: 6.131127
iter: 2140 | loss: 6.130780
iter: 2141 | loss: 6.130432
iter: 2142 | loss: 6.130085
iter: 2143 | loss: 6.129737
iter: 2144 | loss: 6.129390
iter: 2145 | loss: 6.129042
iter: 2146 | loss: 6.128695
iter: 2147 | loss: 6.128347
iter: 2148 | loss: 6.128000
iter: 2149 | loss: 6.127652
iter: 2150 | loss: 6.127305
iter: 2151 | loss: 6.126957
iter: 2152 | loss: 6.126610
iter: 2153 | loss: 6.126262
iter: 2154 | loss: 6.125915
iter: 2155 | loss: 6.125567
iter: 2156 | loss: 6.125220
iter: 2157 | loss: 6.124872
iter: 2158 | loss: 6.124525
iter: 2159 | loss: 6.124177
iter: 2160 | loss: 6.123830
iter: 2161 | loss: 6.123482
iter: 2162 | loss: 6.123135
iter: 2163 | loss: 6.122787
iter: 2164 | loss: 6.122440
iter: 2165 | loss: 6.122092
iter: 2166 | loss: 6.121745
iter: 2167 | loss: 6.121397
iter: 2168 | loss: 6.121050
iter: 2169 | loss: 6.120702
iter: 2170 | loss: 6.120355
iter: 2171 | loss: 6.120007
iter: 2172 | loss: 6.119660
iter: 2173 | loss: 6.119312
iter: 2174 | loss: 6.118965
iter: 2175 | loss: 6.118617
iter: 2176 | loss: 6.118270
iter: 2177 | loss: 6.117922
iter: 2178 | loss: 6.117575
iter: 2179 | loss: 6.117227
iter: 2180 | loss: 6.116880
iter: 2181 | loss: 6.116532
iter: 2182 | loss: 6.116185
iter: 2183 | loss: 6.115838
iter: 2184 | loss: 6.115490
iter: 2185 | loss: 6.115143
iter: 2186 | loss: 6.114795
iter: 2187 | loss: 6.114448
iter: 2188 | loss: 6.114100
iter: 2189 | loss: 6.113753
iter: 2190 | loss: 6.113405
iter: 2191 | loss: 6.113058
iter: 2192 | loss: 6.112710
iter: 2193 | loss: 6.112363
iter: 2194 | loss: 6.112015
iter: 2195 | loss: 6.111668
iter: 2196 | loss: 6.111320
iter: 2197 | loss: 6.110973
iter: 2198 | loss: 6.110625
iter: 2199 | loss: 6.110278
iter: 2200 | loss: 6.109930
iter: 2201 | loss: 6.109583
iter: 2202 | loss: 6.109235
iter: 2203 | loss: 6.108888
iter: 2204 | loss: 6.108540
iter: 2205 | loss: 6.108193
iter: 2206 | loss: 6.107845
iter: 2207 | loss: 6.107498
iter: 2208 | loss: 6.107150
iter: 2209 | loss: 6.106803
iter: 2210 | loss: 6.106455
iter: 2211 | loss: 6.106108
iter: 2212 | loss: 6.105760
iter: 2213 | loss: 6.105413
iter: 2214 | loss: 6.105065
iter: 2215 | loss: 6.104718
iter: 2216 | loss: 6.104370
iter: 2217 | loss: 6.104023
iter: 2218 | loss: 6.103675
iter: 2219 | loss: 6.103328
iter: 2220 | loss: 6.102980
iter: 2221 | loss: 6.102633
iter: 2222 | loss: 6.102285
iter: 2223 | loss: 6.101938
iter: 2224 | loss: 6.101590
iter: 2225 | loss: 6.101243
iter: 2226 | loss: 6.100896
iter: 2227 | loss: 6.100548
iter: 2228 | loss: 6.100201
iter: 2229 | loss: 6.099853
iter: 2230 | loss: 6.099506
iter: 2231 | loss: 6.099158
iter: 2232 | loss: 6.098811
iter: 2233 | loss: 6.098463
iter: 2234 | loss: 6.098116
iter: 2235 | loss: 6.097768
iter: 2236 | loss: 6.097421
iter: 2237 | loss: 6.097073
iter: 2238 | loss: 6.096726
iter: 2239 | loss: 6.096378
iter: 2240 | loss: 6.096031
iter: 2241 | loss: 6.095683
iter: 2242 | loss: 6.095336
iter: 2243 | loss: 6.094988
iter: 2244 | loss: 6.094641
iter: 2245 | loss: 6.094293
iter: 2246 | loss: 6.093946
iter: 2247 | loss: 6.093598
iter: 2248 | loss: 6.093251
iter: 2249 | loss: 6.092903
iter: 2250 | loss: 6.092556
iter: 2251 | loss: 6.092208
iter: 2252 | loss: 6.091861
iter: 2253 | loss: 6.091513
iter: 2254 | loss: 6.091166
iter: 2255 | loss: 6.090818
iter: 2256 | loss: 6.090471
iter: 2257 | loss: 6.090123
iter: 2258 | loss: 6.089776
iter: 2259 | loss: 6.089428
iter: 2260 | loss: 6.089081
iter: 2261 | loss: 6.088733
iter: 2262 | loss: 6.088386
iter: 2263 | loss: 6.088038
iter: 2264 | loss: 6.087691
iter: 2265 | loss: 6.087343
iter: 2266 | loss: 6.086996
iter: 2267 | loss: 6.086648
iter: 2268 | loss: 6.086301
iter: 2269 | loss: 6.085954
iter: 2270 | loss: 6.085606
iter: 2271 | loss: 6.085259
iter: 2272 | loss: 6.084911
iter: 2273 | loss: 6.084564
iter: 2274 | loss: 6.084216
iter: 2275 | loss: 6.083869
iter: 2276 | loss: 6.083521
iter: 2277 | loss: 6.083174
iter: 2278 | loss: 6.082826
iter: 2279 | loss: 6.082479
iter: 2280 | loss: 6.082131
iter: 2281 | loss: 6.081784
iter: 2282 | loss: 6.081436
iter: 2283 | loss: 6.081089
iter: 2284 | loss: 6.080741
iter: 2285 | loss: 6.080394
iter: 2286 | loss: 6.080046
iter: 2287 | loss: 6.079699
iter: 2288 | loss: 6.079351
iter: 2289 | loss: 6.079004
iter: 2290 | loss: 6.078656
iter: 2291 | loss: 6.078309
iter: 2292 | loss: 6.077961
iter: 2293 | loss: 6.077614
iter: 2294 | loss: 6.077266
iter: 2295 | loss: 6.076919
iter: 2296 | loss: 6.076571
iter: 2297 | loss: 6.076224
iter: 2298 | loss: 6.075876
iter: 2299 | loss: 6.075529
iter: 2300 | loss: 6.075181
iter: 2301 | loss: 6.074834
iter: 2302 | loss: 6.074486
iter: 2303 | loss: 6.074139
iter: 2304 | loss: 6.073791
iter: 2305 | loss: 6.073444
iter: 2306 | loss: 6.073096
iter: 2307 | loss: 6.072749
iter: 2308 | loss: 6.072401
iter: 2309 | loss: 6.072054
iter: 2310 | loss: 6.071706
iter: 2311 | loss: 6.071359
iter: 2312 | loss: 6.071011
iter: 2313 | loss: 6.070664
iter: 2314 | loss: 6.070317
iter: 2315 | loss: 6.069969
iter: 2316 | loss: 6.069622
iter: 2317 | loss: 6.069274
iter: 2318 | loss: 6.068927
iter: 2319 | loss: 6.068579
iter: 2320 | loss: 6.068232
iter: 2321 | loss: 6.067884
iter: 2322 | loss: 6.067537
iter: 2323 | loss: 6.067189
iter: 2324 | loss: 6.066842
iter: 2325 | loss: 6.066494
iter: 2326 | loss: 6.066147
iter: 2327 | loss: 6.065799
iter: 2328 | loss: 6.065452
iter: 2329 | loss: 6.065104
iter: 2330 | loss: 6.064757
iter: 2331 | loss: 6.064409
iter: 2332 | loss: 6.064062
iter: 2333 | loss: 6.063714
iter: 2334 | loss: 6.063367
iter: 2335 | loss: 6.063019
iter: 2336 | loss: 6.062672
iter: 2337 | loss: 6.062324
iter: 2338 | loss: 6.061977
iter: 2339 | loss: 6.061629
iter: 2340 | loss: 6.061282
iter: 2341 | loss: 6.060934
iter: 2342 | loss: 6.060587
iter: 2343 | loss: 6.060239
iter: 2344 | loss: 6.059892
iter: 2345 | loss: 6.059544
iter: 2346 | loss: 6.059197
iter: 2347 | loss: 6.058849
iter: 2348 | loss: 6.058502
iter: 2349 | loss: 6.058154
iter: 2350 | loss: 6.057807
iter: 2351 | loss: 6.057459
iter: 2352 | loss: 6.057112
iter: 2353 | loss: 6.056764
iter: 2354 | loss: 6.056417
iter: 2355 | loss: 6.056069
iter: 2356 | loss: 6.055722
iter: 2357 | loss: 6.055375
iter: 2358 | loss: 6.055027
iter: 2359 | loss: 6.054680
iter: 2360 | loss: 6.054332
iter: 2361 | loss: 6.053985
iter: 2362 | loss: 6.053637
iter: 2363 | loss: 6.053290
iter: 2364 | loss: 6.052942
iter: 2365 | loss: 6.052595
iter: 2366 | loss: 6.052247
iter: 2367 | loss: 6.051900
iter: 2368 | loss: 6.051552
iter: 2369 | loss: 6.051205
iter: 2370 | loss: 6.050857
iter: 2371 | loss: 6.050510
iter: 2372 | loss: 6.050162
iter: 2373 | loss: 6.049815
iter: 2374 | loss: 6.049467
iter: 2375 | loss: 6.049120
iter: 2376 | loss: 6.048772
iter: 2377 | loss: 6.048425
iter: 2378 | loss: 6.048077
iter: 2379 | loss: 6.047730
iter: 2380 | loss: 6.047382
iter: 2381 | loss: 6.047035
iter: 2382 | loss: 6.046687
iter: 2383 | loss: 6.046340
iter: 2384 | loss: 6.045992
iter: 2385 | loss: 6.045645
iter: 2386 | loss: 6.045297
iter: 2387 | loss: 6.044950
iter: 2388 | loss: 6.044602
iter: 2389 | loss: 6.044255
iter: 2390 | loss: 6.043907
iter: 2391 | loss: 6.043560
iter: 2392 | loss: 6.043212
iter: 2393 | loss: 6.042865
iter: 2394 | loss: 6.042517
iter: 2395 | loss: 6.042170
iter: 2396 | loss: 6.041822
iter: 2397 | loss: 6.041475
iter: 2398 | loss: 6.041127
iter: 2399 | loss: 6.040780
iter: 2400 | loss: 6.040432
iter: 2401 | loss: 6.040085
iter: 2402 | loss: 6.039738
iter: 2403 | loss: 6.039390
iter: 2404 | loss: 6.039043
iter: 2405 | loss: 6.038695
iter: 2406 | loss: 6.038348
iter: 2407 | loss: 6.038000
iter: 2408 | loss: 6.037653
iter: 2409 | loss: 6.037305
iter: 2410 | loss: 6.036958
iter: 2411 | loss: 6.036610
iter: 2412 | loss: 6.036263
iter: 2413 | loss: 6.035915
iter: 2414 | loss: 6.035568
iter: 2415 | loss: 6.035220
iter: 2416 | loss: 6.034873
iter: 2417 | loss: 6.034525
iter: 2418 | loss: 6.034178
iter: 2419 | loss: 6.033830
iter: 2420 | loss: 6.033483
iter: 2421 | loss: 6.033135
iter: 2422 | loss: 6.032788
iter: 2423 | loss: 6.032440
iter: 2424 | loss: 6.032093
iter: 2425 | loss: 6.031745
iter: 2426 | loss: 6.031398
iter: 2427 | loss: 6.031050
iter: 2428 | loss: 6.030703
iter: 2429 | loss: 6.030355
iter: 2430 | loss: 6.030008
iter: 2431 | loss: 6.029660
iter: 2432 | loss: 6.029313
iter: 2433 | loss: 6.028965
iter: 2434 | loss: 6.028618
iter: 2435 | loss: 6.028270
iter: 2436 | loss: 6.027923
iter: 2437 | loss: 6.027575
iter: 2438 | loss: 6.027228
iter: 2439 | loss: 6.026880
iter: 2440 | loss: 6.026533
iter: 2441 | loss: 6.026185
iter: 2442 | loss: 6.025838
iter: 2443 | loss: 6.025490
iter: 2444 | loss: 6.025143
iter: 2445 | loss: 6.024796
iter: 2446 | loss: 6.024448
iter: 2447 | loss: 6.024101
iter: 2448 | loss: 6.023753
iter: 2449 | loss: 6.023406
iter: 2450 | loss: 6.023058
iter: 2451 | loss: 6.022711
iter: 2452 | loss: 6.022363
iter: 2453 | loss: 6.022016
iter: 2454 | loss: 6.021668
iter: 2455 | loss: 6.021321
iter: 2456 | loss: 6.020973
iter: 2457 | loss: 6.020626
iter: 2458 | loss: 6.020278
iter: 2459 | loss: 6.019931
iter: 2460 | loss: 6.019583
iter: 2461 | loss: 6.019236
iter: 2462 | loss: 6.018888
iter: 2463 | loss: 6.018541
iter: 2464 | loss: 6.018193
iter: 2465 | loss: 6.017846
iter: 2466 | loss: 6.017498
iter: 2467 | loss: 6.017151
iter: 2468 | loss: 6.016803
iter: 2469 | loss: 6.016456
iter: 2470 | loss: 6.016108
iter: 2471 | loss: 6.015761
iter: 2472 | loss: 6.015413
iter: 2473 | loss: 6.015066
iter: 2474 | loss: 6.014718
iter: 2475 | loss: 6.014371
iter: 2476 | loss: 6.014023
iter: 2477 | loss: 6.013676
iter: 2478 | loss: 6.013328
iter: 2479 | loss: 6.012981
iter: 2480 | loss: 6.012633
iter: 2481 | loss: 6.012286
iter: 2482 | loss: 6.011938
iter: 2483 | loss: 6.011591
iter: 2484 | loss: 6.011243
iter: 2485 | loss: 6.010896
iter: 2486 | loss: 6.010548
iter: 2487 | loss: 6.010201
iter: 2488 | loss: 6.009854
iter: 2489 | loss: 6.009506
iter: 2490 | loss: 6.009159
iter: 2491 | loss: 6.008811
iter: 2492 | loss: 6.008464
iter: 2493 | loss: 6.008116
iter: 2494 | loss: 6.007769
iter: 2495 | loss: 6.007421
iter: 2496 | loss: 6.007074
iter: 2497 | loss: 6.006726
iter: 2498 | loss: 6.006379
iter: 2499 | loss: 6.006031
iter: 2500 | loss: 6.005684
iter: 2501 | loss: 6.005336
iter: 2502 | loss: 6.004989
iter: 2503 | loss: 6.004641
iter: 2504 | loss: 6.004294
iter: 2505 | loss: 6.003946
iter: 2506 | loss: 6.003599
iter: 2507 | loss: 6.003251
iter: 2508 | loss: 6.002904
iter: 2509 | loss: 6.002556
iter: 2510 | loss: 6.002209
iter: 2511 | loss: 6.001861
iter: 2512 | loss: 6.001514
iter: 2513 | loss: 6.001166
iter: 2514 | loss: 6.000819
iter: 2515 | loss: 6.000471
iter: 2516 | loss: 6.000124
iter: 2517 | loss: 5.999776
iter: 2518 | loss: 5.999429
iter: 2519 | loss: 5.999081
iter: 2520 | loss: 5.998734
iter: 2521 | loss: 5.998386
iter: 2522 | loss: 5.998039
iter: 2523 | loss: 5.997691
iter: 2524 | loss: 5.997344
iter: 2525 | loss: 5.996996
iter: 2526 | loss: 5.996649
iter: 2527 | loss: 5.996301
iter: 2528 | loss: 5.995954
iter: 2529 | loss: 5.995606
iter: 2530 | loss: 5.995259
iter: 2531 | loss: 5.994911
iter: 2532 | loss: 5.994564
iter: 2533 | loss: 5.994217
iter: 2534 | loss: 5.993869
iter: 2535 | loss: 5.993522
iter: 2536 | loss: 5.993174
iter: 2537 | loss: 5.992827
iter: 2538 | loss: 5.992479
iter: 2539 | loss: 5.992132
iter: 2540 | loss: 5.991784
iter: 2541 | loss: 5.991437
iter: 2542 | loss: 5.991089
iter: 2543 | loss: 5.990742
iter: 2544 | loss: 5.990394
iter: 2545 | loss: 5.990047
iter: 2546 | loss: 5.989699
iter: 2547 | loss: 5.989352
iter: 2548 | loss: 5.989004
iter: 2549 | loss: 5.988657
iter: 2550 | loss: 5.988309
iter: 2551 | loss: 5.987962
iter: 2552 | loss: 5.987614
iter: 2553 | loss: 5.987267
iter: 2554 | loss: 5.986919
iter: 2555 | loss: 5.986572
iter: 2556 | loss: 5.986224
iter: 2557 | loss: 5.985877
iter: 2558 | loss: 5.985529
iter: 2559 | loss: 5.985182
iter: 2560 | loss: 5.984834
iter: 2561 | loss: 5.984487
iter: 2562 | loss: 5.984139
iter: 2563 | loss: 5.983792
iter: 2564 | loss: 5.983444
iter: 2565 | loss: 5.983097
iter: 2566 | loss: 5.982749
iter: 2567 | loss: 5.982402
iter: 2568 | loss: 5.982054
iter: 2569 | loss: 5.981707
iter: 2570 | loss: 5.981359
iter: 2571 | loss: 5.981012
iter: 2572 | loss: 5.980664
iter: 2573 | loss: 5.980317
iter: 2574 | loss: 5.979969
iter: 2575 | loss: 5.979622
iter: 2576 | loss: 5.979275
iter: 2577 | loss: 5.978927
iter: 2578 | loss: 5.978580
iter: 2579 | loss: 5.978232
iter: 2580 | loss: 5.977885
iter: 2581 | loss: 5.977537
iter: 2582 | loss: 5.977190
iter: 2583 | loss: 5.976842
iter: 2584 | loss: 5.976495
iter: 2585 | loss: 5.976147
iter: 2586 | loss: 5.975800
iter: 2587 | loss: 5.975452
iter: 2588 | loss: 5.975105
iter: 2589 | loss: 5.974757
iter: 2590 | loss: 5.974410
iter: 2591 | loss: 5.974062
iter: 2592 | loss: 5.973715
iter: 2593 | loss: 5.973367
iter: 2594 | loss: 5.973020
iter: 2595 | loss: 5.972672
iter: 2596 | loss: 5.972325
iter: 2597 | loss: 5.971977
iter: 2598 | loss: 5.971630
iter: 2599 | loss: 5.971282
iter: 2600 | loss: 5.970935
iter: 2601 | loss: 5.970587
iter: 2602 | loss: 5.970240
iter: 2603 | loss: 5.969892
iter: 2604 | loss: 5.969545
iter: 2605 | loss: 5.969197
iter: 2606 | loss: 5.968850
iter: 2607 | loss: 5.968502
iter: 2608 | loss: 5.968155
iter: 2609 | loss: 5.967807
iter: 2610 | loss: 5.967460
iter: 2611 | loss: 5.967112
iter: 2612 | loss: 5.966765
iter: 2613 | loss: 5.966417
iter: 2614 | loss: 5.966070
iter: 2615 | loss: 5.965722
iter: 2616 | loss: 5.965375
iter: 2617 | loss: 5.965027
iter: 2618 | loss: 5.964680
iter: 2619 | loss: 5.964332
iter: 2620 | loss: 5.963985
iter: 2621 | loss: 5.963638
iter: 2622 | loss: 5.963290
iter: 2623 | loss: 5.962943
iter: 2624 | loss: 5.962595
iter: 2625 | loss: 5.962248
iter: 2626 | loss: 5.961900
iter: 2627 | loss: 5.961553
iter: 2628 | loss: 5.961205
iter: 2629 | loss: 5.960858
iter: 2630 | loss: 5.960510
iter: 2631 | loss: 5.960163
iter: 2632 | loss: 5.959815
iter: 2633 | loss: 5.959468
iter: 2634 | loss: 5.959120
iter: 2635 | loss: 5.958773
iter: 2636 | loss: 5.958425
iter: 2637 | loss: 5.958078
iter: 2638 | loss: 5.957730
iter: 2639 | loss: 5.957383
iter: 2640 | loss: 5.957035
iter: 2641 | loss: 5.956688
iter: 2642 | loss: 5.956340
iter: 2643 | loss: 5.955993
iter: 2644 | loss: 5.955645
iter: 2645 | loss: 5.955298
iter: 2646 | loss: 5.954950
iter: 2647 | loss: 5.954603
iter: 2648 | loss: 5.954255
iter: 2649 | loss: 5.953908
iter: 2650 | loss: 5.953560
iter: 2651 | loss: 5.953213
iter: 2652 | loss: 5.952865
iter: 2653 | loss: 5.952518
iter: 2654 | loss: 5.952170
iter: 2655 | loss: 5.951823
iter: 2656 | loss: 5.951475
iter: 2657 | loss: 5.951128
iter: 2658 | loss: 5.950780
iter: 2659 | loss: 5.950433
iter: 2660 | loss: 5.950085
iter: 2661 | loss: 5.949738
iter: 2662 | loss: 5.949390
iter: 2663 | loss: 5.949043
iter: 2664 | loss: 5.948696
iter: 2665 | loss: 5.948348
iter: 2666 | loss: 5.948001
iter: 2667 | loss: 5.947653
iter: 2668 | loss: 5.947306
iter: 2669 | loss: 5.946958
iter: 2670 | loss: 5.946611
iter: 2671 | loss: 5.946263
iter: 2672 | loss: 5.945916
iter: 2673 | loss: 5.945568
iter: 2674 | loss: 5.945221
iter: 2675 | loss: 5.944873
iter: 2676 | loss: 5.944526
iter: 2677 | loss: 5.944178
iter: 2678 | loss: 5.943831
iter: 2679 | loss: 5.943483
iter: 2680 | loss: 5.943136
iter: 2681 | loss: 5.942788
iter: 2682 | loss: 5.942441
iter: 2683 | loss: 5.942093
iter: 2684 | loss: 5.941746
iter: 2685 | loss: 5.941398
iter: 2686 | loss: 5.941051
iter: 2687 | loss: 5.940703
iter: 2688 | loss: 5.940356
iter: 2689 | loss: 5.940008
iter: 2690 | loss: 5.939661
iter: 2691 | loss: 5.939313
iter: 2692 | loss: 5.938966
iter: 2693 | loss: 5.938618
iter: 2694 | loss: 5.938271
iter: 2695 | loss: 5.937923
iter: 2696 | loss: 5.937576
iter: 2697 | loss: 5.937228
iter: 2698 | loss: 5.936881
iter: 2699 | loss: 5.936533
iter: 2700 | loss: 5.936186
iter: 2701 | loss: 5.935838
iter: 2702 | loss: 5.935491
iter: 2703 | loss: 5.935143
iter: 2704 | loss: 5.934796
iter: 2705 | loss: 5.934448
iter: 2706 | loss: 5.934101
iter: 2707 | loss: 5.933753
iter: 2708 | loss: 5.933406
iter: 2709 | loss: 5.933059
iter: 2710 | loss: 5.932711
iter: 2711 | loss: 5.932364
iter: 2712 | loss: 5.932016
iter: 2713 | loss: 5.931669
iter: 2714 | loss: 5.931321
iter: 2715 | loss: 5.930974
iter: 2716 | loss: 5.930626
iter: 2717 | loss: 5.930279
iter: 2718 | loss: 5.929931
iter: 2719 | loss: 5.929584
iter: 2720 | loss: 5.929236
iter: 2721 | loss: 5.928889
iter: 2722 | loss: 5.928541
iter: 2723 | loss: 5.928194
iter: 2724 | loss: 5.927846
iter: 2725 | loss: 5.927499
iter: 2726 | loss: 5.927151
iter: 2727 | loss: 5.926804
iter: 2728 | loss: 5.926456
iter: 2729 | loss: 5.926109
iter: 2730 | loss: 5.925761
iter: 2731 | loss: 5.925414
iter: 2732 | loss: 5.925066
iter: 2733 | loss: 5.924719
iter: 2734 | loss: 5.924371
iter: 2735 | loss: 5.924024
iter: 2736 | loss: 5.923676
iter: 2737 | loss: 5.923329
iter: 2738 | loss: 5.922981
iter: 2739 | loss: 5.922634
iter: 2740 | loss: 5.922286
iter: 2741 | loss: 5.921939
iter: 2742 | loss: 5.921591
iter: 2743 | loss: 5.921244
iter: 2744 | loss: 5.920896
iter: 2745 | loss: 5.920549
iter: 2746 | loss: 5.920201
iter: 2747 | loss: 5.919854
iter: 2748 | loss: 5.919506
iter: 2749 | loss: 5.919159
iter: 2750 | loss: 5.918811
iter: 2751 | loss: 5.918464
iter: 2752 | loss: 5.918117
iter: 2753 | loss: 5.917769
iter: 2754 | loss: 5.917422
iter: 2755 | loss: 5.917074
iter: 2756 | loss: 5.916727
iter: 2757 | loss: 5.916379
iter: 2758 | loss: 5.916032
iter: 2759 | loss: 5.915684
iter: 2760 | loss: 5.915337
iter: 2761 | loss: 5.914989
iter: 2762 | loss: 5.914642
iter: 2763 | loss: 5.914294
iter: 2764 | loss: 5.913947
iter: 2765 | loss: 5.913599
iter: 2766 | loss: 5.913252
iter: 2767 | loss: 5.912904
iter: 2768 | loss: 5.912557
iter: 2769 | loss: 5.912209
iter: 2770 | loss: 5.911862
iter: 2771 | loss: 5.911514
iter: 2772 | loss: 5.911167
iter: 2773 | loss: 5.910819
iter: 2774 | loss: 5.910472
iter: 2775 | loss: 5.910124
iter: 2776 | loss: 5.909777
iter: 2777 | loss: 5.909429
iter: 2778 | loss: 5.909082
iter: 2779 | loss: 5.908734
iter: 2780 | loss: 5.908387
iter: 2781 | loss: 5.908039
iter: 2782 | loss: 5.907692
iter: 2783 | loss: 5.907344
iter: 2784 | loss: 5.906997
iter: 2785 | loss: 5.906649
iter: 2786 | loss: 5.906302
iter: 2787 | loss: 5.905954
iter: 2788 | loss: 5.905607
iter: 2789 | loss: 5.905259
iter: 2790 | loss: 5.904912
iter: 2791 | loss: 5.904564
iter: 2792 | loss: 5.904217
iter: 2793 | loss: 5.903869
iter: 2794 | loss: 5.903522
iter: 2795 | loss: 5.903175
iter: 2796 | loss: 5.902827
iter: 2797 | loss: 5.902480
iter: 2798 | loss: 5.902132
iter: 2799 | loss: 5.901785
iter: 2800 | loss: 5.901437
iter: 2801 | loss: 5.901090
iter: 2802 | loss: 5.900742
iter: 2803 | loss: 5.900395
iter: 2804 | loss: 5.900047
iter: 2805 | loss: 5.899700
iter: 2806 | loss: 5.899352
iter: 2807 | loss: 5.899005
iter: 2808 | loss: 5.898657
iter: 2809 | loss: 5.898310
iter: 2810 | loss: 5.897962
iter: 2811 | loss: 5.897615
iter: 2812 | loss: 5.897267
iter: 2813 | loss: 5.896920
iter: 2814 | loss: 5.896572
iter: 2815 | loss: 5.896225
iter: 2816 | loss: 5.895877
iter: 2817 | loss: 5.895530
iter: 2818 | loss: 5.895182
iter: 2819 | loss: 5.894835
iter: 2820 | loss: 5.894487
iter: 2821 | loss: 5.894140
iter: 2822 | loss: 5.893792
iter: 2823 | loss: 5.893445
iter: 2824 | loss: 5.893097
iter: 2825 | loss: 5.892750
iter: 2826 | loss: 5.892402
iter: 2827 | loss: 5.892055
iter: 2828 | loss: 5.891707
iter: 2829 | loss: 5.891360
iter: 2830 | loss: 5.891012
iter: 2831 | loss: 5.890665
iter: 2832 | loss: 5.890317
iter: 2833 | loss: 5.889970
iter: 2834 | loss: 5.889622
iter: 2835 | loss: 5.889275
iter: 2836 | loss: 5.888927
iter: 2837 | loss: 5.888580
iter: 2838 | loss: 5.888232
iter: 2839 | loss: 5.887885
iter: 2840 | loss: 5.887538
iter: 2841 | loss: 5.887190
iter: 2842 | loss: 5.886843
iter: 2843 | loss: 5.886495
iter: 2844 | loss: 5.886148
iter: 2845 | loss: 5.885800
iter: 2846 | loss: 5.885453
iter: 2847 | loss: 5.885105
iter: 2848 | loss: 5.884758
iter: 2849 | loss: 5.884410
iter: 2850 | loss: 5.884063
iter: 2851 | loss: 5.883715
iter: 2852 | loss: 5.883368
iter: 2853 | loss: 5.883020
iter: 2854 | loss: 5.882673
iter: 2855 | loss: 5.882325
iter: 2856 | loss: 5.881978
iter: 2857 | loss: 5.881630
iter: 2858 | loss: 5.881283
iter: 2859 | loss: 5.880935
iter: 2860 | loss: 5.880588
iter: 2861 | loss: 5.880240
iter: 2862 | loss: 5.879893
iter: 2863 | loss: 5.879545
iter: 2864 | loss: 5.879198
iter: 2865 | loss: 5.878850
iter: 2866 | loss: 5.878503
iter: 2867 | loss: 5.878155
iter: 2868 | loss: 5.877808
iter: 2869 | loss: 5.877460
iter: 2870 | loss: 5.877113
iter: 2871 | loss: 5.876765
iter: 2872 | loss: 5.876418
iter: 2873 | loss: 5.876070
iter: 2874 | loss: 5.875723
iter: 2875 | loss: 5.875375
iter: 2876 | loss: 5.875028
iter: 2877 | loss: 5.874680
iter: 2878 | loss: 5.874333
iter: 2879 | loss: 5.873985
iter: 2880 | loss: 5.873638
iter: 2881 | loss: 5.873290
iter: 2882 | loss: 5.872943
iter: 2883 | loss: 5.872596
iter: 2884 | loss: 5.872248
iter: 2885 | loss: 5.871901
iter: 2886 | loss: 5.871553
iter: 2887 | loss: 5.871206
iter: 2888 | loss: 5.870858
iter: 2889 | loss: 5.870511
iter: 2890 | loss: 5.870163
iter: 2891 | loss: 5.869816
iter: 2892 | loss: 5.869468
iter: 2893 | loss: 5.869121
iter: 2894 | loss: 5.868773
iter: 2895 | loss: 5.868426
iter: 2896 | loss: 5.868078
iter: 2897 | loss: 5.867731
iter: 2898 | loss: 5.867383
iter: 2899 | loss: 5.867036
iter: 2900 | loss: 5.866688
iter: 2901 | loss: 5.866341
iter: 2902 | loss: 5.865993
iter: 2903 | loss: 5.865646
iter: 2904 | loss: 5.865298
iter: 2905 | loss: 5.864951
iter: 2906 | loss: 5.864603
iter: 2907 | loss: 5.864256
iter: 2908 | loss: 5.863908
iter: 2909 | loss: 5.863561
iter: 2910 | loss: 5.863213
iter: 2911 | loss: 5.862866
iter: 2912 | loss: 5.862518
iter: 2913 | loss: 5.862171
iter: 2914 | loss: 5.861823
iter: 2915 | loss: 5.861476
iter: 2916 | loss: 5.861128
iter: 2917 | loss: 5.860781
iter: 2918 | loss: 5.860433
iter: 2919 | loss: 5.860086
iter: 2920 | loss: 5.859738
iter: 2921 | loss: 5.859391
iter: 2922 | loss: 5.859043
iter: 2923 | loss: 5.858696
iter: 2924 | loss: 5.858348
iter: 2925 | loss: 5.858001
iter: 2926 | loss: 5.857653
iter: 2927 | loss: 5.857306
iter: 2928 | loss: 5.856959
iter: 2929 | loss: 5.856611
iter: 2930 | loss: 5.856264
iter: 2931 | loss: 5.855916
iter: 2932 | loss: 5.855569
iter: 2933 | loss: 5.855221
iter: 2934 | loss: 5.854874
iter: 2935 | loss: 5.854526
iter: 2936 | loss: 5.854179
iter: 2937 | loss: 5.853831
iter: 2938 | loss: 5.853484
iter: 2939 | loss: 5.853136
iter: 2940 | loss: 5.852789
iter: 2941 | loss: 5.852441
iter: 2942 | loss: 5.852094
iter: 2943 | loss: 5.851746
iter: 2944 | loss: 5.851399
iter: 2945 | loss: 5.851051
iter: 2946 | loss: 5.850704
iter: 2947 | loss: 5.850356
iter: 2948 | loss: 5.850009
iter: 2949 | loss: 5.849661
iter: 2950 | loss: 5.849314
iter: 2951 | loss: 5.848966
iter: 2952 | loss: 5.848619
iter: 2953 | loss: 5.848271
iter: 2954 | loss: 5.847924
iter: 2955 | loss: 5.847576
iter: 2956 | loss: 5.847229
iter: 2957 | loss: 5.846881
iter: 2958 | loss: 5.846534
iter: 2959 | loss: 5.846186
iter: 2960 | loss: 5.845839
iter: 2961 | loss: 5.845491
iter: 2962 | loss: 5.845144
iter: 2963 | loss: 5.844796
iter: 2964 | loss: 5.844449
iter: 2965 | loss: 5.844101
iter: 2966 | loss: 5.843754
iter: 2967 | loss: 5.843406
iter: 2968 | loss: 5.843059
iter: 2969 | loss: 5.842711
iter: 2970 | loss: 5.842364
iter: 2971 | loss: 5.842017
iter: 2972 | loss: 5.841669
iter: 2973 | loss: 5.841322
iter: 2974 | loss: 5.840974
iter: 2975 | loss: 5.840627
iter: 2976 | loss: 5.840279
iter: 2977 | loss: 5.839932
iter: 2978 | loss: 5.839584
iter: 2979 | loss: 5.839237
iter: 2980 | loss: 5.838889
iter: 2981 | loss: 5.838542
iter: 2982 | loss: 5.838194
iter: 2983 | loss: 5.837847
iter: 2984 | loss: 5.837499
iter: 2985 | loss: 5.837152
iter: 2986 | loss: 5.836804
iter: 2987 | loss: 5.836457
iter: 2988 | loss: 5.836109
iter: 2989 | loss: 5.835762
iter: 2990 | loss: 5.835414
iter: 2991 | loss: 5.835067
iter: 2992 | loss: 5.834719
iter: 2993 | loss: 5.834372
iter: 2994 | loss: 5.834024
iter: 2995 | loss: 5.833677
iter: 2996 | loss: 5.833329
iter: 2997 | loss: 5.832982
iter: 2998 | loss: 5.832634
iter: 2999 | loss: 5.832287
iter: 3000 | loss: 5.831939
iter: 3001 | loss: 5.831592
iter: 3002 | loss: 5.831244
iter: 3003 | loss: 5.830897
iter: 3004 | loss: 5.830549
iter: 3005 | loss: 5.830202
iter: 3006 | loss: 5.829854
iter: 3007 | loss: 5.829507
iter: 3008 | loss: 5.829159
iter: 3009 | loss: 5.828812
iter: 3010 | loss: 5.828464
iter: 3011 | loss: 5.828117
iter: 3012 | loss: 5.827769
iter: 3013 | loss: 5.827422
iter: 3014 | loss: 5.827075
iter: 3015 | loss: 5.826727
iter: 3016 | loss: 5.826380
iter: 3017 | loss: 5.826032
iter: 3018 | loss: 5.825685
iter: 3019 | loss: 5.825337
iter: 3020 | loss: 5.824990
iter: 3021 | loss: 5.824642
iter: 3022 | loss: 5.824295
iter: 3023 | loss: 5.823947
iter: 3024 | loss: 5.823600
iter: 3025 | loss: 5.823252
iter: 3026 | loss: 5.822905
iter: 3027 | loss: 5.822557
iter: 3028 | loss: 5.822210
iter: 3029 | loss: 5.821862
iter: 3030 | loss: 5.821515
iter: 3031 | loss: 5.821167
iter: 3032 | loss: 5.820820
iter: 3033 | loss: 5.820472
iter: 3034 | loss: 5.820125
iter: 3035 | loss: 5.819777
iter: 3036 | loss: 5.819430
iter: 3037 | loss: 5.819082
iter: 3038 | loss: 5.818735
iter: 3039 | loss: 5.818387
iter: 3040 | loss: 5.818040
iter: 3041 | loss: 5.817692
iter: 3042 | loss: 5.817345
iter: 3043 | loss: 5.816997
iter: 3044 | loss: 5.816650
iter: 3045 | loss: 5.816302
iter: 3046 | loss: 5.815955
iter: 3047 | loss: 5.815607
iter: 3048 | loss: 5.815260
iter: 3049 | loss: 5.814912
iter: 3050 | loss: 5.814565
iter: 3051 | loss: 5.814217
iter: 3052 | loss: 5.813870
iter: 3053 | loss: 5.813522
iter: 3054 | loss: 5.813175
iter: 3055 | loss: 5.812827
iter: 3056 | loss: 5.812480
iter: 3057 | loss: 5.812132
iter: 3058 | loss: 5.811785
iter: 3059 | loss: 5.811438
iter: 3060 | loss: 5.811090
iter: 3061 | loss: 5.810743
iter: 3062 | loss: 5.810395
iter: 3063 | loss: 5.810048
iter: 3064 | loss: 5.809700
iter: 3065 | loss: 5.809353
iter: 3066 | loss: 5.809005
iter: 3067 | loss: 5.808658
iter: 3068 | loss: 5.808310
iter: 3069 | loss: 5.807963
iter: 3070 | loss: 5.807615
iter: 3071 | loss: 5.807268
iter: 3072 | loss: 5.806920
iter: 3073 | loss: 5.806573
iter: 3074 | loss: 5.806225
iter: 3075 | loss: 5.805878
iter: 3076 | loss: 5.805530
iter: 3077 | loss: 5.805183
iter: 3078 | loss: 5.804835
iter: 3079 | loss: 5.804488
iter: 3080 | loss: 5.804140
iter: 3081 | loss: 5.803793
iter: 3082 | loss: 5.803445
iter: 3083 | loss: 5.803098
iter: 3084 | loss: 5.802750
iter: 3085 | loss: 5.802403
iter: 3086 | loss: 5.802055
iter: 3087 | loss: 5.801708
iter: 3088 | loss: 5.801360
iter: 3089 | loss: 5.801013
iter: 3090 | loss: 5.800665
iter: 3091 | loss: 5.800318
iter: 3092 | loss: 5.799970
iter: 3093 | loss: 5.799623
iter: 3094 | loss: 5.799275
iter: 3095 | loss: 5.798928
iter: 3096 | loss: 5.798580
iter: 3097 | loss: 5.798233
iter: 3098 | loss: 5.797885
iter: 3099 | loss: 5.797538
iter: 3100 | loss: 5.797190
iter: 3101 | loss: 5.796843
iter: 3102 | loss: 5.796496
iter: 3103 | loss: 5.796148
iter: 3104 | loss: 5.795801
iter: 3105 | loss: 5.795453
iter: 3106 | loss: 5.795106
iter: 3107 | loss: 5.794758
iter: 3108 | loss: 5.794411
iter: 3109 | loss: 5.794063
iter: 3110 | loss: 5.793716
iter: 3111 | loss: 5.793368
iter: 3112 | loss: 5.793021
iter: 3113 | loss: 5.792673
iter: 3114 | loss: 5.792326
iter: 3115 | loss: 5.791978
iter: 3116 | loss: 5.791631
iter: 3117 | loss: 5.791283
iter: 3118 | loss: 5.790936
iter: 3119 | loss: 5.790588
iter: 3120 | loss: 5.790241
iter: 3121 | loss: 5.789893
iter: 3122 | loss: 5.789546
iter: 3123 | loss: 5.789198
iter: 3124 | loss: 5.788851
iter: 3125 | loss: 5.788503
iter: 3126 | loss: 5.788156
iter: 3127 | loss: 5.787808
iter: 3128 | loss: 5.787461
iter: 3129 | loss: 5.787113
iter: 3130 | loss: 5.786766
iter: 3131 | loss: 5.786418
iter: 3132 | loss: 5.786071
iter: 3133 | loss: 5.785723
iter: 3134 | loss: 5.785376
iter: 3135 | loss: 5.785028
iter: 3136 | loss: 5.784681
iter: 3137 | loss: 5.784333
iter: 3138 | loss: 5.783986
iter: 3139 | loss: 5.783638
iter: 3140 | loss: 5.783291
iter: 3141 | loss: 5.782943
iter: 3142 | loss: 5.782596
iter: 3143 | loss: 5.782248
iter: 3144 | loss: 5.781901
iter: 3145 | loss: 5.781553
iter: 3146 | loss: 5.781206
iter: 3147 | loss: 5.780859
iter: 3148 | loss: 5.780511
iter: 3149 | loss: 5.780164
iter: 3150 | loss: 5.779816
iter: 3151 | loss: 5.779469
iter: 3152 | loss: 5.779121
iter: 3153 | loss: 5.778774
iter: 3154 | loss: 5.778426
iter: 3155 | loss: 5.778079
iter: 3156 | loss: 5.777731
iter: 3157 | loss: 5.777384
iter: 3158 | loss: 5.777036
iter: 3159 | loss: 5.776689
iter: 3160 | loss: 5.776341
iter: 3161 | loss: 5.775994
iter: 3162 | loss: 5.775646
iter: 3163 | loss: 5.775299
iter: 3164 | loss: 5.774951
iter: 3165 | loss: 5.774604
iter: 3166 | loss: 5.774256
iter: 3167 | loss: 5.773909
iter: 3168 | loss: 5.773561
iter: 3169 | loss: 5.773214
iter: 3170 | loss: 5.772866
iter: 3171 | loss: 5.772519
iter: 3172 | loss: 5.772171
iter: 3173 | loss: 5.771824
iter: 3174 | loss: 5.771476
iter: 3175 | loss: 5.771129
iter: 3176 | loss: 5.770781
iter: 3177 | loss: 5.770434
iter: 3178 | loss: 5.770086
iter: 3179 | loss: 5.769739
iter: 3180 | loss: 5.769391
iter: 3181 | loss: 5.769044
iter: 3182 | loss: 5.768696
iter: 3183 | loss: 5.768349
iter: 3184 | loss: 5.768001
iter: 3185 | loss: 5.767654
iter: 3186 | loss: 5.767306
iter: 3187 | loss: 5.766959
iter: 3188 | loss: 5.766611
iter: 3189 | loss: 5.766264
iter: 3190 | loss: 5.765917
iter: 3191 | loss: 5.765569
iter: 3192 | loss: 5.765222
iter: 3193 | loss: 5.764874
iter: 3194 | loss: 5.764527
iter: 3195 | loss: 5.764179
iter: 3196 | loss: 5.763832
iter: 3197 | loss: 5.763484
iter: 3198 | loss: 5.763137
iter: 3199 | loss: 5.762789
iter: 3200 | loss: 5.762442
iter: 3201 | loss: 5.762094
iter: 3202 | loss: 5.761747
iter: 3203 | loss: 5.761399
iter: 3204 | loss: 5.761052
iter: 3205 | loss: 5.760704
iter: 3206 | loss: 5.760357
iter: 3207 | loss: 5.760009
iter: 3208 | loss: 5.759662
iter: 3209 | loss: 5.759314
iter: 3210 | loss: 5.758967
iter: 3211 | loss: 5.758619
iter: 3212 | loss: 5.758272
iter: 3213 | loss: 5.757924
iter: 3214 | loss: 5.757577
iter: 3215 | loss: 5.757229
iter: 3216 | loss: 5.756882
iter: 3217 | loss: 5.756534
iter: 3218 | loss: 5.756187
iter: 3219 | loss: 5.755839
iter: 3220 | loss: 5.755492
iter: 3221 | loss: 5.755144
iter: 3222 | loss: 5.754797
iter: 3223 | loss: 5.754449
iter: 3224 | loss: 5.754102
iter: 3225 | loss: 5.753754
iter: 3226 | loss: 5.753407
iter: 3227 | loss: 5.753059
iter: 3228 | loss: 5.752712
iter: 3229 | loss: 5.752364
iter: 3230 | loss: 5.752017
iter: 3231 | loss: 5.751669
iter: 3232 | loss: 5.751322
iter: 3233 | loss: 5.750974
iter: 3234 | loss: 5.750627
iter: 3235 | loss: 5.750280
iter: 3236 | loss: 5.749932
iter: 3237 | loss: 5.749585
iter: 3238 | loss: 5.749237
iter: 3239 | loss: 5.748890
iter: 3240 | loss: 5.748542
iter: 3241 | loss: 5.748195
iter: 3242 | loss: 5.747847
iter: 3243 | loss: 5.747500
iter: 3244 | loss: 5.747152
iter: 3245 | loss: 5.746805
iter: 3246 | loss: 5.746457
iter: 3247 | loss: 5.746110
iter: 3248 | loss: 5.745762
iter: 3249 | loss: 5.745415
iter: 3250 | loss: 5.745067
iter: 3251 | loss: 5.744720
iter: 3252 | loss: 5.744372
iter: 3253 | loss: 5.744025
iter: 3254 | loss: 5.743677
iter: 3255 | loss: 5.743330
iter: 3256 | loss: 5.742982
iter: 3257 | loss: 5.742635
iter: 3258 | loss: 5.742287
iter: 3259 | loss: 5.741940
iter: 3260 | loss: 5.741592
iter: 3261 | loss: 5.741245
iter: 3262 | loss: 5.740897
iter: 3263 | loss: 5.740550
iter: 3264 | loss: 5.740202
iter: 3265 | loss: 5.739855
iter: 3266 | loss: 5.739507
iter: 3267 | loss: 5.739160
iter: 3268 | loss: 5.738812
iter: 3269 | loss: 5.738465
iter: 3270 | loss: 5.738117
iter: 3271 | loss: 5.737770
iter: 3272 | loss: 5.737422
iter: 3273 | loss: 5.737075
iter: 3274 | loss: 5.736727
iter: 3275 | loss: 5.736380
iter: 3276 | loss: 5.736032
iter: 3277 | loss: 5.735685
iter: 3278 | loss: 5.735338
iter: 3279 | loss: 5.734990
iter: 3280 | loss: 5.734643
iter: 3281 | loss: 5.734295
iter: 3282 | loss: 5.733948
iter: 3283 | loss: 5.733600
iter: 3284 | loss: 5.733253
iter: 3285 | loss: 5.732905
iter: 3286 | loss: 5.732558
iter: 3287 | loss: 5.732210
iter: 3288 | loss: 5.731863
iter: 3289 | loss: 5.731515
iter: 3290 | loss: 5.731168
iter: 3291 | loss: 5.730820
iter: 3292 | loss: 5.730473
iter: 3293 | loss: 5.730125
iter: 3294 | loss: 5.729778
iter: 3295 | loss: 5.729430
iter: 3296 | loss: 5.729083
iter: 3297 | loss: 5.728735
iter: 3298 | loss: 5.728388
iter: 3299 | loss: 5.728040
iter: 3300 | loss: 5.727693
iter: 3301 | loss: 5.727345
iter: 3302 | loss: 5.726998
iter: 3303 | loss: 5.726650
iter: 3304 | loss: 5.726303
iter: 3305 | loss: 5.725955
iter: 3306 | loss: 5.725608
iter: 3307 | loss: 5.725260
iter: 3308 | loss: 5.724913
iter: 3309 | loss: 5.724565
iter: 3310 | loss: 5.724218
iter: 3311 | loss: 5.723870
iter: 3312 | loss: 5.723523
iter: 3313 | loss: 5.723175
iter: 3314 | loss: 5.722828
iter: 3315 | loss: 5.722480
iter: 3316 | loss: 5.722133
iter: 3317 | loss: 5.721785
iter: 3318 | loss: 5.721438
iter: 3319 | loss: 5.721090
iter: 3320 | loss: 5.720743
iter: 3321 | loss: 5.720396
iter: 3322 | loss: 5.720048
iter: 3323 | loss: 5.719701
iter: 3324 | loss: 5.719353
iter: 3325 | loss: 5.719006
iter: 3326 | loss: 5.718658
iter: 3327 | loss: 5.718311
iter: 3328 | loss: 5.717963
iter: 3329 | loss: 5.717616
iter: 3330 | loss: 5.717268
iter: 3331 | loss: 5.716921
iter: 3332 | loss: 5.716573
iter: 3333 | loss: 5.716226
iter: 3334 | loss: 5.715878
iter: 3335 | loss: 5.715531
iter: 3336 | loss: 5.715183
iter: 3337 | loss: 5.714836
iter: 3338 | loss: 5.714488
iter: 3339 | loss: 5.714141
iter: 3340 | loss: 5.713793
iter: 3341 | loss: 5.713446
iter: 3342 | loss: 5.713098
iter: 3343 | loss: 5.712751
iter: 3344 | loss: 5.712403
iter: 3345 | loss: 5.712056
iter: 3346 | loss: 5.711708
iter: 3347 | loss: 5.711361
iter: 3348 | loss: 5.711013
iter: 3349 | loss: 5.710666
iter: 3350 | loss: 5.710318
iter: 3351 | loss: 5.709971
iter: 3352 | loss: 5.709623
iter: 3353 | loss: 5.709276
iter: 3354 | loss: 5.708928
iter: 3355 | loss: 5.708581
iter: 3356 | loss: 5.708233
iter: 3357 | loss: 5.707886
iter: 3358 | loss: 5.707538
iter: 3359 | loss: 5.707191
iter: 3360 | loss: 5.706843
iter: 3361 | loss: 5.706496
iter: 3362 | loss: 5.706148
iter: 3363 | loss: 5.705801
iter: 3364 | loss: 5.705453
iter: 3365 | loss: 5.705106
iter: 3366 | loss: 5.704759
iter: 3367 | loss: 5.704411
iter: 3368 | loss: 5.704064
iter: 3369 | loss: 5.703716
iter: 3370 | loss: 5.703369
iter: 3371 | loss: 5.703021
iter: 3372 | loss: 5.702674
iter: 3373 | loss: 5.702326
iter: 3374 | loss: 5.701979
iter: 3375 | loss: 5.701631
iter: 3376 | loss: 5.701284
iter: 3377 | loss: 5.700936
iter: 3378 | loss: 5.700589
iter: 3379 | loss: 5.700241
iter: 3380 | loss: 5.699894
iter: 3381 | loss: 5.699546
iter: 3382 | loss: 5.699199
iter: 3383 | loss: 5.698851
iter: 3384 | loss: 5.698504
iter: 3385 | loss: 5.698156
iter: 3386 | loss: 5.697809
iter: 3387 | loss: 5.697461
iter: 3388 | loss: 5.697114
iter: 3389 | loss: 5.696766
iter: 3390 | loss: 5.696419
iter: 3391 | loss: 5.696071
iter: 3392 | loss: 5.695724
iter: 3393 | loss: 5.695376
iter: 3394 | loss: 5.695029
iter: 3395 | loss: 5.694681
iter: 3396 | loss: 5.694334
iter: 3397 | loss: 5.693986
iter: 3398 | loss: 5.693639
iter: 3399 | loss: 5.693291
iter: 3400 | loss: 5.692944
iter: 3401 | loss: 5.692596
iter: 3402 | loss: 5.692249
iter: 3403 | loss: 5.691901
iter: 3404 | loss: 5.691554
iter: 3405 | loss: 5.691206
iter: 3406 | loss: 5.690859
iter: 3407 | loss: 5.690511
iter: 3408 | loss: 5.690164
iter: 3409 | loss: 5.689817
iter: 3410 | loss: 5.689469
iter: 3411 | loss: 5.689122
iter: 3412 | loss: 5.688774
iter: 3413 | loss: 5.688427
iter: 3414 | loss: 5.688079
iter: 3415 | loss: 5.687732
iter: 3416 | loss: 5.687384
iter: 3417 | loss: 5.687037
iter: 3418 | loss: 5.686689
iter: 3419 | loss: 5.686342
iter: 3420 | loss: 5.685994
iter: 3421 | loss: 5.685647
iter: 3422 | loss: 5.685299
iter: 3423 | loss: 5.684952
iter: 3424 | loss: 5.684604
iter: 3425 | loss: 5.684257
iter: 3426 | loss: 5.683909
iter: 3427 | loss: 5.683562
iter: 3428 | loss: 5.683214
iter: 3429 | loss: 5.682867
iter: 3430 | loss: 5.682519
iter: 3431 | loss: 5.682172
iter: 3432 | loss: 5.681824
iter: 3433 | loss: 5.681477
iter: 3434 | loss: 5.681129
iter: 3435 | loss: 5.680782
iter: 3436 | loss: 5.680434
iter: 3437 | loss: 5.680087
iter: 3438 | loss: 5.679739
iter: 3439 | loss: 5.679392
iter: 3440 | loss: 5.679044
iter: 3441 | loss: 5.678697
iter: 3442 | loss: 5.678349
iter: 3443 | loss: 5.678002
iter: 3444 | loss: 5.677654
iter: 3445 | loss: 5.677307
iter: 3446 | loss: 5.676959
iter: 3447 | loss: 5.676612
iter: 3448 | loss: 5.676264
iter: 3449 | loss: 5.675917
iter: 3450 | loss: 5.675569
iter: 3451 | loss: 5.675222
iter: 3452 | loss: 5.674874
iter: 3453 | loss: 5.674527
iter: 3454 | loss: 5.674180
iter: 3455 | loss: 5.673832
iter: 3456 | loss: 5.673485
iter: 3457 | loss: 5.673137
iter: 3458 | loss: 5.672790
iter: 3459 | loss: 5.672442
iter: 3460 | loss: 5.672095
iter: 3461 | loss: 5.671747
iter: 3462 | loss: 5.671400
iter: 3463 | loss: 5.671052
iter: 3464 | loss: 5.670705
iter: 3465 | loss: 5.670357
iter: 3466 | loss: 5.670010
iter: 3467 | loss: 5.669662
iter: 3468 | loss: 5.669315
iter: 3469 | loss: 5.668967
iter: 3470 | loss: 5.668620
iter: 3471 | loss: 5.668272
iter: 3472 | loss: 5.667925
iter: 3473 | loss: 5.667577
iter: 3474 | loss: 5.667230
iter: 3475 | loss: 5.666882
iter: 3476 | loss: 5.666535
iter: 3477 | loss: 5.666187
iter: 3478 | loss: 5.665840
iter: 3479 | loss: 5.665492
iter: 3480 | loss: 5.665145
iter: 3481 | loss: 5.664797
iter: 3482 | loss: 5.664450
iter: 3483 | loss: 5.664102
iter: 3484 | loss: 5.663755
iter: 3485 | loss: 5.663407
iter: 3486 | loss: 5.663060
iter: 3487 | loss: 5.662712
iter: 3488 | loss: 5.662365
iter: 3489 | loss: 5.662017
iter: 3490 | loss: 5.661670
iter: 3491 | loss: 5.661322
iter: 3492 | loss: 5.660975
iter: 3493 | loss: 5.660627
iter: 3494 | loss: 5.660280
iter: 3495 | loss: 5.659932
iter: 3496 | loss: 5.659585
iter: 3497 | loss: 5.659238
iter: 3498 | loss: 5.658890
iter: 3499 | loss: 5.658543
iter: 3500 | loss: 5.658195
iter: 3501 | loss: 5.657848
iter: 3502 | loss: 5.657500
iter: 3503 | loss: 5.657153
iter: 3504 | loss: 5.656805
iter: 3505 | loss: 5.656458
iter: 3506 | loss: 5.656110
iter: 3507 | loss: 5.655763
iter: 3508 | loss: 5.655415
iter: 3509 | loss: 5.655068
iter: 3510 | loss: 5.654720
iter: 3511 | loss: 5.654373
iter: 3512 | loss: 5.654025
iter: 3513 | loss: 5.653678
iter: 3514 | loss: 5.653330
iter: 3515 | loss: 5.652983
iter: 3516 | loss: 5.652635
iter: 3517 | loss: 5.652288
iter: 3518 | loss: 5.651940
iter: 3519 | loss: 5.651593
iter: 3520 | loss: 5.651245
iter: 3521 | loss: 5.650898
iter: 3522 | loss: 5.650550
iter: 3523 | loss: 5.650203
iter: 3524 | loss: 5.649855
iter: 3525 | loss: 5.649508
iter: 3526 | loss: 5.649160
iter: 3527 | loss: 5.648813
iter: 3528 | loss: 5.648465
iter: 3529 | loss: 5.648118
iter: 3530 | loss: 5.647770
iter: 3531 | loss: 5.647423
iter: 3532 | loss: 5.647075
iter: 3533 | loss: 5.646728
iter: 3534 | loss: 5.646380
iter: 3535 | loss: 5.646033
iter: 3536 | loss: 5.645685
iter: 3537 | loss: 5.645338
iter: 3538 | loss: 5.644990
iter: 3539 | loss: 5.644643
iter: 3540 | loss: 5.644296
iter: 3541 | loss: 5.643948
iter: 3542 | loss: 5.643601
iter: 3543 | loss: 5.643253
iter: 3544 | loss: 5.642906
iter: 3545 | loss: 5.642558
iter: 3546 | loss: 5.642211
iter: 3547 | loss: 5.641863
iter: 3548 | loss: 5.641516
iter: 3549 | loss: 5.641168
iter: 3550 | loss: 5.640821
iter: 3551 | loss: 5.640473
iter: 3552 | loss: 5.640126
iter: 3553 | loss: 5.639778
iter: 3554 | loss: 5.639431
iter: 3555 | loss: 5.639083
iter: 3556 | loss: 5.638736
iter: 3557 | loss: 5.638388
iter: 3558 | loss: 5.638041
iter: 3559 | loss: 5.637693
iter: 3560 | loss: 5.637346
iter: 3561 | loss: 5.636998
iter: 3562 | loss: 5.636651
iter: 3563 | loss: 5.636303
iter: 3564 | loss: 5.635956
iter: 3565 | loss: 5.635608
iter: 3566 | loss: 5.635261
iter: 3567 | loss: 5.634913
iter: 3568 | loss: 5.634566
iter: 3569 | loss: 5.634218
iter: 3570 | loss: 5.633871
iter: 3571 | loss: 5.633523
iter: 3572 | loss: 5.633176
iter: 3573 | loss: 5.632828
iter: 3574 | loss: 5.632481
iter: 3575 | loss: 5.632133
iter: 3576 | loss: 5.631786
iter: 3577 | loss: 5.631438
iter: 3578 | loss: 5.631091
iter: 3579 | loss: 5.630743
iter: 3580 | loss: 5.630396
iter: 3581 | loss: 5.630048
iter: 3582 | loss: 5.629701
iter: 3583 | loss: 5.629353
iter: 3584 | loss: 5.629006
iter: 3585 | loss: 5.628659
iter: 3586 | loss: 5.628311
iter: 3587 | loss: 5.627964
iter: 3588 | loss: 5.627616
iter: 3589 | loss: 5.627269
iter: 3590 | loss: 5.626921
iter: 3591 | loss: 5.626574
iter: 3592 | loss: 5.626226
iter: 3593 | loss: 5.625879
iter: 3594 | loss: 5.625531
iter: 3595 | loss: 5.625184
iter: 3596 | loss: 5.624836
iter: 3597 | loss: 5.624489
iter: 3598 | loss: 5.624141
iter: 3599 | loss: 5.623794
iter: 3600 | loss: 5.623446
iter: 3601 | loss: 5.623099
iter: 3602 | loss: 5.622751
iter: 3603 | loss: 5.622404
iter: 3604 | loss: 5.622056
iter: 3605 | loss: 5.621709
iter: 3606 | loss: 5.621361
iter: 3607 | loss: 5.621014
iter: 3608 | loss: 5.620666
iter: 3609 | loss: 5.620319
iter
- ...TRUNCATED BY DUNE...
- (cd _build/default/examples/opt && ./single.exe)
-
step: 0 | loss: 5.963389709
step: 10 | loss: 5.958094347
step: 20 | loss: 5.952214037
step: 30 | loss: 5.946337725
step: 40 | loss: 5.940465670
step: 50 | loss: 5.934598001
step: 60 | loss: 5.928734758
step: 70 | loss: 5.922875933
step: 80 | loss: 5.917021492
step: 90 | loss: 5.911171389
step: 100 | loss: 5.905325573
step: 110 | loss: 5.899483992
step: 120 | loss: 5.893646592
step: 130 | loss: 5.887813317
step: 140 | loss: 5.881984114
step: 150 | loss: 5.876158928
step: 160 | loss: 5.870337703
step: 170 | loss: 5.864520383
step: 180 | loss: 5.858706915
step: 190 | loss: 5.852897243
step: 200 | loss: 5.847091313
step: 210 | loss: 5.841289068
step: 220 | loss: 5.835490456
step: 230 | loss: 5.829695421
step: 240 | loss: 5.823903910
step: 250 | loss: 5.818115869
step: 260 | loss: 5.812331244
step: 270 | loss: 5.806549984
step: 280 | loss: 5.800772036
step: 290 | loss: 5.794997348
step: 300 | loss: 5.789225870
step: 310 | loss: 5.783457552
step: 320 | loss: 5.777692344
step: 330 | loss: 5.771930197
step: 340 | loss: 5.766171065
step: 350 | loss: 5.760414899
step: 360 | loss: 5.754661655
step: 370 | loss: 5.748911286
step: 380 | loss: 5.743163751
step: 390 | loss: 5.737419005
step: 400 | loss: 5.731677007
step: 410 | loss: 5.725937717
step: 420 | loss: 5.720201096
step: 430 | loss: 5.714467104
step: 440 | loss: 5.708735706
step: 450 | loss: 5.703006865
step: 460 | loss: 5.697280547
step: 470 | loss: 5.691556718
step: 480 | loss: 5.685835347
step: 490 | loss: 5.680116403
step: 500 | loss: 5.674399856
step: 510 | loss: 5.668685677
step: 520 | loss: 5.662973840
step: 530 | loss: 5.657264318
step: 540 | loss: 5.651557087
step: 550 | loss: 5.645852122
step: 560 | loss: 5.640149402
step: 570 | loss: 5.634448904
step: 580 | loss: 5.628750608
step: 590 | loss: 5.623054495
step: 600 | loss: 5.617360546
step: 610 | loss: 5.611668743
step: 620 | loss: 5.605979070
step: 630 | loss: 5.600291511
step: 640 | loss: 5.594606052
step: 650 | loss: 5.588922677
step: 660 | loss: 5.583241374
step: 670 | loss: 5.577562130
step: 680 | loss: 5.571884933
step: 690 | loss: 5.566209771
step: 700 | loss: 5.560536634
step: 710 | loss: 5.554865511
step: 720 | loss: 5.549196394
step: 730 | loss: 5.543529272
step: 740 | loss: 5.537864136
step: 750 | loss: 5.532200980
step: 760 | loss: 5.526539794
step: 770 | loss: 5.520880571
step: 780 | loss: 5.515223304
step: 790 | loss: 5.509567986
step: 800 | loss: 5.503914610
step: 810 | loss: 5.498263170
step: 820 | loss: 5.492613660
step: 830 | loss: 5.486966073
step: 840 | loss: 5.481320404
step: 850 | loss: 5.475676648
step: 860 | loss: 5.470034799
step: 870 | loss: 5.464394851
step: 880 | loss: 5.458756799
step: 890 | loss: 5.453120639
step: 900 | loss: 5.447486365
step: 910 | loss: 5.441853972
step: 920 | loss: 5.436223457
step: 930 | loss: 5.430594813
step: 940 | loss: 5.424968036
step: 950 | loss: 5.419343121
step: 960 | loss: 5.413720065
step: 970 | loss: 5.408098861
step: 980 | loss: 5.402479506
step: 990 | loss: 5.396861996
step: 1000 | loss: 5.391246325
step: 1010 | loss: 5.385632489
step: 1020 | loss: 5.380020484
step: 1030 | loss: 5.374410305
step: 1040 | loss: 5.368801947
step: 1050 | loss: 5.363195406
step: 1060 | loss: 5.357590678
step: 1070 | loss: 5.351987758
step: 1080 | loss: 5.346386642
step: 1090 | loss: 5.340787325
step: 1100 | loss: 5.335189802
step: 1110 | loss: 5.329594069
step: 1120 | loss: 5.324000121
step: 1130 | loss: 5.318407954
step: 1140 | loss: 5.312817563
step: 1150 | loss: 5.307228944
step: 1160 | loss: 5.301642091
step: 1170 | loss: 5.296057001
step: 1180 | loss: 5.290473668
step: 1190 | loss: 5.284892089
step: 1200 | loss: 5.279312257
step: 1210 | loss: 5.273734169
step: 1220 | loss: 5.268157819
step: 1230 | loss: 5.262583204
step: 1240 | loss: 5.257010317
step: 1250 | loss: 5.251439155
step: 1260 | loss: 5.245869712
step: 1270 | loss: 5.240301984
step: 1280 | loss: 5.234735966
step: 1290 | loss: 5.229171652
step: 1300 | loss: 5.223609039
step: 1310 | loss: 5.218048120
step: 1320 | loss: 5.212488891
step: 1330 | loss: 5.206931348
step: 1340 | loss: 5.201375484
step: 1350 | loss: 5.195821296
step: 1360 | loss: 5.190268777
step: 1370 | loss: 5.184717923
step: 1380 | loss: 5.179168729
step: 1390 | loss: 5.173621190
step: 1400 | loss: 5.168075300
step: 1410 | loss: 5.162531054
step: 1420 | loss: 5.156988448
step: 1430 | loss: 5.151447475
step: 1440 | loss: 5.145908132
step: 1450 | loss: 5.140370411
step: 1460 | loss: 5.134834309
step: 1470 | loss: 5.129299820
step: 1480 | loss: 5.123766939
step: 1490 | loss: 5.118235659
step: 1500 | loss: 5.112705977
step: 1510 | loss: 5.107177886
step: 1520 | loss: 5.101651381
step: 1530 | loss: 5.096126457
step: 1540 | loss: 5.090603108
step: 1550 | loss: 5.085081330
step: 1560 | loss: 5.079561115
step: 1570 | loss: 5.074042459
step: 1580 | loss: 5.068525357
step: 1590 | loss: 5.063009802
step: 1600 | loss: 5.057495789
step: 1610 | loss: 5.051983314
step: 1620 | loss: 5.046472369
step: 1630 | loss: 5.040962949
step: 1640 | loss: 5.035455050
step: 1650 | loss: 5.029948664
step: 1660 | loss: 5.024443787
step: 1670 | loss: 5.018940413
step: 1680 | loss: 5.013438536
step: 1690 | loss: 5.007938150
step: 1700 | loss: 5.002439250
step: 1710 | loss: 4.996941830
step: 1720 | loss: 4.991445884
step: 1730 | loss: 4.985951407
step: 1740 | loss: 4.980458391
step: 1750 | loss: 4.974966833
step: 1760 | loss: 4.969476726
step: 1770 | loss: 4.963988063
step: 1780 | loss: 4.958500840
step: 1790 | loss: 4.953015051
step: 1800 | loss: 4.947530688
step: 1810 | loss: 4.942047748
step: 1820 | loss: 4.936566223
step: 1830 | loss: 4.931086108
step: 1840 | loss: 4.925607397
step: 1850 | loss: 4.920130084
step: 1860 | loss: 4.914654163
step: 1870 | loss: 4.909179629
step: 1880 | loss: 4.903706474
step: 1890 | loss: 4.898234694
step: 1900 | loss: 4.892764282
step: 1910 | loss: 4.887295233
step: 1920 | loss: 4.881827540
step: 1930 | loss: 4.876361197
step: 1940 | loss: 4.870896199
step: 1950 | loss: 4.865432539
step: 1960 | loss: 4.859970212
step: 1970 | loss: 4.854509212
step: 1980 | loss: 4.849049532
step: 1990 | loss: 4.843591167
step: 2000 | loss: 4.838134110
step: 2010 | loss: 4.832678356
step: 2020 | loss: 4.827223899
step: 2030 | loss: 4.821770732
step: 2040 | loss: 4.816318851
step: 2050 | loss: 4.810868248
step: 2060 | loss: 4.805418918
step: 2070 | loss: 4.799970855
step: 2080 | loss: 4.794524054
step: 2090 | loss: 4.789078507
step: 2100 | loss: 4.783634210
step: 2110 | loss: 4.778191156
step: 2120 | loss: 4.772749339
step: 2130 | loss: 4.767308755
step: 2140 | loss: 4.761869396
step: 2150 | loss: 4.756431257
step: 2160 | loss: 4.750994332
step: 2170 | loss: 4.745558615
step: 2180 | loss: 4.740124101
step: 2190 | loss: 4.734690784
step: 2200 | loss: 4.729258658
step: 2210 | loss: 4.723827717
step: 2220 | loss: 4.718397956
step: 2230 | loss: 4.712969369
step: 2240 | loss: 4.707541950
step: 2250 | loss: 4.702115694
step: 2260 | loss: 4.696690594
step: 2270 | loss: 4.691266647
step: 2280 | loss: 4.685843845
step: 2290 | loss: 4.680422184
step: 2300 | loss: 4.675001658
step: 2310 | loss: 4.669582261
step: 2320 | loss: 4.664163989
step: 2330 | loss: 4.658746835
step: 2340 | loss: 4.653330795
step: 2350 | loss: 4.647915863
step: 2360 | loss: 4.642502033
step: 2370 | loss: 4.637089301
step: 2380 | loss: 4.631677662
step: 2390 | loss: 4.626267110
step: 2400 | loss: 4.620857640
step: 2410 | loss: 4.615449247
step: 2420 | loss: 4.610041926
step: 2430 | loss: 4.604635673
step: 2440 | loss: 4.599230481
step: 2450 | loss: 4.593826347
step: 2460 | loss: 4.588423265
step: 2470 | loss: 4.583021231
step: 2480 | loss: 4.577620240
step: 2490 | loss: 4.572220287
step: 2500 | loss: 4.566821368
step: 2510 | loss: 4.561423477
step: 2520 | loss: 4.556026612
step: 2530 | loss: 4.550630766
step: 2540 | loss: 4.545235936
step: 2550 | loss: 4.539842117
step: 2560 | loss: 4.534449305
step: 2570 | loss: 4.529057496
step: 2580 | loss: 4.523666686
step: 2590 | loss: 4.518276870
step: 2600 | loss: 4.512888045
step: 2610 | loss: 4.507500206
step: 2620 | loss: 4.502113349
step: 2630 | loss: 4.496727471
step: 2640 | loss: 4.491342568
step: 2650 | loss: 4.485958636
step: 2660 | loss: 4.480575672
step: 2670 | loss: 4.475193671
step: 2680 | loss: 4.469812631
step: 2690 | loss: 4.464432548
step: 2700 | loss: 4.459053418
step: 2710 | loss: 4.453675238
step: 2720 | loss: 4.448298006
step: 2730 | loss: 4.442921717
step: 2740 | loss: 4.437546368
step: 2750 | loss: 4.432171957
step: 2760 | loss: 4.426798481
step: 2770 | loss: 4.421425937
step: 2780 | loss: 4.416054321
step: 2790 | loss: 4.410683632
step: 2800 | loss: 4.405313866
step: 2810 | loss: 4.399945021
step: 2820 | loss: 4.394577095
step: 2830 | loss: 4.389210084
step: 2840 | loss: 4.383843988
step: 2850 | loss: 4.378478802
step: 2860 | loss: 4.373114526
step: 2870 | loss: 4.367751157
step: 2880 | loss: 4.362388692
step: 2890 | loss: 4.357027131
step: 2900 | loss: 4.351666470
step: 2910 | loss: 4.346306709
step: 2920 | loss: 4.340947845
step: 2930 | loss: 4.335589877
step: 2940 | loss: 4.330232803
step: 2950 | loss: 4.324876622
step: 2960 | loss: 4.319521332
step: 2970 | loss: 4.314166932
step: 2980 | loss: 4.308813421
step: 2990 | loss: 4.303460797
step: 3000 | loss: 4.298109059
step: 3010 | loss: 4.292758206
step: 3020 | loss: 4.287408238
step: 3030 | loss: 4.282059153
step: 3040 | loss: 4.276710950
step: 3050 | loss: 4.271363629
step: 3060 | loss: 4.266017189
step: 3070 | loss: 4.260671629
step: 3080 | loss: 4.255326949
step: 3090 | loss: 4.249983149
step: 3100 | loss: 4.244640228
step: 3110 | loss: 4.239298185
step: 3120 | loss: 4.233957021
step: 3130 | loss: 4.228616735
step: 3140 | loss: 4.223277327
step: 3150 | loss: 4.217938797
step: 3160 | loss: 4.212601145
step: 3170 | loss: 4.207264372
step: 3180 | loss: 4.201928477
step: 3190 | loss: 4.196593461
step: 3200 | loss: 4.191259324
step: 3210 | loss: 4.185926066
step: 3220 | loss: 4.180593688
step: 3230 | loss: 4.175262191
step: 3240 | loss: 4.169931574
step: 3250 | loss: 4.164601839
step: 3260 | loss: 4.159272986
step: 3270 | loss: 4.153945017
step: 3280 | loss: 4.148617931
step: 3290 | loss: 4.143291729
step: 3300 | loss: 4.137966414
step: 3310 | loss: 4.132641985
step: 3320 | loss: 4.127318444
step: 3330 | loss: 4.121995792
step: 3340 | loss: 4.116674029
step: 3350 | loss: 4.111353159
step: 3360 | loss: 4.106033180
step: 3370 | loss: 4.100714096
step: 3380 | loss: 4.095395907
step: 3390 | loss: 4.090078615
step: 3400 | loss: 4.084762221
step: 3410 | loss: 4.079446726
step: 3420 | loss: 4.074132134
step: 3430 | loss: 4.068818444
step: 3440 | loss: 4.063505659
step: 3450 | loss: 4.058193781
step: 3460 | loss: 4.052882812
step: 3470 | loss: 4.047572752
step: 3480 | loss: 4.042263605
step: 3490 | loss: 4.036955371
step: 3500 | loss: 4.031648054
step: 3510 | loss: 4.026341655
step: 3520 | loss: 4.021036176
step: 3530 | loss: 4.015731620
step: 3540 | loss: 4.010427987
step: 3550 | loss: 4.005125282
step: 3560 | loss: 3.999823505
step: 3570 | loss: 3.994522660
step: 3580 | loss: 3.989222748
step: 3590 | loss: 3.983923772
step: 3600 | loss: 3.978625734
step: 3610 | loss: 3.973328637
step: 3620 | loss: 3.968032483
step: 3630 | loss: 3.962737275
step: 3640 | loss: 3.957443015
step: 3650 | loss: 3.952149706
step: 3660 | loss: 3.946857350
step: 3670 | loss: 3.941565950
step: 3680 | loss: 3.936275509
step: 3690 | loss: 3.930986029
step: 3700 | loss: 3.925697514
step: 3710 | loss: 3.920409965
step: 3720 | loss: 3.915123387
step: 3730 | loss: 3.909837780
step: 3740 | loss: 3.904553150
step: 3750 | loss: 3.899269497
step: 3760 | loss: 3.893986826
step: 3770 | loss: 3.888705139
step: 3780 | loss: 3.883424439
step: 3790 | loss: 3.878144730
step: 3800 | loss: 3.872866013
step: 3810 | loss: 3.867588293
step: 3820 | loss: 3.862311573
step: 3830 | loss: 3.857035854
step: 3840 | loss: 3.851761142
step: 3850 | loss: 3.846487438
step: 3860 | loss: 3.841214746
step: 3870 | loss: 3.835943070
step: 3880 | loss: 3.830672412
step: 3890 | loss: 3.825402775
step: 3900 | loss: 3.820134164
step: 3910 | loss: 3.814866581
step: 3920 | loss: 3.809600029
step: 3930 | loss: 3.804334513
step: 3940 | loss: 3.799070035
step: 3950 | loss: 3.793806599
step: 3960 | loss: 3.788544208
step: 3970 | loss: 3.783282865
step: 3980 | loss: 3.778022575
step: 3990 | loss: 3.772763341
step: 4000 | loss: 3.767505166
step: 4010 | loss: 3.762248053
step: 4020 | loss: 3.756992007
step: 4030 | loss: 3.751737031
step: 4040 | loss: 3.746483128
step: 4050 | loss: 3.741230303
step: 4060 | loss: 3.735978558
step: 4070 | loss: 3.730727898
step: 4080 | loss: 3.725478326
step: 4090 | loss: 3.720229846
step: 4100 | loss: 3.714982461
step: 4110 | loss: 3.709736176
step: 4120 | loss: 3.704490994
step: 4130 | loss: 3.699246919
step: 4140 | loss: 3.694003955
step: 4150 | loss: 3.688762106
step: 4160 | loss: 3.683521375
step: 4170 | loss: 3.678281766
step: 4180 | loss: 3.673043284
step: 4190 | loss: 3.667805932
step: 4200 | loss: 3.662569714
step: 4210 | loss: 3.657334635
step: 4220 | loss: 3.652100698
step: 4230 | loss: 3.646867907
step: 4240 | loss: 3.641636266
step: 4250 | loss: 3.636405780
step: 4260 | loss: 3.631176451
step: 4270 | loss: 3.625948286
step: 4280 | loss: 3.620721287
step: 4290 | loss: 3.615495459
step: 4300 | loss: 3.610270806
step: 4310 | loss: 3.605047332
step: 4320 | loss: 3.599825041
step: 4330 | loss: 3.594603938
step: 4340 | loss: 3.589384027
step: 4350 | loss: 3.584165311
step: 4360 | loss: 3.578947797
step: 4370 | loss: 3.573731487
step: 4380 | loss: 3.568516386
step: 4390 | loss: 3.563302498
step: 4400 | loss: 3.558089828
step: 4410 | loss: 3.552878381
step: 4420 | loss: 3.547668160
step: 4430 | loss: 3.542459170
step: 4440 | loss: 3.537251416
step: 4450 | loss: 3.532044902
step: 4460 | loss: 3.526839633
step: 4470 | loss: 3.521635613
step: 4480 | loss: 3.516432846
step: 4490 | loss: 3.511231338
step: 4500 | loss: 3.506031093
step: 4510 | loss: 3.500832115
step: 4520 | loss: 3.495634410
step: 4530 | loss: 3.490437981
step: 4540 | loss: 3.485242834
step: 4550 | loss: 3.480048973
step: 4560 | loss: 3.474856404
step: 4570 | loss: 3.469665130
step: 4580 | loss: 3.464475157
step: 4590 | loss: 3.459286490
step: 4600 | loss: 3.454099132
step: 4610 | loss: 3.448913090
step: 4620 | loss: 3.443728368
step: 4630 | loss: 3.438544971
step: 4640 | loss: 3.433362904
step: 4650 | loss: 3.428182172
step: 4660 | loss: 3.423002780
step: 4670 | loss: 3.417824733
step: 4680 | loss: 3.412648035
step: 4690 | loss: 3.407472693
step: 4700 | loss: 3.402298711
step: 4710 | loss: 3.397126094
step: 4720 | loss: 3.391954847
step: 4730 | loss: 3.386784976
step: 4740 | loss: 3.381616486
step: 4750 | loss: 3.376449382
step: 4760 | loss: 3.371283668
step: 4770 | loss: 3.366119352
step: 4780 | loss: 3.360956437
step: 4790 | loss: 3.355794929
step: 4800 | loss: 3.350634833
step: 4810 | loss: 3.345476155
step: 4820 | loss: 3.340318900
step: 4830 | loss: 3.335163074
step: 4840 | loss: 3.330008682
step: 4850 | loss: 3.324855730
step: 4860 | loss: 3.319704222
step: 4870 | loss: 3.314554165
step: 4880 | loss: 3.309405564
step: 4890 | loss: 3.304258425
step: 4900 | loss: 3.299112753
step: 4910 | loss: 3.293968554
step: 4920 | loss: 3.288825833
step: 4930 | loss: 3.283684597
step: 4940 | loss: 3.278544851
step: 4950 | loss: 3.273406600
step: 4960 | loss: 3.268269851
step: 4970 | loss: 3.263134610
step: 4980 | loss: 3.258000881
step: 4990 | loss: 3.252868671
step: 5000 | loss: 3.247737987
step: 5010 | loss: 3.242608833
step: 5020 | loss: 3.237481215
step: 5030 | loss: 3.232355141
step: 5040 | loss: 3.227230615
step: 5050 | loss: 3.222107643
step: 5060 | loss: 3.216986233
step: 5070 | loss: 3.211866389
step: 5080 | loss: 3.206748119
step: 5090 | loss: 3.201631427
step: 5100 | loss: 3.196516321
step: 5110 | loss: 3.191402806
step: 5120 | loss: 3.186290889
step: 5130 | loss: 3.181180576
step: 5140 | loss: 3.176071873
step: 5150 | loss: 3.170964787
step: 5160 | loss: 3.165859323
step: 5170 | loss: 3.160755489
step: 5180 | loss: 3.155653291
step: 5190 | loss: 3.150552735
step: 5200 | loss: 3.145453827
step: 5210 | loss: 3.140356575
step: 5220 | loss: 3.135260984
step: 5230 | loss: 3.130167061
step: 5240 | loss: 3.125074813
step: 5250 | loss: 3.119984247
step: 5260 | loss: 3.114895369
step: 5270 | loss: 3.109808185
step: 5280 | loss: 3.104722703
step: 5290 | loss: 3.099638930
step: 5300 | loss: 3.094556871
step: 5310 | loss: 3.089476534
step: 5320 | loss: 3.084397926
step: 5330 | loss: 3.079321053
step: 5340 | loss: 3.074245923
step: 5350 | loss: 3.069172543
step: 5360 | loss: 3.064100918
step: 5370 | loss: 3.059031057
step: 5380 | loss: 3.053962967
step: 5390 | loss: 3.048896654
step: 5400 | loss: 3.043832126
step: 5410 | loss: 3.038769389
step: 5420 | loss: 3.033708451
step: 5430 | loss: 3.028649320
step: 5440 | loss: 3.023592002
step: 5450 | loss: 3.018536504
step: 5460 | loss: 3.013482835
step: 5470 | loss: 3.008431001
step: 5480 | loss: 3.003381009
step: 5490 | loss: 2.998332867
step: 5500 | loss: 2.993286583
step: 5510 | loss: 2.988242164
step: 5520 | loss: 2.983199618
step: 5530 | loss: 2.978158951
step: 5540 | loss: 2.973120173
step: 5550 | loss: 2.968083289
step: 5560 | loss: 2.963048308
step: 5570 | loss: 2.958015238
step: 5580 | loss: 2.952984086
step: 5590 | loss: 2.947954860
step: 5600 | loss: 2.942927568
step: 5610 | loss: 2.937902218
step: 5620 | loss: 2.932878817
step: 5630 | loss: 2.927857374
step: 5640 | loss: 2.922837895
step: 5650 | loss: 2.917820391
step: 5660 | loss: 2.912804867
step: 5670 | loss: 2.907791333
step: 5680 | loss: 2.902779796
step: 5690 | loss: 2.897770265
step: 5700 | loss: 2.892762747
step: 5710 | loss: 2.887757252
step: 5720 | loss: 2.882753786
step: 5730 | loss: 2.877752359
step: 5740 | loss: 2.872752978
step: 5750 | loss: 2.867755652
step: 5760 | loss: 2.862760390
step: 5770 | loss: 2.857767199
step: 5780 | loss: 2.852776089
step: 5790 | loss: 2.847787067
step: 5800 | loss: 2.842800142
step: 5810 | loss: 2.837815324
step: 5820 | loss: 2.832832619
step: 5830 | loss: 2.827852037
step: 5840 | loss: 2.822873587
step: 5850 | loss: 2.817897278
step: 5860 | loss: 2.812923117
step: 5870 | loss: 2.807951115
step: 5880 | loss: 2.802981279
step: 5890 | loss: 2.798013618
step: 5900 | loss: 2.793048142
step: 5910 | loss: 2.788084859
step: 5920 | loss: 2.783123779
step: 5930 | loss: 2.778164910
step: 5940 | loss: 2.773208262
step: 5950 | loss: 2.768253843
step: 5960 | loss: 2.763301662
step: 5970 | loss: 2.758351730
step: 5980 | loss: 2.753404054
step: 5990 | loss: 2.748458645
step: 6000 | loss: 2.743515511
step: 6010 | loss: 2.738574662
step: 6020 | loss: 2.733636107
step: 6030 | loss: 2.728699856
step: 6040 | loss: 2.723765918
step: 6050 | loss: 2.718834303
step: 6060 | loss: 2.713905020
step: 6070 | loss: 2.708978078
step: 6080 | loss: 2.704053488
step: 6090 | loss: 2.699131259
step: 6100 | loss: 2.694211400
step: 6110 | loss: 2.689293921
step: 6120 | loss: 2.684378833
step: 6130 | loss: 2.679466144
step: 6140 | loss: 2.674555865
step: 6150 | loss: 2.669648006
step: 6160 | loss: 2.664742577
step: 6170 | loss: 2.659839586
step: 6180 | loss: 2.654939046
step: 6190 | loss: 2.650040965
step: 6200 | loss: 2.645145353
step: 6210 | loss: 2.640252221
step: 6220 | loss: 2.635361580
step: 6230 | loss: 2.630473438
step: 6240 | loss: 2.625587806
step: 6250 | loss: 2.620704696
step: 6260 | loss: 2.615824116
step: 6270 | loss: 2.610946078
step: 6280 | loss: 2.606070591
step: 6290 | loss: 2.601197667
step: 6300 | loss: 2.596327315
step: 6310 | loss: 2.591459547
step: 6320 | loss: 2.586594372
step: 6330 | loss: 2.581731802
step: 6340 | loss: 2.576871847
step: 6350 | loss: 2.572014518
step: 6360 | loss: 2.567159825
step: 6370 | loss: 2.562307779
step: 6380 | loss: 2.557458392
step: 6390 | loss: 2.552611673
step: 6400 | loss: 2.547767634
step: 6410 | loss: 2.542926286
step: 6420 | loss: 2.538087640
step: 6430 | loss: 2.533251707
step: 6440 | loss: 2.528418497
step: 6450 | loss: 2.523588022
step: 6460 | loss: 2.518760293
step: 6470 | loss: 2.513935322
step: 6480 | loss: 2.509113119
step: 6490 | loss: 2.504293695
step: 6500 | loss: 2.499477063
step: 6510 | loss: 2.494663233
step: 6520 | loss: 2.489852216
step: 6530 | loss: 2.485044025
step: 6540 | loss: 2.480238670
step: 6550 | loss: 2.475436163
step: 6560 | loss: 2.470636516
step: 6570 | loss: 2.465839740
step: 6580 | loss: 2.461045847
step: 6590 | loss: 2.456254848
step: 6600 | loss: 2.451466755
step: 6610 | loss: 2.446681580
step: 6620 | loss: 2.441899334
step: 6630 | loss: 2.437120030
step: 6640 | loss: 2.432343679
step: 6650 | loss: 2.427570293
step: 6660 | loss: 2.422799884
step: 6670 | loss: 2.418032465
step: 6680 | loss: 2.413268046
step: 6690 | loss: 2.408506640
step: 6700 | loss: 2.403748259
step: 6710 | loss: 2.398992915
step: 6720 | loss: 2.394240620
step: 6730 | loss: 2.389491387
step: 6740 | loss: 2.384745228
step: 6750 | loss: 2.380002154
step: 6760 | loss: 2.375262179
step: 6770 | loss: 2.370525314
step: 6780 | loss: 2.365791572
step: 6790 | loss: 2.361060965
step: 6800 | loss: 2.356333506
step: 6810 | loss: 2.351609207
step: 6820 | loss: 2.346888080
step: 6830 | loss: 2.342170139
step: 6840 | loss: 2.337455395
step: 6850 | loss: 2.332743861
step: 6860 | loss: 2.328035551
step: 6870 | loss: 2.323330475
step: 6880 | loss: 2.318628649
step: 6890 | loss: 2.313930082
step: 6900 | loss: 2.309234790
step: 6910 | loss: 2.304542784
step: 6920 | loss: 2.299854078
step: 6930 | loss: 2.295168683
step: 6940 | loss: 2.290486613
step: 6950 | loss: 2.285807882
step: 6960 | loss: 2.281132501
step: 6970 | loss: 2.276460483
step: 6980 | loss: 2.271791843
step: 6990 | loss: 2.267126592
step: 7000 | loss: 2.262464744
step: 7010 | loss: 2.257806312
step: 7020 | loss: 2.253151309
step: 7030 | loss: 2.248499747
step: 7040 | loss: 2.243851641
step: 7050 | loss: 2.239207003
step: 7060 | loss: 2.234565847
step: 7070 | loss: 2.229928186
step: 7080 | loss: 2.225294032
step: 7090 | loss: 2.220663400
step: 7100 | loss: 2.216036302
step: 7110 | loss: 2.211412752
step: 7120 | loss: 2.206792763
step: 7130 | loss: 2.202176349
step: 7140 | loss: 2.197563522
step: 7150 | loss: 2.192954297
step: 7160 | loss: 2.188348686
step: 7170 | loss: 2.183746703
step: 7180 | loss: 2.179148361
step: 7190 | loss: 2.174553675
step: 7200 | loss: 2.169962656
step: 7210 | loss: 2.165375320
step: 7220 | loss: 2.160791679
step: 7230 | loss: 2.156211746
step: 7240 | loss: 2.151635536
step: 7250 | loss: 2.147063062
step: 7260 | loss: 2.142494337
step: 7270 | loss: 2.137929375
step: 7280 | loss: 2.133368189
step: 7290 | loss: 2.128810793
step: 7300 | loss: 2.124257201
step: 7310 | loss: 2.119707426
step: 7320 | loss: 2.115161481
step: 7330 | loss: 2.110619381
step: 7340 | loss: 2.106081139
step: 7350 | loss: 2.101546768
step: 7360 | loss: 2.097016282
step: 7370 | loss: 2.092489695
step: 7380 | loss: 2.087967020
step: 7390 | loss: 2.083448270
step: 7400 | loss: 2.078933460
step: 7410 | loss: 2.074422603
step: 7420 | loss: 2.069915712
step: 7430 | loss: 2.065412802
step: 7440 | loss: 2.060913884
step: 7450 | loss: 2.056418974
step: 7460 | loss: 2.051928085
step: 7470 | loss: 2.047441230
step: 7480 | loss: 2.042958423
step: 7490 | loss: 2.038479676
step: 7500 | loss: 2.034005005
step: 7510 | loss: 2.029534421
step: 7520 | loss: 2.025067939
step: 7530 | loss: 2.020605573
step: 7540 | loss: 2.016147334
step: 7550 | loss: 2.011693238
step: 7560 | loss: 2.007243296
step: 7570 | loss: 2.002797523
step: 7580 | loss: 1.998355932
step: 7590 | loss: 1.993918536
step: 7600 | loss: 1.989485349
step: 7610 | loss: 1.985056383
step: 7620 | loss: 1.980631652
step: 7630 | loss: 1.976211169
step: 7640 | loss: 1.971794947
step: 7650 | loss: 1.967382999
step: 7660 | loss: 1.962975339
step: 7670 | loss: 1.958571979
step: 7680 | loss: 1.954172932
step: 7690 | loss: 1.949778212
step: 7700 | loss: 1.945387830
step: 7710 | loss: 1.941001801
step: 7720 | loss: 1.936620136
step: 7730 | loss: 1.932242848
step: 7740 | loss: 1.927869951
step: 7750 | loss: 1.923501457
step: 7760 | loss: 1.919137378
step: 7770 | loss: 1.914777728
step: 7780 | loss: 1.910422517
step: 7790 | loss: 1.906071760
step: 7800 | loss: 1.901725467
step: 7810 | loss: 1.897383652
step: 7820 | loss: 1.893046327
step: 7830 | loss: 1.888713504
step: 7840 | loss: 1.884385194
step: 7850 | loss: 1.880061411
step: 7860 | loss: 1.875742165
step: 7870 | loss: 1.871427469
step: 7880 | loss: 1.867117335
step: 7890 | loss: 1.862811774
step: 7900 | loss: 1.858510798
step: 7910 | loss: 1.854214418
step: 7920 | loss: 1.849922646
step: 7930 | loss: 1.845635493
step: 7940 | loss: 1.841352970
step: 7950 | loss: 1.837075089
step: 7960 | loss: 1.832801861
step: 7970 | loss: 1.828533296
step: 7980 | loss: 1.824269406
step: 7990 | loss: 1.820010201
step: 8000 | loss: 1.815755692
step: 8010 | loss: 1.811505889
step: 8020 | loss: 1.807260804
step: 8030 | loss: 1.803020445
step: 8040 | loss: 1.798784824
step: 8050 | loss: 1.794553951
step: 8060 | loss: 1.790327835
step: 8070 | loss: 1.786106487
step: 8080 | loss: 1.781889916
step: 8090 | loss: 1.777678131
step: 8100 | loss: 1.773471143
step: 8110 | loss: 1.769268961
step: 8120 | loss: 1.765071593
step: 8130 | loss: 1.760879049
step: 8140 | loss: 1.756691337
step: 8150 | loss: 1.752508467
step: 8160 | loss: 1.748330448
step: 8170 | loss: 1.744157287
step: 8180 | loss: 1.739988993
step: 8190 | loss: 1.735825574
step: 8200 | loss: 1.731667038
step: 8210 | loss: 1.727513394
step: 8220 | loss: 1.723364648
step: 8230 | loss: 1.719220808
step: 8240 | loss: 1.715081882
step: 8250 | loss: 1.710947877
step: 8260 | loss: 1.706818800
step: 8270 | loss: 1.702694657
step: 8280 | loss: 1.698575456
step: 8290 | loss: 1.694461203
step: 8300 | loss: 1.690351903
step: 8310 | loss: 1.686247564
step: 8320 | loss: 1.682148192
step: 8330 | loss: 1.678053791
step: 8340 | loss: 1.673964367
step: 8350 | loss: 1.669879926
step: 8360 | loss: 1.665800473
step: 8370 | loss: 1.661726013
step: 8380 | loss: 1.657656550
step: 8390 | loss: 1.653592090
step: 8400 | loss: 1.649532635
step: 8410 | loss: 1.645478191
step: 8420 | loss: 1.641428761
step: 8430 | loss: 1.637384349
step: 8440 | loss: 1.633344959
step: 8450 | loss: 1.629310592
step: 8460 | loss: 1.625281253
step: 8470 | loss: 1.621256944
step: 8480 | loss: 1.617237667
step: 8490 | loss: 1.613223425
step: 8500 | loss: 1.609214220
step: 8510 | loss: 1.605210054
step: 8520 | loss: 1.601210927
step: 8530 | loss: 1.597216842
step: 8540 | loss: 1.593227798
step: 8550 | loss: 1.589243798
step: 8560 | loss: 1.585264841
step: 8570 | loss: 1.581290928
step: 8580 | loss: 1.577322058
step: 8590 | loss: 1.573358232
step: 8600 | loss: 1.569399448
step: 8610 | loss: 1.565445706
step: 8620 | loss: 1.561497005
step: 8630 | loss: 1.557553343
step: 8640 | loss: 1.553614719
step: 8650 | loss: 1.549681130
step: 8660 | loss: 1.545752575
step: 8670 | loss: 1.541829050
step: 8680 | loss: 1.537910554
step: 8690 | loss: 1.533997082
step: 8700 | loss: 1.530088632
step: 8710 | loss: 1.526185200
step: 8720 | loss: 1.522286781
step: 8730 | loss: 1.518393372
step: 8740 | loss: 1.514504968
step: 8750 | loss: 1.510621563
step: 8760 | loss: 1.506743154
step: 8770 | loss: 1.502869733
step: 8780 | loss: 1.499001296
step: 8790 | loss: 1.495137837
step: 8800 | loss: 1.491279348
step: 8810 | loss: 1.487425823
step: 8820 | loss: 1.483577255
step: 8830 | loss: 1.479733636
step: 8840 | loss: 1.475894959
step: 8850 | loss: 1.472061217
step: 8860 | loss: 1.468232399
step: 8870 | loss: 1.464408499
step: 8880 | loss: 1.460589506
step: 8890 | loss: 1.456775411
step: 8900 | loss: 1.452966206
step: 8910 | loss: 1.449161879
step: 8920 | loss: 1.445362420
step: 8930 | loss: 1.441567820
step: 8940 | loss: 1.437778066
step: 8950 | loss: 1.433993148
step: 8960 | loss: 1.430213055
step: 8970 | loss: 1.426437773
step: 8980 | loss: 1.422667291
step: 8990 | loss: 1.418901596
step: 9000 | loss: 1.415140676
step: 9010 | loss: 1.411384516
step: 9020 | loss: 1.407633104
step: 9030 | loss: 1.403886426
step: 9040 | loss: 1.400144467
step: 9050 | loss: 1.396407213
step: 9060 | loss: 1.392674648
step: 9070 | loss: 1.388946758
step: 9080 | loss: 1.385223528
step: 9090 | loss: 1.381504940
step: 9100 | loss: 1.377790980
step: 9110 | loss: 1.374081630
step: 9120 | loss: 1.370376873
step: 9130 | loss: 1.366676693
step: 9140 | loss: 1.362981073
step: 9150 | loss: 1.359289993
step: 9160 | loss: 1.355603437
step: 9170 | loss: 1.351921385
step: 9180 | loss: 1.348243819
step: 9190 | loss: 1.344570721
step: 9200 | loss: 1.340902070
step: 9210 | loss: 1.337237847
step: 9220 | loss: 1.333578032
step: 9230 | loss: 1.329922604
step: 9240 | loss: 1.326271544
step: 9250 | loss: 1.322624831
step: 9260 | loss: 1.318982442
step: 9270 | loss: 1.315344358
step: 9280 | loss: 1.311710555
step: 9290 | loss: 1.308081013
step: 9300 | loss: 1.304455709
step: 9310 | loss: 1.300834619
step: 9320 | loss: 1.297217723
step: 9330 | loss: 1.293604996
step: 9340 | loss: 1.289996415
step: 9350 | loss: 1.286391956
step: 9360 | loss: 1.282791596
step: 9370 | loss: 1.279195311
step: 9380 | loss: 1.275603076
step: 9390 | loss: 1.272014866
step: 9400 | loss: 1.268430657
step: 9410 | loss: 1.264850423
step: 9420 | loss: 1.261274140
step: 9430 | loss: 1.257701782
step: 9440 | loss: 1.254133322
step: 9450 | loss: 1.250568735
step: 9460 | loss: 1.247007995
step: 9470 | loss: 1.243451076
step: 9480 | loss: 1.239897950
step: 9490 | loss: 1.236348591
step: 9500 | loss: 1.232802972
step: 9510 | loss: 1.229261065
step: 9520 | loss: 1.225722844
step: 9530 | loss: 1.222188281
step: 9540 | loss: 1.218657347
step: 9550 | loss: 1.215130016
step: 9560 | loss: 1.211606259
step: 9570 | loss: 1.208086047
step: 9580 | loss: 1.204569353
step: 9590 | loss: 1.201056148
step: 9600 | loss: 1.197546404
step: 9610 | loss: 1.194040090
step: 9620 | loss: 1.190537180
step: 9630 | loss: 1.187037643
step: 9640 | loss: 1.183541450
step: 9650 | loss: 1.180048572
step: 9660 | loss: 1.176558980
step: 9670 | loss: 1.173072645
step: 9680 | loss: 1.169589535
step: 9690 | loss: 1.166109623
step: 9700 | loss: 1.162632879
step: 9710 | loss: 1.159159271
step: 9720 | loss: 1.155688771
step: 9730 | loss: 1.152221349
step: 9740 | loss: 1.148756974
step: 9750 | loss: 1.145295616
step: 9760 | loss: 1.141837246
step: 9770 | loss: 1.138381833
step: 9780 | loss: 1.134929347
step: 9790 | loss: 1.131479758
step: 9800 | loss: 1.128033035
step: 9810 | loss: 1.124589148
step: 9820 | loss: 1.121148067
step: 9830 | loss: 1.117709761
step: 9840 | loss: 1.114274200
step: 9850 | loss: 1.110841355
step: 9860 | loss: 1.107411193
step: 9870 | loss: 1.103983686
step: 9880 | loss: 1.100558803
step: 9890 | loss: 1.097136514
step: 9900 | loss: 1.093716788
step: 9910 | loss: 1.090299595
step: 9920 | loss: 1.086884906
step: 9930 | loss: 1.083472689
step: 9940 | loss: 1.080062915
step: 9950 | loss: 1.076655555
step: 9960 | loss: 1.073250577
step: 9970 | loss: 1.069847952
step: 9980 | loss: 1.066447650
step: 9990 | loss: 1.063049642
step: 10000 | loss: 1.059653898
step: 10010 | loss: 1.056260388
step: 10020 | loss: 1.052869083
step: 10030 | loss: 1.049479953
step: 10040 | loss: 1.046092970
step: 10050 | loss: 1.042708103
step: 10060 | loss: 1.039325324
step: 10070 | loss: 1.035944604
step: 10080 | loss: 1.032565915
step: 10090 | loss: 1.029189226
step: 10100 | loss: 1.025814511
step: 10110 | loss: 1.022441740
step: 10120 | loss: 1.019070885
step: 10130 | loss: 1.015701917
step: 10140 | loss: 1.012334810
step: 10150 | loss: 1.008969535
step: 10160 | loss: 1.005606064
step: 10170 | loss: 1.002244369
step: 10180 | loss: 0.998884425
step: 10190 | loss: 0.995526202
step: 10200 | loss: 0.992169674
step: 10210 | loss: 0.988814815
step: 10220 | loss: 0.985461597
step: 10230 | loss: 0.982109994
step: 10240 | loss: 0.978759980
step: 10250 | loss: 0.975411529
step: 10260 | loss: 0.972064614
step: 10270 | loss: 0.968719211
step: 10280 | loss: 0.965375293
step: 10290 | loss: 0.962032835
step: 10300 | loss: 0.958691813
step: 10310 | loss: 0.955352201
step: 10320 | loss: 0.952013975
step: 10330 | loss: 0.948677110
step: 10340 | loss: 0.945341582
step: 10350 | loss: 0.942007367
step: 10360 | loss: 0.938674442
step: 10370 | loss: 0.935342783
step: 10380 | loss: 0.932012367
step: 10390 | loss: 0.928683171
step: 10400 | loss: 0.925355172
step: 10410 | loss: 0.922028347
step: 10420 | loss: 0.918702675
step: 10430 | loss: 0.915378134
step: 10440 | loss: 0.912054701
step: 10450 | loss: 0.908732356
step: 10460 | loss: 0.905411077
step: 10470 | loss: 0.902090843
step: 10480 | loss: 0.898771635
step: 10490 | loss: 0.895453430
step: 10500 | loss: 0.892136210
step: 10510 | loss: 0.888819954
step: 10520 | loss: 0.885504644
step: 10530 | loss: 0.882190259
step: 10540 | loss: 0.878876782
step: 10550 | loss: 0.875564192
step: 10560 | loss: 0.872252473
step: 10570 | loss: 0.868941605
step: 10580 | loss: 0.865631572
step: 10590 | loss: 0.862322354
step: 10600 | loss: 0.859013937
step: 10610 | loss: 0.855706302
step: 10620 | loss: 0.852399432
step: 10630 | loss: 0.849093312
step: 10640 | loss: 0.845787926
step: 10650 | loss: 0.842483257
step: 10660 | loss: 0.839179291
step: 10670 | loss: 0.835876012
step: 10680 | loss: 0.832573405
step: 10690 | loss: 0.829271456
step: 10700 | loss: 0.825970151
step: 10710 | loss: 0.822669475
step: 10720 | loss: 0.819369415
step: 10730 | loss: 0.816069957
step: 10740 | loss: 0.812771088
step: 10750 | loss: 0.809472796
step: 10760 | loss: 0.806175068
step: 10770 | loss: 0.802877892
step: 10780 | loss: 0.799581255
step: 10790 | loss: 0.796285146
step: 10800 | loss: 0.792989553
step: 10810 | loss: 0.789694466
step: 10820 | loss: 0.786399873
step: 10830 | loss: 0.783105764
step: 10840 | loss: 0.779812129
step: 10850 | loss: 0.776518956
step: 10860 | loss: 0.773226237
step: 10870 | loss: 0.769933962
step: 10880 | loss: 0.766642121
step: 10890 | loss: 0.763350705
step: 10900 | loss: 0.760059706
step: 10910 | loss: 0.756769115
step: 10920 | loss: 0.753478923
step: 10930 | loss: 0.750189122
step: 10940 | loss: 0.746899705
step: 10950 | loss: 0.743610663
step: 10960 | loss: 0.740321989
step: 10970 | loss: 0.737033676
step: 10980 | loss: 0.733745716
step: 10990 | loss: 0.730458103
step: 11000 | loss: 0.727170830
step: 11010 | loss: 0.723883891
step: 11020 | loss: 0.720597279
step: 11030 | loss: 0.717310988
step: 11040 | loss: 0.714025012
step: 11050 | loss: 0.710739346
step: 11060 | loss: 0.707453983
step: 11070 | loss: 0.704168919
step: 11080 | loss: 0.700884148
step: 11090 | loss: 0.697599665
step: 11100 | loss: 0.694315465
step: 11110 | loss: 0.691031543
step: 11120 | loss: 0.687747895
step: 11130 | loss: 0.684464515
step: 11140 | loss: 0.681181401
step: 11150 | loss: 0.677898547
step: 11160 | loss: 0.674615949
step: 11170 | loss: 0.671333604
step: 11180 | loss: 0.668051506
step: 11190 | loss: 0.664769654
step: 11200 | loss: 0.661488042
step: 11210 | loss: 0.658206668
step: 11220 | loss: 0.654925527
step: 11230 | loss: 0.651644617
step: 11240 | loss: 0.648363935
step: 11250 | loss: 0.645083476
step: 11260 | loss: 0.641803239
step: 11270 | loss: 0.638523219
step: 11280 | loss: 0.635243415
step: 11290 | loss: 0.631963822
step: 11300 | loss: 0.628684439
step: 11310 | loss: 0.625405263
step: 11320 | loss: 0.622126291
step: 11330 | loss: 0.618847520
step: 11340 | loss: 0.615568948
step: 11350 | loss: 0.612290573
step: 11360 | loss: 0.609012392
step: 11370 | loss: 0.605734403
step: 11380 | loss: 0.602456603
step: 11390 | loss: 0.599178990
step: 11400 | loss: 0.595901563
step: 11410 | loss: 0.592624318
step: 11420 | loss: 0.589347255
step: 11430 | loss: 0.586070370
step: 11440 | loss: 0.582793662
step: 11450 | loss: 0.579517128
step: 11460 | loss: 0.576240768
step: 11470 | loss: 0.572964578
step: 11480 | loss: 0.569688557
step: 11490 | loss: 0.566412704
step: 11500 | loss: 0.563137015
step: 11510 | loss: 0.559861491
step: 11520 | loss: 0.556586128
step: 11530 | loss: 0.553310926
step: 11540 | loss: 0.550035882
step: 11550 | loss: 0.546760994
step: 11560 | loss: 0.543486262
step: 11570 | loss: 0.540211683
step: 11580 | loss: 0.536937256
step: 11590 | loss: 0.533662979
step: 11600 | loss: 0.530388851
step: 11610 | loss: 0.527114871
step: 11620 | loss: 0.523841036
step: 11630 | loss: 0.520567345
step: 11640 | loss: 0.517293796
step: 11650 | loss: 0.514020389
step: 11660 | loss: 0.510747122
step: 11670 | loss: 0.507473993
step: 11680 | loss: 0.504201001
step: 11690 | loss: 0.500928145
step: 11700 | loss: 0.497655423
step: 11710 | loss: 0.494382833
step: 11720 | loss: 0.491110375
step: 11730 | loss: 0.487838047
step: 11740 | loss: 0.484565848
step: 11750 | loss: 0.481293777
step: 11760 | loss: 0.478021831
step: 11770 | loss: 0.474750011
step: 11780 | loss: 0.471478314
step: 11790 | loss: 0.468206740
step: 11800 | loss: 0.464935286
step: 11810 | loss: 0.461663953
step: 11820 | loss: 0.458392739
step: 11830 | loss: 0.455121642
step: 11840 | loss: 0.451850661
step: 11850 | loss: 0.448579796
step: 11860 | loss: 0.445309045
step: 11870 | loss: 0.442038406
step: 11880 | loss: 0.438767880
step: 11890 | loss: 0.435497464
step: 11900 | loss: 0.432227157
step: 11910 | loss: 0.428956959
step: 11920 | loss: 0.425686869
step: 11930 | loss: 0.422416884
step: 11940 | loss: 0.419147005
step: 11950 | loss: 0.415877230
step: 11960 | loss: 0.412607557
step: 11970 | loss: 0.409337987
step: 11980 | loss: 0.406068518
step: 11990 | loss: 0.402799149
step: 12000 | loss: 0.399529879
step: 12010 | loss: 0.396260707
step: 12020 | loss: 0.392991632
step: 12030 | loss: 0.389722653
step: 12040 | loss: 0.386453769
step: 12050 | loss: 0.383184979
step: 12060 | loss: 0.379916283
step: 12070 | loss: 0.376647679
step: 12080 | loss: 0.373379165
step: 12090 | loss: 0.370110743
step: 12100 | loss: 0.366842410
step: 12110 | loss: 0.363574165
step: 12120 | loss: 0.360306008
step: 12130 | loss: 0.357037938
step: 12140 | loss: 0.353769954
step: 12150 | loss: 0.350502055
step: 12160 | loss: 0.347234240
step: 12170 | loss: 0.343966509
step: 12180 | loss: 0.340698860
step: 12190 | loss: 0.337431293
step: 12200 | loss: 0.334163807
step: 12210 | loss: 0.330896401
step: 12220 | loss: 0.327629074
step: 12230 | loss: 0.324361826
step: 12240 | loss: 0.321094655
step: 12250 | loss: 0.317827561
step: 12260 | loss: 0.314560544
step: 12270 | loss: 0.311293602
step: 12280 | loss: 0.308026734
step: 12290 | loss: 0.304759941
step: 12300 | loss: 0.301493221
step: 12310 | loss: 0.298226573
step: 12320 | loss: 0.294959996
step: 12330 | loss: 0.291693491
step: 12340 | loss: 0.288427056
step: 12350 | loss: 0.285160690
step: 12360 | loss: 0.281894394
step: 12370 | loss: 0.278628165
step: 12380 | loss: 0.275362004
step: 12390 | loss: 0.272095910
step: 12400 | loss: 0.268829882
step: 12410 | loss: 0.265563920
step: 12420 | loss: 0.262298022
step: 12430 | loss: 0.259032189
step: 12440 | loss: 0.255766419
step: 12450 | loss: 0.252500712
step: 12460 | loss: 0.249235067
step: 12470 | loss: 0.245969484
step: 12480 | loss: 0.242703962
step: 12490 | loss: 0.239438500
step: 12500 | loss: 0.236173098
step: 12510 | loss: 0.232907755
step: 12520 | loss: 0.229642471
step: 12530 | loss: 0.226377245
step: 12540 | loss: 0.223112077
step: 12550 | loss: 0.219846965
step: 12560 | loss: 0.216581910
step: 12570 | loss: 0.213316910
step: 12580 | loss: 0.210051966
step: 12590 | loss: 0.206787076
step: 12600 | loss: 0.203522240
step: 12610 | loss: 0.200257458
step: 12620 | loss: 0.196992729
step: 12630 | loss: 0.193728052
step: 12640 | loss: 0.190463428
step: 12650 | loss: 0.187198855
step: 12660 | loss: 0.183934332
step: 12670 | loss: 0.180669860
step: 12680 | loss: 0.177405438
step: 12690 | loss: 0.174141066
step: 12700 | loss: 0.170876742
step: 12710 | loss: 0.167612467
step: 12720 | loss: 0.164348240
step: 12730 | loss: 0.161084060
step: 12740 | loss: 0.157819927
step: 12750 | loss: 0.154555841
step: 12760 | loss: 0.151291800
step: 12770 | loss: 0.148027806
step: 12780 | loss: 0.144763856
step: 12790 | loss: 0.141499951
step: 12800 | loss: 0.138236091
step: 12810 | loss: 0.134972274
step: 12820 | loss: 0.131708501
step: 12830 | loss: 0.128444770
step: 12840 | loss: 0.125181082
step: 12850 | loss: 0.121917436
step: 12860 | loss: 0.118653832
step: 12870 | loss: 0.115390269
step: 12880 | loss: 0.112126747
step: 12890 | loss: 0.108863265
step: 12900 | loss: 0.105599823
step: 12910 | loss: 0.102336421
step: 12920 | loss: 0.099073058
step: 12930 | loss: 0.095809734
step: 12940 | loss: 0.092546448
step: 12950 | loss: 0.089283200
step: 12960 | loss: 0.086019991
step: 12970 | loss: 0.082756818
step: 12980 | loss: 0.079493682
step: 12990 | loss: 0.076230583
step: 13000 | loss: 0.072967520
step: 13010 | loss: 0.069704493
step: 13020 | loss: 0.066441501
step: 13030 | loss: 0.063178545
step: 13040 | loss: 0.059915623
step: 13050 | loss: 0.056652735
step: 13060 | loss: 0.053389882
step: 13070 | loss: 0.050127063
step: 13080 | loss: 0.046864276
step: 13090 | loss: 0.043601523
step: 13100 | loss: 0.040338803
step: 13110 | loss: 0.037076115
step: 13120 | loss: 0.033813459
step: 13130 | loss: 0.030550835
step: 13140 | loss: 0.027289522
step: 13150 | loss: 0.024025766
step: 13160 | loss: 0.020763413
step: 13170 | loss: 0.017500846
step: 13180 | loss: 0.014238321
step: 13190 | loss: 0.010975891
step: 13200 | loss: 0.007713639
step: 13210 | loss: 0.004452549
step: 13220 | loss: 0.001190869
- final loss: 0.000869
- (cd _build/default/examples/opt && ./rmsprop.exe)
-
step: 0 | loss: 4.219410115
step: 10 | loss: 4.211019027
step: 20 | loss: 4.205076600
step: 30 | loss: 4.199651382
step: 40 | loss: 4.194376682
step: 50 | loss: 4.189153525
step: 60 | loss: 4.183950076
step: 70 | loss: 4.178755542
step: 80 | loss: 4.173566202
step: 90 | loss: 4.168380770
step: 100 | loss: 4.163198807
step: 110 | loss: 4.158020167
step: 120 | loss: 4.152844807
step: 130 | loss: 4.147672720
step: 140 | loss: 4.142503911
step: 150 | loss: 4.137338390
step: 160 | loss: 4.132176168
step: 170 | loss: 4.127017258
step: 180 | loss: 4.121861672
step: 190 | loss: 4.116709421
step: 200 | loss: 4.111560519
step: 210 | loss: 4.106414977
step: 220 | loss: 4.101272809
step: 230 | loss: 4.096134026
step: 240 | loss: 4.090998641
step: 250 | loss: 4.085866667
step: 260 | loss: 4.080738116
step: 270 | loss: 4.075613001
step: 280 | loss: 4.070491336
step: 290 | loss: 4.065373132
step: 300 | loss: 4.060258403
step: 310 | loss: 4.055147162
step: 320 | loss: 4.050039421
step: 330 | loss: 4.044935193
step: 340 | loss: 4.039834493
step: 350 | loss: 4.034737332
step: 360 | loss: 4.029643724
step: 370 | loss: 4.024553683
step: 380 | loss: 4.019467221
step: 390 | loss: 4.014384352
step: 400 | loss: 4.009305089
step: 410 | loss: 4.004229446
step: 420 | loss: 3.999157436
step: 430 | loss: 3.994089073
step: 440 | loss: 3.989024370
step: 450 | loss: 3.983963342
step: 460 | loss: 3.978906001
step: 470 | loss: 3.973852362
step: 480 | loss: 3.968802437
step: 490 | loss: 3.963756243
step: 500 | loss: 3.958713791
step: 510 | loss: 3.953675096
step: 520 | loss: 3.948640173
step: 530 | loss: 3.943609034
step: 540 | loss: 3.938581695
step: 550 | loss: 3.933558169
step: 560 | loss: 3.928538471
step: 570 | loss: 3.923522615
step: 580 | loss: 3.918510615
step: 590 | loss: 3.913502486
step: 600 | loss: 3.908498241
step: 610 | loss: 3.903497897
step: 620 | loss: 3.898501466
step: 630 | loss: 3.893508963
step: 640 | loss: 3.888520404
step: 650 | loss: 3.883535803
step: 660 | loss: 3.878555174
step: 670 | loss: 3.873578533
step: 680 | loss: 3.868605893
step: 690 | loss: 3.863637270
step: 700 | loss: 3.858672679
step: 710 | loss: 3.853712135
step: 720 | loss: 3.848755653
step: 730 | loss: 3.843803246
step: 740 | loss: 3.838854932
step: 750 | loss: 3.833910724
step: 760 | loss: 3.828970638
step: 770 | loss: 3.824034689
step: 780 | loss: 3.819102892
step: 790 | loss: 3.814175262
step: 800 | loss: 3.809251815
step: 810 | loss: 3.804332565
step: 820 | loss: 3.799417529
step: 830 | loss: 3.794506720
step: 840 | loss: 3.789600155
step: 850 | loss: 3.784697849
step: 860 | loss: 3.779799817
step: 870 | loss: 3.774906075
step: 880 | loss: 3.770016637
step: 890 | loss: 3.765131519
step: 900 | loss: 3.760250737
step: 910 | loss: 3.755374305
step: 920 | loss: 3.750502239
step: 930 | loss: 3.745634554
step: 940 | loss: 3.740771264
step: 950 | loss: 3.735912387
step: 960 | loss: 3.731057935
step: 970 | loss: 3.726207924
step: 980 | loss: 3.721362369
step: 990 | loss: 3.716521285
step: 1000 | loss: 3.711684686
step: 1010 | loss: 3.706852587
step: 1020 | loss: 3.702025001
step: 1030 | loss: 3.697201942
step: 1040 | loss: 3.692383426
step: 1050 | loss: 3.687569463
step: 1060 | loss: 3.682760068
step: 1070 | loss: 3.677955254
step: 1080 | loss: 3.673155032
step: 1090 | loss: 3.668359413
step: 1100 | loss: 3.663568409
step: 1110 | loss: 3.658782028
step: 1120 | loss: 3.654000281
step: 1130 | loss: 3.649223174
step: 1140 | loss: 3.644450713
step: 1150 | loss: 3.639682903
step: 1160 | loss: 3.634919744
step: 1170 | loss: 3.630161235
step: 1180 | loss: 3.625407370
step: 1190 | loss: 3.620658137
step: 1200 | loss: 3.615913519
step: 1210 | loss: 3.611173485
step: 1220 | loss: 3.606437992
step: 1230 | loss: 3.601706974
step: 1240 | loss: 3.596980332
step: 1250 | loss: 3.592257912
step: 1260 | loss: 3.587539482
step: 1270 | loss: 3.582824693
step: 1280 | loss: 3.578113125
step: 1290 | loss: 3.573404471
step: 1300 | loss: 3.568698652
step: 1310 | loss: 3.563995672
step: 1320 | loss: 3.559295544
step: 1330 | loss: 3.554598277
step: 1340 | loss: 3.549903884
step: 1350 | loss: 3.545212375
step: 1360 | loss: 3.540523763
step: 1370 | loss: 3.535838057
step: 1380 | loss: 3.531155271
step: 1390 | loss: 3.526475415
step: 1400 | loss: 3.521798502
step: 1410 | loss: 3.517124542
step: 1420 | loss: 3.512453547
step: 1430 | loss: 3.507785530
step: 1440 | loss: 3.503120501
step: 1450 | loss: 3.498458473
step: 1460 | loss: 3.493799458
step: 1470 | loss: 3.489143468
step: 1480 | loss: 3.484490514
step: 1490 | loss: 3.479840609
step: 1500 | loss: 3.475193765
step: 1510 | loss: 3.470549995
step: 1520 | loss: 3.465909309
step: 1530 | loss: 3.461271721
step: 1540 | loss: 3.456637243
step: 1550 | loss: 3.452005888
step: 1560 | loss: 3.447377667
step: 1570 | loss: 3.442752594
step: 1580 | loss: 3.438130681
step: 1590 | loss: 3.433511940
step: 1600 | loss: 3.428896384
step: 1610 | loss: 3.424284027
step: 1620 | loss: 3.419674881
step: 1630 | loss: 3.415068958
step: 1640 | loss: 3.410466272
step: 1650 | loss: 3.405866835
step: 1660 | loss: 3.401270662
step: 1670 | loss: 3.396677764
step: 1680 | loss: 3.392088155
step: 1690 | loss: 3.387501848
step: 1700 | loss: 3.382918857
step: 1710 | loss: 3.378339194
step: 1720 | loss: 3.373762874
step: 1730 | loss: 3.369189909
step: 1740 | loss: 3.364620314
step: 1750 | loss: 3.360054102
step: 1760 | loss: 3.355491285
step: 1770 | loss: 3.350931880
step: 1780 | loss: 3.346375898
step: 1790 | loss: 3.341823353
step: 1800 | loss: 3.337274261
step: 1810 | loss: 3.332728634
step: 1820 | loss: 3.328186487
step: 1830 | loss: 3.323647833
step: 1840 | loss: 3.319112687
step: 1850 | loss: 3.314581064
step: 1860 | loss: 3.310052976
step: 1870 | loss: 3.305528440
step: 1880 | loss: 3.301007468
step: 1890 | loss: 3.296490076
step: 1900 | loss: 3.291976279
step: 1910 | loss: 3.287466089
step: 1920 | loss: 3.282959524
step: 1930 | loss: 3.278456596
step: 1940 | loss: 3.273957321
step: 1950 | loss: 3.269461714
step: 1960 | loss: 3.264969790
step: 1970 | loss: 3.260481563
step: 1980 | loss: 3.255997049
step: 1990 | loss: 3.251516262
step: 2000 | loss: 3.247039219
step: 2010 | loss: 3.242565934
step: 2020 | loss: 3.238096422
step: 2030 | loss: 3.233630699
step: 2040 | loss: 3.229168781
step: 2050 | loss: 3.224710683
step: 2060 | loss: 3.220256420
step: 2070 | loss: 3.215806008
step: 2080 | loss: 3.211359464
step: 2090 | loss: 3.206916801
step: 2100 | loss: 3.202478038
step: 2110 | loss: 3.198043189
step: 2120 | loss: 3.193612270
step: 2130 | loss: 3.189185298
step: 2140 | loss: 3.184762288
step: 2150 | loss: 3.180343257
step: 2160 | loss: 3.175928222
step: 2170 | loss: 3.171517198
step: 2180 | loss: 3.167110201
step: 2190 | loss: 3.162707249
step: 2200 | loss: 3.158308358
step: 2210 | loss: 3.153913544
step: 2220 | loss: 3.149522825
step: 2230 | loss: 3.145136216
step: 2240 | loss: 3.140753735
step: 2250 | loss: 3.136375399
step: 2260 | loss: 3.132001224
step: 2270 | loss: 3.127631228
step: 2280 | loss: 3.123265428
step: 2290 | loss: 3.118903840
step: 2300 | loss: 3.114546483
step: 2310 | loss: 3.110193372
step: 2320 | loss: 3.105844527
step: 2330 | loss: 3.101499964
step: 2340 | loss: 3.097159700
step: 2350 | loss: 3.092823754
step: 2360 | loss: 3.088492143
step: 2370 | loss: 3.084164884
step: 2380 | loss: 3.079841996
step: 2390 | loss: 3.075523496
step: 2400 | loss: 3.071209402
step: 2410 | loss: 3.066899733
step: 2420 | loss: 3.062594505
step: 2430 | loss: 3.058293738
step: 2440 | loss: 3.053997449
step: 2450 | loss: 3.049705657
step: 2460 | loss: 3.045418380
step: 2470 | loss: 3.041135637
step: 2480 | loss: 3.036857445
step: 2490 | loss: 3.032583823
step: 2500 | loss: 3.028314791
step: 2510 | loss: 3.024050366
step: 2520 | loss: 3.019790567
step: 2530 | loss: 3.015535413
step: 2540 | loss: 3.011284922
step: 2550 | loss: 3.007039115
step: 2560 | loss: 3.002798008
step: 2570 | loss: 2.998561622
step: 2580 | loss: 2.994329976
step: 2590 | loss: 2.990103088
step: 2600 | loss: 2.985880978
step: 2610 | loss: 2.981663665
step: 2620 | loss: 2.977451167
step: 2630 | loss: 2.973243505
step: 2640 | loss: 2.969040698
step: 2650 | loss: 2.964842764
step: 2660 | loss: 2.960649724
step: 2670 | loss: 2.956461597
step: 2680 | loss: 2.952278402
step: 2690 | loss: 2.948100158
step: 2700 | loss: 2.943926886
step: 2710 | loss: 2.939758604
step: 2720 | loss: 2.935595333
step: 2730 | loss: 2.931437092
step: 2740 | loss: 2.927283900
step: 2750 | loss: 2.923135777
step: 2760 | loss: 2.918992743
step: 2770 | loss: 2.914854816
step: 2780 | loss: 2.910722018
step: 2790 | loss: 2.906594367
step: 2800 | loss: 2.902471882
step: 2810 | loss: 2.898354584
step: 2820 | loss: 2.894242491
step: 2830 | loss: 2.890135623
step: 2840 | loss: 2.886033999
step: 2850 | loss: 2.881937638
step: 2860 | loss: 2.877846560
step: 2870 | loss: 2.873760783
step: 2880 | loss: 2.869680326
step: 2890 | loss: 2.865605207
step: 2900 | loss: 2.861535446
step: 2910 | loss: 2.857471059
step: 2920 | loss: 2.853412066
step: 2930 | loss: 2.849358483
step: 2940 | loss: 2.845310328
step: 2950 | loss: 2.841267618
step: 2960 | loss: 2.837230368
step: 2970 | loss: 2.833198595
step: 2980 | loss: 2.829172314
step: 2990 | loss: 2.825151538
step: 3000 | loss: 2.821136282
step: 3010 | loss: 2.817126556
step: 3020 | loss: 2.813122373
step: 3030 | loss: 2.809123742
step: 3040 | loss: 2.805130669
step: 3050 | loss: 2.801143160
step: 3060 | loss: 2.797161216
step: 3070 | loss: 2.793184836
step: 3080 | loss: 2.789214013
step: 3090 | loss: 2.785248734
step: 3100 | loss: 2.781288977
step: 3110 | loss: 2.777334708
step: 3120 | loss: 2.773385878
step: 3130 | loss: 2.769442413
step: 3140 | loss: 2.765504201
step: 3150 | loss: 2.761571076
step: 3160 | loss: 2.757642781
step: 3170 | loss: 2.753718928
step: 3180 | loss: 2.749798990
step: 3190 | loss: 2.745882459
step: 3200 | loss: 2.741969108
step: 3210 | loss: 2.738058917
step: 3220 | loss: 2.734151894
step: 3230 | loss: 2.730248047
step: 3240 | loss: 2.726347385
step: 3250 | loss: 2.722449917
step: 3260 | loss: 2.718555651
step: 3270 | loss: 2.714664593
step: 3280 | loss: 2.710776751
step: 3290 | loss: 2.706892130
step: 3300 | loss: 2.703010737
step: 3310 | loss: 2.699132574
step: 3320 | loss: 2.695257645
step: 3330 | loss: 2.691385952
step: 3340 | loss: 2.687517494
step: 3350 | loss: 2.683652269
step: 3360 | loss: 2.679790272
step: 3370 | loss: 2.675931494
step: 3380 | loss: 2.672075924
step: 3390 | loss: 2.668223543
step: 3400 | loss: 2.664374327
step: 3410 | loss: 2.660528240
step: 3420 | loss: 2.656685236
step: 3430 | loss: 2.652845246
step: 3440 | loss: 2.649008175
step: 3450 | loss: 2.645173886
step: 3460 | loss: 2.641342174
step: 3470 | loss: 2.637512729
step: 3480 | loss: 2.633685093
step: 3490 | loss: 2.629858684
step: 3500 | loss: 2.626033028
step: 3510 | loss: 2.622207963
step: 3520 | loss: 2.618383477
step: 3530 | loss: 2.614559572
step: 3540 | loss: 2.610736251
step: 3550 | loss: 2.606913516
step: 3560 | loss: 2.603091370
step: 3570 | loss: 2.599269816
step: 3580 | loss: 2.595448857
step: 3590 | loss: 2.591628494
step: 3600 | loss: 2.587808730
step: 3610 | loss: 2.583989569
step: 3620 | loss: 2.580171012
step: 3630 | loss: 2.576353063
step: 3640 | loss: 2.572535724
step: 3650 | loss: 2.568718999
step: 3660 | loss: 2.564902888
step: 3670 | loss: 2.561087396
step: 3680 | loss: 2.557272526
step: 3690 | loss: 2.553458279
step: 3700 | loss: 2.549644659
step: 3710 | loss: 2.545831668
step: 3720 | loss: 2.542019310
step: 3730 | loss: 2.538207587
step: 3740 | loss: 2.534396502
step: 3750 | loss: 2.530586058
step: 3760 | loss: 2.526776258
step: 3770 | loss: 2.522967105
step: 3780 | loss: 2.519158602
step: 3790 | loss: 2.515350751
step: 3800 | loss: 2.511543555
step: 3810 | loss: 2.507737019
step: 3820 | loss: 2.503931144
step: 3830 | loss: 2.500125933
step: 3840 | loss: 2.496321390
step: 3850 | loss: 2.492517518
step: 3860 | loss: 2.488714320
step: 3870 | loss: 2.484911798
step: 3880 | loss: 2.481109957
step: 3890 | loss: 2.477308799
step: 3900 | loss: 2.473508326
step: 3910 | loss: 2.469708544
step: 3920 | loss: 2.465909453
step: 3930 | loss: 2.462111059
step: 3940 | loss: 2.458313364
step: 3950 | loss: 2.454516371
step: 3960 | loss: 2.450720083
step: 3970 | loss: 2.446924504
step: 3980 | loss: 2.443129638
step: 3990 | loss: 2.439335486
step: 4000 | loss: 2.435542054
step: 4010 | loss: 2.431749343
step: 4020 | loss: 2.427957358
step: 4030 | loss: 2.424166102
step: 4040 | loss: 2.420375579
step: 4050 | loss: 2.416585791
step: 4060 | loss: 2.412796742
step: 4070 | loss: 2.409008436
step: 4080 | loss: 2.405220876
step: 4090 | loss: 2.401434066
step: 4100 | loss: 2.397648009
step: 4110 | loss: 2.393862709
step: 4120 | loss: 2.390078169
step: 4130 | loss: 2.386294393
step: 4140 | loss: 2.382511385
step: 4150 | loss: 2.378729149
step: 4160 | loss: 2.374947687
step: 4170 | loss: 2.371167004
step: 4180 | loss: 2.367387104
step: 4190 | loss: 2.363607989
step: 4200 | loss: 2.359829665
step: 4210 | loss: 2.356052135
step: 4220 | loss: 2.352275402
step: 4230 | loss: 2.348499470
step: 4240 | loss: 2.344724344
step: 4250 | loss: 2.340950027
step: 4260 | loss: 2.337176522
step: 4270 | loss: 2.333403835
step: 4280 | loss: 2.329631969
step: 4290 | loss: 2.325860928
step: 4300 | loss: 2.322090716
step: 4310 | loss: 2.318321337
step: 4320 | loss: 2.314552795
step: 4330 | loss: 2.310785094
step: 4340 | loss: 2.307018239
step: 4350 | loss: 2.303252232
step: 4360 | loss: 2.299487080
step: 4370 | loss: 2.295722785
step: 4380 | loss: 2.291959352
step: 4390 | loss: 2.288196786
step: 4400 | loss: 2.284435090
step: 4410 | loss: 2.280674269
step: 4420 | loss: 2.276914326
step: 4430 | loss: 2.273155268
step: 4440 | loss: 2.269397097
step: 4450 | loss: 2.265639819
step: 4460 | loss: 2.261883437
step: 4470 | loss: 2.258127957
step: 4480 | loss: 2.254373382
step: 4490 | loss: 2.250619717
step: 4500 | loss: 2.246866968
step: 4510 | loss: 2.243115137
step: 4520 | loss: 2.239364231
step: 4530 | loss: 2.235614253
step: 4540 | loss: 2.231865208
step: 4550 | loss: 2.228117102
step: 4560 | loss: 2.224369938
step: 4570 | loss: 2.220623721
step: 4580 | loss: 2.216878457
step: 4590 | loss: 2.213134150
step: 4600 | loss: 2.209390805
step: 4610 | loss: 2.205648427
step: 4620 | loss: 2.201907020
step: 4630 | loss: 2.198166591
step: 4640 | loss: 2.194427142
step: 4650 | loss: 2.190688681
step: 4660 | loss: 2.186951211
step: 4670 | loss: 2.183214739
step: 4680 | loss: 2.179479268
step: 4690 | loss: 2.175744804
step: 4700 | loss: 2.172011352
step: 4710 | loss: 2.168278919
step: 4720 | loss: 2.164547507
step: 4730 | loss: 2.160817124
step: 4740 | loss: 2.157087774
step: 4750 | loss: 2.153359463
step: 4760 | loss: 2.149632196
step: 4770 | loss: 2.145905979
step: 4780 | loss: 2.142180817
step: 4790 | loss: 2.138456715
step: 4800 | loss: 2.134733679
step: 4810 | loss: 2.131011715
step: 4820 | loss: 2.127290828
step: 4830 | loss: 2.123571024
step: 4840 | loss: 2.119852308
step: 4850 | loss: 2.116134687
step: 4860 | loss: 2.112418166
step: 4870 | loss: 2.108702750
step: 4880 | loss: 2.104988447
step: 4890 | loss: 2.101275261
step: 4900 | loss: 2.097563198
step: 4910 | loss: 2.093852265
step: 4920 | loss: 2.090142467
step: 4930 | loss: 2.086433811
step: 4940 | loss: 2.082726303
step: 4950 | loss: 2.079019948
step: 4960 | loss: 2.075314753
step: 4970 | loss: 2.071610724
step: 4980 | loss: 2.067907867
step: 4990 | loss: 2.064206190
step: 5000 | loss: 2.060505697
step: 5010 | loss: 2.056806395
step: 5020 | loss: 2.053108291
step: 5030 | loss: 2.049411391
step: 5040 | loss: 2.045715703
step: 5050 | loss: 2.042021231
step: 5060 | loss: 2.038327983
step: 5070 | loss: 2.034635966
step: 5080 | loss: 2.030945186
step: 5090 | loss: 2.027255650
step: 5100 | loss: 2.023567365
step: 5110 | loss: 2.019880337
step: 5120 | loss: 2.016194574
step: 5130 | loss: 2.012510082
step: 5140 | loss: 2.008826869
step: 5150 | loss: 2.005144941
step: 5160 | loss: 2.001464305
step: 5170 | loss: 1.997784969
step: 5180 | loss: 1.994106940
step: 5190 | loss: 1.990430225
step: 5200 | loss: 1.986754831
step: 5210 | loss: 1.983080766
step: 5220 | loss: 1.979408036
step: 5230 | loss: 1.975736650
step: 5240 | loss: 1.972066615
step: 5250 | loss: 1.968397938
step: 5260 | loss: 1.964730627
step: 5270 | loss: 1.961064690
step: 5280 | loss: 1.957400134
step: 5290 | loss: 1.953736967
step: 5300 | loss: 1.950075197
step: 5310 | loss: 1.946414831
step: 5320 | loss: 1.942755879
step: 5330 | loss: 1.939098346
step: 5340 | loss: 1.935442243
step: 5350 | loss: 1.931787576
step: 5360 | loss: 1.928134354
step: 5370 | loss: 1.924482585
step: 5380 | loss: 1.920832277
step: 5390 | loss: 1.917183439
step: 5400 | loss: 1.913536079
step: 5410 | loss: 1.909890205
step: 5420 | loss: 1.906245827
step: 5430 | loss: 1.902602951
step: 5440 | loss: 1.898961588
step: 5450 | loss: 1.895321746
step: 5460 | loss: 1.891683433
step: 5470 | loss: 1.888046658
step: 5480 | loss: 1.884411431
step: 5490 | loss: 1.880777760
step: 5500 | loss: 1.877145654
step: 5510 | loss: 1.873515122
step: 5520 | loss: 1.869886173
step: 5530 | loss: 1.866258817
step: 5540 | loss: 1.862633063
step: 5550 | loss: 1.859008920
step: 5560 | loss: 1.855386398
step: 5570 | loss: 1.851765505
step: 5580 | loss: 1.848146253
step: 5590 | loss: 1.844528649
step: 5600 | loss: 1.840912704
step: 5610 | loss: 1.837298428
step: 5620 | loss: 1.833685830
step: 5630 | loss: 1.830074920
step: 5640 | loss: 1.826465709
step: 5650 | loss: 1.822858206
step: 5660 | loss: 1.819252422
step: 5670 | loss: 1.815648366
step: 5680 | loss: 1.812046049
step: 5690 | loss: 1.808445481
step: 5700 | loss: 1.804846673
step: 5710 | loss: 1.801249635
step: 5720 | loss: 1.797654378
step: 5730 | loss: 1.794060912
step: 5740 | loss: 1.790469249
step: 5750 | loss: 1.786879398
step: 5760 | loss: 1.783291371
step: 5770 | loss: 1.779705179
step: 5780 | loss: 1.776120833
step: 5790 | loss: 1.772538344
step: 5800 | loss: 1.768957723
step: 5810 | loss: 1.765378981
step: 5820 | loss: 1.761802131
step: 5830 | loss: 1.758227183
step: 5840 | loss: 1.754654149
step: 5850 | loss: 1.751083040
step: 5860 | loss: 1.747513869
step: 5870 | loss: 1.743946647
step: 5880 | loss: 1.740381386
step: 5890 | loss: 1.736818099
step: 5900 | loss: 1.733256796
step: 5910 | loss: 1.729697491
step: 5920 | loss: 1.726140196
step: 5930 | loss: 1.722584922
step: 5940 | loss: 1.719031684
step: 5950 | loss: 1.715480492
step: 5960 | loss: 1.711931360
step: 5970 | loss: 1.708384301
step: 5980 | loss: 1.704839327
step: 5990 | loss: 1.701296451
step: 6000 | loss: 1.697755687
step: 6010 | loss: 1.694217048
step: 6020 | loss: 1.690680546
step: 6030 | loss: 1.687146195
step: 6040 | loss: 1.683614009
step: 6050 | loss: 1.680084001
step: 6060 | loss: 1.676556185
step: 6070 | loss: 1.673030575
step: 6080 | loss: 1.669507184
step: 6090 | loss: 1.665986026
step: 6100 | loss: 1.662467116
step: 6110 | loss: 1.658950468
step: 6120 | loss: 1.655436096
step: 6130 | loss: 1.651924015
step: 6140 | loss: 1.648414238
step: 6150 | loss: 1.644906782
step: 6160 | loss: 1.641401659
step: 6170 | loss: 1.637898886
step: 6180 | loss: 1.634398477
step: 6190 | loss: 1.630900448
step: 6200 | loss: 1.627404813
step: 6210 | loss: 1.623911588
step: 6220 | loss: 1.620420789
step: 6230 | loss: 1.616932431
step: 6240 | loss: 1.613446529
step: 6250 | loss: 1.609963100
step: 6260 | loss: 1.606482159
step: 6270 | loss: 1.603003723
step: 6280 | loss: 1.599527808
step: 6290 | loss: 1.596054429
step: 6300 | loss: 1.592583605
step: 6310 | loss: 1.589115350
step: 6320 | loss: 1.585649682
step: 6330 | loss: 1.582186618
step: 6340 | loss: 1.578726174
step: 6350 | loss: 1.575268368
step: 6360 | loss: 1.571813218
step: 6370 | loss: 1.568360739
step: 6380 | loss: 1.564910950
step: 6390 | loss: 1.561463869
step: 6400 | loss: 1.558019513
step: 6410 | loss: 1.554577901
step: 6420 | loss: 1.551139049
step: 6430 | loss: 1.547702978
step: 6440 | loss: 1.544269704
step: 6450 | loss: 1.540839246
step: 6460 | loss: 1.537411623
step: 6470 | loss: 1.533986855
step: 6480 | loss: 1.530564958
step: 6490 | loss: 1.527145954
step: 6500 | loss: 1.523729861
step: 6510 | loss: 1.520316698
step: 6520 | loss: 1.516906486
step: 6530 | loss: 1.513499243
step: 6540 | loss: 1.510094989
step: 6550 | loss: 1.506693745
step: 6560 | loss: 1.503295531
step: 6570 | loss: 1.499900367
step: 6580 | loss: 1.496508273
step: 6590 | loss: 1.493119271
step: 6600 | loss: 1.489733381
step: 6610 | loss: 1.486350623
step: 6620 | loss: 1.482971020
step: 6630 | loss: 1.479594592
step: 6640 | loss: 1.476221361
step: 6650 | loss: 1.472851349
step: 6660 | loss: 1.469484577
step: 6670 | loss: 1.466121067
step: 6680 | loss: 1.462760842
step: 6690 | loss: 1.459403924
step: 6700 | loss: 1.456050335
step: 6710 | loss: 1.452700098
step: 6720 | loss: 1.449353236
step: 6730 | loss: 1.446009771
step: 6740 | loss: 1.442669729
step: 6750 | loss: 1.439333130
step: 6760 | loss: 1.436000000
step: 6770 | loss: 1.432670361
step: 6780 | loss: 1.429344239
step: 6790 | loss: 1.426021657
step: 6800 | loss: 1.422702638
step: 6810 | loss: 1.419387209
step: 6820 | loss: 1.416075393
step: 6830 | loss: 1.412767216
step: 6840 | loss: 1.409462703
step: 6850 | loss: 1.406161878
step: 6860 | loss: 1.402864768
step: 6870 | loss: 1.399571397
step: 6880 | loss: 1.396281793
step: 6890 | loss: 1.392995980
step: 6900 | loss: 1.389713986
step: 6910 | loss: 1.386435836
step: 6920 | loss: 1.383161558
step: 6930 | loss: 1.379891179
step: 6940 | loss: 1.376624724
step: 6950 | loss: 1.373362222
step: 6960 | loss: 1.370103701
step: 6970 | loss: 1.366849187
step: 6980 | loss: 1.363598709
step: 6990 | loss: 1.360352295
step: 7000 | loss: 1.357109973
step: 7010 | loss: 1.353871772
step: 7020 | loss: 1.350637720
step: 7030 | loss: 1.347407847
step: 7040 | loss: 1.344182181
step: 7050 | loss: 1.340960752
step: 7060 | loss: 1.337743590
step: 7070 | loss: 1.334530724
step: 7080 | loss: 1.331322184
step: 7090 | loss: 1.328118001
step: 7100 | loss: 1.324918205
step: 7110 | loss: 1.321722826
step: 7120 | loss: 1.318531897
step: 7130 | loss: 1.315345446
step: 7140 | loss: 1.312163507
step: 7150 | loss: 1.308986109
step: 7160 | loss: 1.305813286
step: 7170 | loss: 1.302645069
step: 7180 | loss: 1.299481489
step: 7190 | loss: 1.296322580
step: 7200 | loss: 1.293168374
step: 7210 | loss: 1.290018904
step: 7220 | loss: 1.286874201
step: 7230 | loss: 1.283734301
step: 7240 | loss: 1.280599235
step: 7250 | loss: 1.277469038
step: 7260 | loss: 1.274343742
step: 7270 | loss: 1.271223383
step: 7280 | loss: 1.268107993
step: 7290 | loss: 1.264997607
step: 7300 | loss: 1.261892260
step: 7310 | loss: 1.258791986
step: 7320 | loss: 1.255696819
step: 7330 | loss: 1.252606795
step: 7340 | loss: 1.249521948
step: 7350 | loss: 1.246442313
step: 7360 | loss: 1.243367926
step: 7370 | loss: 1.240298822
step: 7380 | loss: 1.237235036
step: 7390 | loss: 1.234176603
step: 7400 | loss: 1.231123559
step: 7410 | loss: 1.228075939
step: 7420 | loss: 1.225033779
step: 7430 | loss: 1.221997115
step: 7440 | loss: 1.218965980
step: 7450 | loss: 1.215940412
step: 7460 | loss: 1.212920445
step: 7470 | loss: 1.209906113
step: 7480 | loss: 1.206897452
step: 7490 | loss: 1.203894496
step: 7500 | loss: 1.200897279
step: 7510 | loss: 1.197905835
step: 7520 | loss: 1.194920197
step: 7530 | loss: 1.191940397
step: 7540 | loss: 1.188966468
step: 7550 | loss: 1.185998439
step: 7560 | loss: 1.183036342
step: 7570 | loss: 1.180080203
step: 7580 | loss: 1.177130051
step: 7590 | loss: 1.174185911
step: 7600 | loss: 1.171247805
step: 7610 | loss: 1.168315755
step: 7620 | loss: 1.165389776
step: 7630 | loss: 1.162469884
step: 7640 | loss: 1.159556086
step: 7650 | loss: 1.156648385
step: 7660 | loss: 1.153746776
step: 7670 | loss: 1.150851244
step: 7680 | loss: 1.147961762
step: 7690 | loss: 1.145078288
step: 7700 | loss: 1.142200754
step: 7710 | loss: 1.139329064
step: 7720 | loss: 1.136463077
step: 7730 | loss: 1.133602588
step: 7740 | loss: 1.130747292
step: 7750 | loss: 1.127896728
step: 7760 | loss: 1.125050194
step: 7770 | loss: 1.122206639
step: 7780 | loss: 1.119364701
step: 7790 | loss: 1.116523223
step: 7800 | loss: 1.113681786
step: 7810 | loss: 1.110840350
step: 7820 | loss: 1.107998914
step: 7830 | loss: 1.105157479
step: 7840 | loss: 1.102316043
step: 7850 | loss: 1.099474608
step: 7860 | loss: 1.096633172
step: 7870 | loss: 1.093791736
step: 7880 | loss: 1.090950301
step: 7890 | loss: 1.088108865
step: 7900 | loss: 1.085267430
step: 7910 | loss: 1.082425994
step: 7920 | loss: 1.079584559
step: 7930 | loss: 1.076743123
step: 7940 | loss: 1.073901687
step: 7950 | loss: 1.071060252
step: 7960 | loss: 1.068218816
step: 7970 | loss: 1.065377381
step: 7980 | loss: 1.062535945
step: 7990 | loss: 1.059694509
step: 8000 | loss: 1.056853074
step: 8010 | loss: 1.054011638
step: 8020 | loss: 1.051170203
step: 8030 | loss: 1.048328767
step: 8040 | loss: 1.045487332
step: 8050 | loss: 1.042645896
step: 8060 | loss: 1.039804460
step: 8070 | loss: 1.036963025
step: 8080 | loss: 1.034121589
step: 8090 | loss: 1.031280154
step: 8100 | loss: 1.028438718
step: 8110 | loss: 1.025597283
step: 8120 | loss: 1.022755847
step: 8130 | loss: 1.019914411
step: 8140 | loss: 1.017072976
step: 8150 | loss: 1.014231540
step: 8160 | loss: 1.011390105
step: 8170 | loss: 1.008548669
step: 8180 | loss: 1.005707234
step: 8190 | loss: 1.002865798
step: 8200 | loss: 1.000024362
step: 8210 | loss: 0.997182927
step: 8220 | loss: 0.994341491
step: 8230 | loss: 0.991500056
step: 8240 | loss: 0.988658620
step: 8250 | loss: 0.985817184
step: 8260 | loss: 0.982975749
step: 8270 | loss: 0.980134313
step: 8280 | loss: 0.977292878
step: 8290 | loss: 0.974451442
step: 8300 | loss: 0.971610007
step: 8310 | loss: 0.968768571
step: 8320 | loss: 0.965927135
step: 8330 | loss: 0.963085700
step: 8340 | loss: 0.960244264
step: 8350 | loss: 0.957402829
step: 8360 | loss: 0.954561393
step: 8370 | loss: 0.951719958
step: 8380 | loss: 0.948878522
step: 8390 | loss: 0.946037086
step: 8400 | loss: 0.943195651
step: 8410 | loss: 0.940354215
step: 8420 | loss: 0.937512780
step: 8430 | loss: 0.934671344
step: 8440 | loss: 0.931829908
step: 8450 | loss: 0.928988473
step: 8460 | loss: 0.926147037
step: 8470 | loss: 0.923305602
step: 8480 | loss: 0.920464166
step: 8490 | loss: 0.917622731
step: 8500 | loss: 0.914781295
step: 8510 | loss: 0.911939859
step: 8520 | loss: 0.909098424
step: 8530 | loss: 0.906256988
step: 8540 | loss: 0.903415553
step: 8550 | loss: 0.900574117
step: 8560 | loss: 0.897732682
step: 8570 | loss: 0.894891246
step: 8580 | loss: 0.892049810
step: 8590 | loss: 0.889208375
step: 8600 | loss: 0.886366939
step: 8610 | loss: 0.883525504
step: 8620 | loss: 0.880684068
step: 8630 | loss: 0.877842633
step: 8640 | loss: 0.875001197
step: 8650 | loss: 0.872159761
step: 8660 | loss: 0.869318326
step: 8670 | loss: 0.866476890
step: 8680 | loss: 0.863635455
step: 8690 | loss: 0.860794019
step: 8700 | loss: 0.857952583
step: 8710 | loss: 0.855111148
step: 8720 | loss: 0.852269712
step: 8730 | loss: 0.849428277
step: 8740 | loss: 0.846586841
step: 8750 | loss: 0.843745406
step: 8760 | loss: 0.840903970
step: 8770 | loss: 0.838062534
step: 8780 | loss: 0.835221099
step: 8790 | loss: 0.832379663
step: 8800 | loss: 0.829538228
step: 8810 | loss: 0.826696792
step: 8820 | loss: 0.823855357
step: 8830 | loss: 0.821013921
step: 8840 | loss: 0.818172485
step: 8850 | loss: 0.815331050
step: 8860 | loss: 0.812489614
step: 8870 | loss: 0.809648179
step: 8880 | loss: 0.806806743
step: 8890 | loss: 0.803965308
step: 8900 | loss: 0.801123872
step: 8910 | loss: 0.798282436
step: 8920 | loss: 0.795441001
step: 8930 | loss: 0.792599565
step: 8940 | loss: 0.789758130
step: 8950 | loss: 0.786916694
step: 8960 | loss: 0.784075258
step: 8970 | loss: 0.781233823
step: 8980 | loss: 0.778392387
step: 8990 | loss: 0.775550952
step: 9000 | loss: 0.772709516
step: 9010 | loss: 0.769868081
step: 9020 | loss: 0.767026645
step: 9030 | loss: 0.764185209
step: 9040 | loss: 0.761343774
step: 9050 | loss: 0.758502338
step: 9060 | loss: 0.755660903
step: 9070 | loss: 0.752819467
step: 9080 | loss: 0.749978032
step: 9090 | loss: 0.747136596
step: 9100 | loss: 0.744295160
step: 9110 | loss: 0.741453725
step: 9120 | loss: 0.738612289
step: 9130 | loss: 0.735770854
step: 9140 | loss: 0.732929418
step: 9150 | loss: 0.730087982
step: 9160 | loss: 0.727246547
step: 9170 | loss: 0.724405111
step: 9180 | loss: 0.721563676
step: 9190 | loss: 0.718722240
step: 9200 | loss: 0.715880805
step: 9210 | loss: 0.713039369
step: 9220 | loss: 0.710197933
step: 9230 | loss: 0.707356498
step: 9240 | loss: 0.704515062
step: 9250 | loss: 0.701673627
step: 9260 | loss: 0.698832191
step: 9270 | loss: 0.695990756
step: 9280 | loss: 0.693149320
step: 9290 | loss: 0.690307884
step: 9300 | loss: 0.687466449
step: 9310 | loss: 0.684625013
step: 9320 | loss: 0.681783578
step: 9330 | loss: 0.678942142
step: 9340 | loss: 0.676100707
step: 9350 | loss: 0.673259271
step: 9360 | loss: 0.670417835
step: 9370 | loss: 0.667576400
step: 9380 | loss: 0.664734964
step: 9390 | loss: 0.661893529
step: 9400 | loss: 0.659052093
step: 9410 | loss: 0.656210657
step: 9420 | loss: 0.653369222
step: 9430 | loss: 0.650527786
step: 9440 | loss: 0.647686351
step: 9450 | loss: 0.644844915
step: 9460 | loss: 0.642003480
step: 9470 | loss: 0.639162044
step: 9480 | loss: 0.636320608
step: 9490 | loss: 0.633479173
step: 9500 | loss: 0.630637737
step: 9510 | loss: 0.627796302
step: 9520 | loss: 0.624954866
step: 9530 | loss: 0.622113431
step: 9540 | loss: 0.619271995
step: 9550 | loss: 0.616430559
step: 9560 | loss: 0.613589124
step: 9570 | loss: 0.610747688
step: 9580 | loss: 0.607906253
step: 9590 | loss: 0.605064817
step: 9600 | loss: 0.602223381
step: 9610 | loss: 0.599381946
step: 9620 | loss: 0.596540510
step: 9630 | loss: 0.593699075
step: 9640 | loss: 0.590857639
step: 9650 | loss: 0.588016204
step: 9660 | loss: 0.585174768
step: 9670 | loss: 0.582333332
step: 9680 | loss: 0.579491897
step: 9690 | loss: 0.576650461
step: 9700 | loss: 0.573809026
step: 9710 | loss: 0.570967590
step: 9720 | loss: 0.568126155
step: 9730 | loss: 0.565284719
step: 9740 | loss: 0.562443283
step: 9750 | loss: 0.559601848
step: 9760 | loss: 0.556760412
step: 9770 | loss: 0.553918977
step: 9780 | loss: 0.551077541
step: 9790 | loss: 0.548236106
step: 9800 | loss: 0.545394670
step: 9810 | loss: 0.542553234
step: 9820 | loss: 0.539711799
step: 9830 | loss: 0.536870363
step: 9840 | loss: 0.534028928
step: 9850 | loss: 0.531187492
step: 9860 | loss: 0.528346056
step: 9870 | loss: 0.525504621
step: 9880 | loss: 0.522663185
step: 9890 | loss: 0.519821750
step: 9900 | loss: 0.516980314
step: 9910 | loss: 0.514138879
step: 9920 | loss: 0.511297443
step: 9930 | loss: 0.508456007
step: 9940 | loss: 0.505614572
step: 9950 | loss: 0.502773136
step: 9960 | loss: 0.499931701
step: 9970 | loss: 0.497090265
step: 9980 | loss: 0.494248830
step: 9990 | loss: 0.491407394
step: 10000 | loss: 0.488565958
step: 10010 | loss: 0.485724523
step: 10020 | loss: 0.482883087
step: 10030 | loss: 0.480041652
step: 10040 | loss: 0.477200216
step: 10050 | loss: 0.474358780
step: 10060 | loss: 0.471517345
step: 10070 | loss: 0.468675909
step: 10080 | loss: 0.465834474
step: 10090 | loss: 0.462993038
step: 10100 | loss: 0.460151603
step: 10110 | loss: 0.457310167
step: 10120 | loss: 0.454468731
step: 10130 | loss: 0.451627296
step: 10140 | loss: 0.448785860
step: 10150 | loss: 0.445944425
step: 10160 | loss: 0.443102989
step: 10170 | loss: 0.440261554
step: 10180 | loss: 0.437420118
step: 10190 | loss: 0.434578682
step: 10200 | loss: 0.431737247
step: 10210 | loss: 0.428895811
step: 10220 | loss: 0.426054376
step: 10230 | loss: 0.423212940
step: 10240 | loss: 0.420371505
step: 10250 | loss: 0.417530069
step: 10260 | loss: 0.414688633
step: 10270 | loss: 0.411847198
step: 10280 | loss: 0.409005762
step: 10290 | loss: 0.406164327
step: 10300 | loss: 0.403322891
step: 10310 | loss: 0.400481455
step: 10320 | loss: 0.397640020
step: 10330 | loss: 0.394798584
step: 10340 | loss: 0.391957149
step: 10350 | loss: 0.389115713
step: 10360 | loss: 0.386274278
step: 10370 | loss: 0.383432842
step: 10380 | loss: 0.380591406
step: 10390 | loss: 0.377749971
step: 10400 | loss: 0.374908535
step: 10410 | loss: 0.372067100
step: 10420 | loss: 0.369225664
step: 10430 | loss: 0.366384229
step: 10440 | loss: 0.363542793
step: 10450 | loss: 0.360701357
step: 10460 | loss: 0.357859922
step: 10470 | loss: 0.355018486
step: 10480 | loss: 0.352177051
step: 10490 | loss: 0.349335615
step: 10500 | loss: 0.346494180
step: 10510 | loss: 0.343652744
step: 10520 | loss: 0.340811308
step: 10530 | loss: 0.337969873
step: 10540 | loss: 0.335128437
step: 10550 | loss: 0.332287002
step: 10560 | loss: 0.329445566
step: 10570 | loss: 0.326604130
step: 10580 | loss: 0.323762695
step: 10590 | loss: 0.320921259
step: 10600 | loss: 0.318079824
step: 10610 | loss: 0.315238388
step: 10620 | loss: 0.312396953
step: 10630 | loss: 0.309555517
step: 10640 | loss: 0.306714081
step: 10650 | loss: 0.303872646
step: 10660 | loss: 0.301031210
step: 10670 | loss: 0.298189775
step: 10680 | loss: 0.295348339
step: 10690 | loss: 0.292506904
step: 10700 | loss: 0.289665468
step: 10710 | loss: 0.286824032
step: 10720 | loss: 0.283982597
step: 10730 | loss: 0.281141161
step: 10740 | loss: 0.278299726
step: 10750 | loss: 0.275458290
step: 10760 | loss: 0.272616854
step: 10770 | loss: 0.269775419
step: 10780 | loss: 0.266933983
step: 10790 | loss: 0.264092548
step: 10800 | loss: 0.261251112
step: 10810 | loss: 0.258409677
step: 10820 | loss: 0.255568241
step: 10830 | loss: 0.252726805
step: 10840 | loss: 0.249885370
step: 10850 | loss: 0.247043934
step: 10860 | loss: 0.244202499
step: 10870 | loss: 0.241361063
step: 10880 | loss: 0.238519628
step: 10890 | loss: 0.235678192
step: 10900 | loss: 0.232836756
step: 10910 | loss: 0.229995321
step: 10920 | loss: 0.227153885
step: 10930 | loss: 0.224312450
step: 10940 | loss: 0.221471014
step: 10950 | loss: 0.218629579
step: 10960 | loss: 0.215788143
step: 10970 | loss: 0.212946707
step: 10980 | loss: 0.210105272
step: 10990 | loss: 0.207263836
step: 11000 | loss: 0.204422401
step: 11010 | loss: 0.201580965
step: 11020 | loss: 0.198739529
step: 11030 | loss: 0.195898094
step: 11040 | loss: 0.193056658
step: 11050 | loss: 0.190215223
step: 11060 | loss: 0.187373787
step: 11070 | loss: 0.184532352
step: 11080 | loss: 0.181690916
step: 11090 | loss: 0.178849480
step: 11100 | loss: 0.176008045
step: 11110 | loss: 0.173166609
step: 11120 | loss: 0.170325174
step: 11130 | loss: 0.167483738
step: 11140 | loss: 0.164642303
step: 11150 | loss: 0.161800867
step: 11160 | loss: 0.158959431
step: 11170 | loss: 0.156117996
step: 11180 | loss: 0.153276560
step: 11190 | loss: 0.150435125
step: 11200 | loss: 0.147593689
step: 11210 | loss: 0.144752253
step: 11220 | loss: 0.141910818
step: 11230 | loss: 0.139069382
step: 11240 | loss: 0.136227947
step: 11250 | loss: 0.133386511
step: 11260 | loss: 0.130545076
step: 11270 | loss: 0.127703640
step: 11280 | loss: 0.124862204
step: 11290 | loss: 0.122020769
step: 11300 | loss: 0.119179333
step: 11310 | loss: 0.116337898
step: 11320 | loss: 0.113496462
step: 11330 | loss: 0.110655027
step: 11340 | loss: 0.107813591
step: 11350 | loss: 0.104972155
step: 11360 | loss: 0.102130720
step: 11370 | loss: 0.099289284
step: 11380 | loss: 0.096447849
step: 11390 | loss: 0.093606413
step: 11400 | loss: 0.090764978
step: 11410 | loss: 0.087923542
step: 11420 | loss: 0.085082106
step: 11430 | loss: 0.082240671
step: 11440 | loss: 0.079399235
step: 11450 | loss: 0.076557800
step: 11460 | loss: 0.073716364
step: 11470 | loss: 0.070874928
step: 11480 | loss: 0.068033493
step: 11490 | loss: 0.065192057
step: 11500 | loss: 0.062350622
step: 11510 | loss: 0.059509186
step: 11520 | loss: 0.056667751
step: 11530 | loss: 0.053826315
step: 11540 | loss: 0.050984879
step: 11550 | loss: 0.048143444
step: 11560 | loss: 0.045302008
step: 11570 | loss: 0.042460573
step: 11580 | loss: 0.039619137
step: 11590 | loss: 0.036777702
step: 11600 | loss: 0.033936266
step: 11610 | loss: 0.031094830
step: 11620 | loss: 0.028253395
step: 11630 | loss: 0.025411959
step: 11640 | loss: 0.022570524
step: 11650 | loss: 0.019729088
step: 11660 | loss: 0.016887653
step: 11670 | loss: 0.014046217
step: 11680 | loss: 0.011204781
step: 11690 | loss: 0.008363346
step: 11700 | loss: 0.005521910
step: 11710 | loss: 0.002680475
- final loss: 0.000976
- (cd _build/default/examples/opt && ./adam.exe)
-
step: 0 | loss: 3.325826443
step: 10 | loss: 3.322962112
step: 20 | loss: 3.319781599
step: 30 | loss: 3.316603227
step: 40 | loss: 3.313427051
step: 50 | loss: 3.310253067
step: 60 | loss: 3.307081201
step: 70 | loss: 3.303911346
step: 80 | loss: 3.300743385
step: 90 | loss: 3.297577198
step: 100 | loss: 3.294412666
step: 110 | loss: 3.291249673
step: 120 | loss: 3.288088115
step: 130 | loss: 3.284927892
step: 140 | loss: 3.281768916
step: 150 | loss: 3.278611109
step: 160 | loss: 3.275454403
step: 170 | loss: 3.272298739
step: 180 | loss: 3.269144068
step: 190 | loss: 3.265990351
step: 200 | loss: 3.262837556
step: 210 | loss: 3.259685656
step: 220 | loss: 3.256534633
step: 230 | loss: 3.253384471
step: 240 | loss: 3.250235159
step: 250 | loss: 3.247086690
step: 260 | loss: 3.243939058
step: 270 | loss: 3.240792260
step: 280 | loss: 3.237646292
step: 290 | loss: 3.234501154
step: 300 | loss: 3.231356846
step: 310 | loss: 3.228213366
step: 320 | loss: 3.225070716
step: 330 | loss: 3.221928895
step: 340 | loss: 3.218787905
step: 350 | loss: 3.215647745
step: 360 | loss: 3.212508417
step: 370 | loss: 3.209369922
step: 380 | loss: 3.206232260
step: 390 | loss: 3.203095433
step: 400 | loss: 3.199959440
step: 410 | loss: 3.196824284
step: 420 | loss: 3.193689964
step: 430 | loss: 3.190556482
step: 440 | loss: 3.187423840
step: 450 | loss: 3.184292037
step: 460 | loss: 3.181161074
step: 470 | loss: 3.178030954
step: 480 | loss: 3.174901675
step: 490 | loss: 3.171773241
step: 500 | loss: 3.168645651
step: 510 | loss: 3.165518906
step: 520 | loss: 3.162393008
step: 530 | loss: 3.159267958
step: 540 | loss: 3.156143756
step: 550 | loss: 3.153020403
step: 560 | loss: 3.149897901
step: 570 | loss: 3.146776251
step: 580 | loss: 3.143655453
step: 590 | loss: 3.140535508
step: 600 | loss: 3.137416419
step: 610 | loss: 3.134298184
step: 620 | loss: 3.131180807
step: 630 | loss: 3.128064287
step: 640 | loss: 3.124948626
step: 650 | loss: 3.121833826
step: 660 | loss: 3.118719886
step: 670 | loss: 3.115606808
step: 680 | loss: 3.112494593
step: 690 | loss: 3.109383242
step: 700 | loss: 3.106272757
step: 710 | loss: 3.103163138
step: 720 | loss: 3.100054387
step: 730 | loss: 3.096946505
step: 740 | loss: 3.093839492
step: 750 | loss: 3.090733350
step: 760 | loss: 3.087628081
step: 770 | loss: 3.084523685
step: 780 | loss: 3.081420163
step: 790 | loss: 3.078317517
step: 800 | loss: 3.075215747
step: 810 | loss: 3.072114855
step: 820 | loss: 3.069014843
step: 830 | loss: 3.065915711
step: 840 | loss: 3.062817460
step: 850 | loss: 3.059720092
step: 860 | loss: 3.056623609
step: 870 | loss: 3.053528010
step: 880 | loss: 3.050433297
step: 890 | loss: 3.047339473
step: 900 | loss: 3.044246537
step: 910 | loss: 3.041154491
step: 920 | loss: 3.038063336
step: 930 | loss: 3.034973075
step: 940 | loss: 3.031883707
step: 950 | loss: 3.028795234
step: 960 | loss: 3.025707658
step: 970 | loss: 3.022620979
step: 980 | loss: 3.019535200
step: 990 | loss: 3.016450320
step: 1000 | loss: 3.013366343
step: 1010 | loss: 3.010283268
step: 1020 | loss: 3.007201098
step: 1030 | loss: 3.004119834
step: 1040 | loss: 3.001039476
step: 1050 | loss: 2.997960027
step: 1060 | loss: 2.994881487
step: 1070 | loss: 2.991803859
step: 1080 | loss: 2.988727142
step: 1090 | loss: 2.985651340
step: 1100 | loss: 2.982576453
step: 1110 | loss: 2.979502482
step: 1120 | loss: 2.976429430
step: 1130 | loss: 2.973357296
step: 1140 | loss: 2.970286084
step: 1150 | loss: 2.967215793
step: 1160 | loss: 2.964146427
step: 1170 | loss: 2.961077985
step: 1180 | loss: 2.958010470
step: 1190 | loss: 2.954943883
step: 1200 | loss: 2.951878225
step: 1210 | loss: 2.948813498
step: 1220 | loss: 2.945749704
step: 1230 | loss: 2.942686843
step: 1240 | loss: 2.939624918
step: 1250 | loss: 2.936563930
step: 1260 | loss: 2.933503879
step: 1270 | loss: 2.930444769
step: 1280 | loss: 2.927386600
step: 1290 | loss: 2.924329374
step: 1300 | loss: 2.921273092
step: 1310 | loss: 2.918217757
step: 1320 | loss: 2.915163369
step: 1330 | loss: 2.912109929
step: 1340 | loss: 2.909057441
step: 1350 | loss: 2.906005904
step: 1360 | loss: 2.902955322
step: 1370 | loss: 2.899905695
step: 1380 | loss: 2.896857024
step: 1390 | loss: 2.893809313
step: 1400 | loss: 2.890762561
step: 1410 | loss: 2.887716771
step: 1420 | loss: 2.884671945
step: 1430 | loss: 2.881628084
step: 1440 | loss: 2.878585189
step: 1450 | loss: 2.875543263
step: 1460 | loss: 2.872502307
step: 1470 | loss: 2.869462322
step: 1480 | loss: 2.866423311
step: 1490 | loss: 2.863385274
step: 1500 | loss: 2.860348215
step: 1510 | loss: 2.857312134
step: 1520 | loss: 2.854277033
step: 1530 | loss: 2.851242913
step: 1540 | loss: 2.848209777
step: 1550 | loss: 2.845177627
step: 1560 | loss: 2.842146463
step: 1570 | loss: 2.839116289
step: 1580 | loss: 2.836087104
step: 1590 | loss: 2.833058912
step: 1600 | loss: 2.830031714
step: 1610 | loss: 2.827005512
step: 1620 | loss: 2.823980307
step: 1630 | loss: 2.820956102
step: 1640 | loss: 2.817932897
step: 1650 | loss: 2.814910696
step: 1660 | loss: 2.811889500
step: 1670 | loss: 2.808869310
step: 1680 | loss: 2.805850128
step: 1690 | loss: 2.802831957
step: 1700 | loss: 2.799814798
step: 1710 | loss: 2.796798653
step: 1720 | loss: 2.793783524
step: 1730 | loss: 2.790769412
step: 1740 | loss: 2.787756320
step: 1750 | loss: 2.784744250
step: 1760 | loss: 2.781733203
step: 1770 | loss: 2.778723181
step: 1780 | loss: 2.775714187
step: 1790 | loss: 2.772706221
step: 1800 | loss: 2.769699287
step: 1810 | loss: 2.766693385
step: 1820 | loss: 2.763688519
step: 1830 | loss: 2.760684689
step: 1840 | loss: 2.757681898
step: 1850 | loss: 2.754680148
step: 1860 | loss: 2.751679441
step: 1870 | loss: 2.748679778
step: 1880 | loss: 2.745681163
step: 1890 | loss: 2.742683596
step: 1900 | loss: 2.739687080
step: 1910 | loss: 2.736691616
step: 1920 | loss: 2.733697208
step: 1930 | loss: 2.730703856
step: 1940 | loss: 2.727711563
step: 1950 | loss: 2.724720331
step: 1960 | loss: 2.721730162
step: 1970 | loss: 2.718741059
step: 1980 | loss: 2.715753022
step: 1990 | loss: 2.712766054
step: 2000 | loss: 2.709780158
step: 2010 | loss: 2.706795336
step: 2020 | loss: 2.703811589
step: 2030 | loss: 2.700828919
step: 2040 | loss: 2.697847330
step: 2050 | loss: 2.694866822
step: 2060 | loss: 2.691887398
step: 2070 | loss: 2.688909061
step: 2080 | loss: 2.685931812
step: 2090 | loss: 2.682955653
step: 2100 | loss: 2.679980587
step: 2110 | loss: 2.677006616
step: 2120 | loss: 2.674033742
step: 2130 | loss: 2.671061968
step: 2140 | loss: 2.668091294
step: 2150 | loss: 2.665121725
step: 2160 | loss: 2.662153262
step: 2170 | loss: 2.659185906
step: 2180 | loss: 2.656219661
step: 2190 | loss: 2.653254529
step: 2200 | loss: 2.650290512
step: 2210 | loss: 2.647327612
step: 2220 | loss: 2.644365831
step: 2230 | loss: 2.641405172
step: 2240 | loss: 2.638445637
step: 2250 | loss: 2.635487229
step: 2260 | loss: 2.632529949
step: 2270 | loss: 2.629573800
step: 2280 | loss: 2.626618784
step: 2290 | loss: 2.623664904
step: 2300 | loss: 2.620712162
step: 2310 | loss: 2.617760560
step: 2320 | loss: 2.614810101
step: 2330 | loss: 2.611860787
step: 2340 | loss: 2.608912620
step: 2350 | loss: 2.605965603
step: 2360 | loss: 2.603019739
step: 2370 | loss: 2.600075028
step: 2380 | loss: 2.597131475
step: 2390 | loss: 2.594189081
step: 2400 | loss: 2.591247849
step: 2410 | loss: 2.588307781
step: 2420 | loss: 2.585368880
step: 2430 | loss: 2.582431148
step: 2440 | loss: 2.579494588
step: 2450 | loss: 2.576559202
step: 2460 | loss: 2.573624992
step: 2470 | loss: 2.570691961
step: 2480 | loss: 2.567760112
step: 2490 | loss: 2.564829447
step: 2500 | loss: 2.561899968
step: 2510 | loss: 2.558971678
step: 2520 | loss: 2.556044580
step: 2530 | loss: 2.553118676
step: 2540 | loss: 2.550193968
step: 2550 | loss: 2.547270459
step: 2560 | loss: 2.544348153
step: 2570 | loss: 2.541427050
step: 2580 | loss: 2.538507154
step: 2590 | loss: 2.535588468
step: 2600 | loss: 2.532670994
step: 2610 | loss: 2.529754734
step: 2620 | loss: 2.526839691
step: 2630 | loss: 2.523925868
step: 2640 | loss: 2.521013268
step: 2650 | loss: 2.518101893
step: 2660 | loss: 2.515191745
step: 2670 | loss: 2.512282827
step: 2680 | loss: 2.509375143
step: 2690 | loss: 2.506468694
step: 2700 | loss: 2.503563483
step: 2710 | loss: 2.500659514
step: 2720 | loss: 2.497756788
step: 2730 | loss: 2.494855308
step: 2740 | loss: 2.491955077
step: 2750 | loss: 2.489056098
step: 2760 | loss: 2.486158373
step: 2770 | loss: 2.483261906
step: 2780 | loss: 2.480366698
step: 2790 | loss: 2.477472753
step: 2800 | loss: 2.474580073
step: 2810 | loss: 2.471688661
step: 2820 | loss: 2.468798520
step: 2830 | loss: 2.465909653
step: 2840 | loss: 2.463022062
step: 2850 | loss: 2.460135750
step: 2860 | loss: 2.457250720
step: 2870 | loss: 2.454366975
step: 2880 | loss: 2.451484518
step: 2890 | loss: 2.448603350
step: 2900 | loss: 2.445723476
step: 2910 | loss: 2.442844898
step: 2920 | loss: 2.439967619
step: 2930 | loss: 2.437091641
step: 2940 | loss: 2.434216967
step: 2950 | loss: 2.431343601
step: 2960 | loss: 2.428471546
step: 2970 | loss: 2.425600803
step: 2980 | loss: 2.422731376
step: 2990 | loss: 2.419863268
step: 3000 | loss: 2.416996482
step: 3010 | loss: 2.414131020
step: 3020 | loss: 2.411266886
step: 3030 | loss: 2.408404082
step: 3040 | loss: 2.405542611
step: 3050 | loss: 2.402682477
step: 3060 | loss: 2.399823681
step: 3070 | loss: 2.396966228
step: 3080 | loss: 2.394110120
step: 3090 | loss: 2.391255360
step: 3100 | loss: 2.388401951
step: 3110 | loss: 2.385549895
step: 3120 | loss: 2.382699197
step: 3130 | loss: 2.379849858
step: 3140 | loss: 2.377001882
step: 3150 | loss: 2.374155272
step: 3160 | loss: 2.371310031
step: 3170 | loss: 2.368466161
step: 3180 | loss: 2.365623666
step: 3190 | loss: 2.362782550
step: 3200 | loss: 2.359942813
step: 3210 | loss: 2.357104461
step: 3220 | loss: 2.354267496
step: 3230 | loss: 2.351431920
step: 3240 | loss: 2.348597738
step: 3250 | loss: 2.345764951
step: 3260 | loss: 2.342933564
step: 3270 | loss: 2.340103578
step: 3280 | loss: 2.337274998
step: 3290 | loss: 2.334447826
step: 3300 | loss: 2.331622065
step: 3310 | loss: 2.328797719
step: 3320 | loss: 2.325974790
step: 3330 | loss: 2.323153282
step: 3340 | loss: 2.320333197
step: 3350 | loss: 2.317514540
step: 3360 | loss: 2.314697312
step: 3370 | loss: 2.311881517
step: 3380 | loss: 2.309067158
step: 3390 | loss: 2.306254239
step: 3400 | loss: 2.303442762
step: 3410 | loss: 2.300632730
step: 3420 | loss: 2.297824147
step: 3430 | loss: 2.295017016
step: 3440 | loss: 2.292211339
step: 3450 | loss: 2.289407121
step: 3460 | loss: 2.286604364
step: 3470 | loss: 2.283803072
step: 3480 | loss: 2.281003247
step: 3490 | loss: 2.278204892
step: 3500 | loss: 2.275408012
step: 3510 | loss: 2.272612609
step: 3520 | loss: 2.269818686
step: 3530 | loss: 2.267026246
step: 3540 | loss: 2.264235293
step: 3550 | loss: 2.261445830
step: 3560 | loss: 2.258657860
step: 3570 | loss: 2.255871386
step: 3580 | loss: 2.253086412
step: 3590 | loss: 2.250302940
step: 3600 | loss: 2.247520974
step: 3610 | loss: 2.244740518
step: 3620 | loss: 2.241961573
step: 3630 | loss: 2.239184144
step: 3640 | loss: 2.236408234
step: 3650 | loss: 2.233633846
step: 3660 | loss: 2.230860983
step: 3670 | loss: 2.228089648
step: 3680 | loss: 2.225319845
step: 3690 | loss: 2.222551577
step: 3700 | loss: 2.219784847
step: 3710 | loss: 2.217019659
step: 3720 | loss: 2.214256015
step: 3730 | loss: 2.211493919
step: 3740 | loss: 2.208733374
step: 3750 | loss: 2.205974383
step: 3760 | loss: 2.203216950
step: 3770 | loss: 2.200461078
step: 3780 | loss: 2.197706770
step: 3790 | loss: 2.194954030
step: 3800 | loss: 2.192202860
step: 3810 | loss: 2.189453264
step: 3820 | loss: 2.186705246
step: 3830 | loss: 2.183958807
step: 3840 | loss: 2.181213953
step: 3850 | loss: 2.178470685
step: 3860 | loss: 2.175729008
step: 3870 | loss: 2.172988924
step: 3880 | loss: 2.170250437
step: 3890 | loss: 2.167513550
step: 3900 | loss: 2.164778266
step: 3910 | loss: 2.162044589
step: 3920 | loss: 2.159312522
step: 3930 | loss: 2.156582067
step: 3940 | loss: 2.153853229
step: 3950 | loss: 2.151126011
step: 3960 | loss: 2.148400416
step: 3970 | loss: 2.145676446
step: 3980 | loss: 2.142954107
step: 3990 | loss: 2.140233399
step: 4000 | loss: 2.137514328
step: 4010 | loss: 2.134796896
step: 4020 | loss: 2.132081107
step: 4030 | loss: 2.129366963
step: 4040 | loss: 2.126654468
step: 4050 | loss: 2.123943625
step: 4060 | loss: 2.121234438
step: 4070 | loss: 2.118526909
step: 4080 | loss: 2.115821043
step: 4090 | loss: 2.113116842
step: 4100 | loss: 2.110414309
step: 4110 | loss: 2.107713447
step: 4120 | loss: 2.105014261
step: 4130 | loss: 2.102316753
step: 4140 | loss: 2.099620926
step: 4150 | loss: 2.096926784
step: 4160 | loss: 2.094234330
step: 4170 | loss: 2.091543566
step: 4180 | loss: 2.088854497
step: 4190 | loss: 2.086167125
step: 4200 | loss: 2.083481454
step: 4210 | loss: 2.080797487
step: 4220 | loss: 2.078115227
step: 4230 | loss: 2.075434677
step: 4240 | loss: 2.072755841
step: 4250 | loss: 2.070078721
step: 4260 | loss: 2.067403321
step: 4270 | loss: 2.064729643
step: 4280 | loss: 2.062057692
step: 4290 | loss: 2.059387471
step: 4300 | loss: 2.056718981
step: 4310 | loss: 2.054052227
step: 4320 | loss: 2.051387212
step: 4330 | loss: 2.048723939
step: 4340 | loss: 2.046062410
step: 4350 | loss: 2.043402630
step: 4360 | loss: 2.040744601
step: 4370 | loss: 2.038088326
step: 4380 | loss: 2.035433808
step: 4390 | loss: 2.032781051
step: 4400 | loss: 2.030130058
step: 4410 | loss: 2.027480830
step: 4420 | loss: 2.024833373
step: 4430 | loss: 2.022187688
step: 4440 | loss: 2.019543779
step: 4450 | loss: 2.016901649
step: 4460 | loss: 2.014261300
step: 4470 | loss: 2.011622737
step: 4480 | loss: 2.008985961
step: 4490 | loss: 2.006350976
step: 4500 | loss: 2.003717784
step: 4510 | loss: 2.001086390
step: 4520 | loss: 1.998456795
step: 4530 | loss: 1.995829002
step: 4540 | loss: 1.993203016
step: 4550 | loss: 1.990578837
step: 4560 | loss: 1.987956470
step: 4570 | loss: 1.985335918
step: 4580 | loss: 1.982717183
step: 4590 | loss: 1.980100267
step: 4600 | loss: 1.977485175
step: 4610 | loss: 1.974871908
step: 4620 | loss: 1.972260470
step: 4630 | loss: 1.969650864
step: 4640 | loss: 1.967043091
step: 4650 | loss: 1.964437156
step: 4660 | loss: 1.961833061
step: 4670 | loss: 1.959230808
step: 4680 | loss: 1.956630400
step: 4690 | loss: 1.954031840
step: 4700 | loss: 1.951435132
step: 4710 | loss: 1.948840276
step: 4720 | loss: 1.946247277
step: 4730 | loss: 1.943656137
step: 4740 | loss: 1.941066858
step: 4750 | loss: 1.938479443
step: 4760 | loss: 1.935893895
step: 4770 | loss: 1.933310216
step: 4780 | loss: 1.930728409
step: 4790 | loss: 1.928148477
step: 4800 | loss: 1.925570421
step: 4810 | loss: 1.922994245
step: 4820 | loss: 1.920419951
step: 4830 | loss: 1.917847542
step: 4840 | loss: 1.915277020
step: 4850 | loss: 1.912708387
step: 4860 | loss: 1.910141646
step: 4870 | loss: 1.907576799
step: 4880 | loss: 1.905013849
step: 4890 | loss: 1.902452798
step: 4900 | loss: 1.899893648
step: 4910 | loss: 1.897336402
step: 4920 | loss: 1.894781062
step: 4930 | loss: 1.892227630
step: 4940 | loss: 1.889676109
step: 4950 | loss: 1.887126501
step: 4960 | loss: 1.884578807
step: 4970 | loss: 1.882033031
step: 4980 | loss: 1.879489174
step: 4990 | loss: 1.876947238
step: 5000 | loss: 1.874407226
step: 5010 | loss: 1.871869139
step: 5020 | loss: 1.869332980
step: 5030 | loss: 1.866798751
step: 5040 | loss: 1.864266454
step: 5050 | loss: 1.861736090
step: 5060 | loss: 1.859207662
step: 5070 | loss: 1.856681172
step: 5080 | loss: 1.854156621
step: 5090 | loss: 1.851634012
step: 5100 | loss: 1.849113345
step: 5110 | loss: 1.846594624
step: 5120 | loss: 1.844077850
step: 5130 | loss: 1.841563024
step: 5140 | loss: 1.839050148
step: 5150 | loss: 1.836539225
step: 5160 | loss: 1.834030255
step: 5170 | loss: 1.831523241
step: 5180 | loss: 1.829018183
step: 5190 | loss: 1.826515084
step: 5200 | loss: 1.824013945
step: 5210 | loss: 1.821514767
step: 5220 | loss: 1.819017553
step: 5230 | loss: 1.816522303
step: 5240 | loss: 1.814029019
step: 5250 | loss: 1.811537702
step: 5260 | loss: 1.809048354
step: 5270 | loss: 1.806560976
step: 5280 | loss: 1.804075569
step: 5290 | loss: 1.801592135
step: 5300 | loss: 1.799110674
step: 5310 | loss: 1.796631188
step: 5320 | loss: 1.794153678
step: 5330 | loss: 1.791678145
step: 5340 | loss: 1.789204591
step: 5350 | loss: 1.786733016
step: 5360 | loss: 1.784263420
step: 5370 | loss: 1.781795806
step: 5380 | loss: 1.779330175
step: 5390 | loss: 1.776866526
step: 5400 | loss: 1.774404860
step: 5410 | loss: 1.771945180
step: 5420 | loss: 1.769487485
step: 5430 | loss: 1.767031776
step: 5440 | loss: 1.764578053
step: 5450 | loss: 1.762126319
step: 5460 | loss: 1.759676572
step: 5470 | loss: 1.757228814
step: 5480 | loss: 1.754783044
step: 5490 | loss: 1.752339265
step: 5500 | loss: 1.749897475
step: 5510 | loss: 1.747457676
step: 5520 | loss: 1.745019868
step: 5530 | loss: 1.742584051
step: 5540 | loss: 1.740150225
step: 5550 | loss: 1.737718391
step: 5560 | loss: 1.735288548
step: 5570 | loss: 1.732860697
step: 5580 | loss: 1.730434839
step: 5590 | loss: 1.728010972
step: 5600 | loss: 1.725589097
step: 5610 | loss: 1.723169215
step: 5620 | loss: 1.720751324
step: 5630 | loss: 1.718335425
step: 5640 | loss: 1.715921517
step: 5650 | loss: 1.713509601
step: 5660 | loss: 1.711099676
step: 5670 | loss: 1.708691742
step: 5680 | loss: 1.706285798
step: 5690 | loss: 1.703881843
step: 5700 | loss: 1.701479879
step: 5710 | loss: 1.699079903
step: 5720 | loss: 1.696681916
step: 5730 | loss: 1.694285916
step: 5740 | loss: 1.691891904
step: 5750 | loss: 1.689499878
step: 5760 | loss: 1.687109837
step: 5770 | loss: 1.684721782
step: 5780 | loss: 1.682335710
step: 5790 | loss: 1.679951621
step: 5800 | loss: 1.677569515
step: 5810 | loss: 1.675189389
step: 5820 | loss: 1.672811244
step: 5830 | loss: 1.670435077
step: 5840 | loss: 1.668060888
step: 5850 | loss: 1.665688676
step: 5860 | loss: 1.663318438
step: 5870 | loss: 1.660950175
step: 5880 | loss: 1.658583884
step: 5890 | loss: 1.656219564
step: 5900 | loss: 1.653857214
step: 5910 | loss: 1.651496832
step: 5920 | loss: 1.649138416
step: 5930 | loss: 1.646781965
step: 5940 | loss: 1.644427477
step: 5950 | loss: 1.642074950
step: 5960 | loss: 1.639724384
step: 5970 | loss: 1.637375774
step: 5980 | loss: 1.635029121
step: 5990 | loss: 1.632684421
step: 6000 | loss: 1.630341673
step: 6010 | loss: 1.628000875
step: 6020 | loss: 1.625662025
step: 6030 | loss: 1.623325120
step: 6040 | loss: 1.620990158
step: 6050 | loss: 1.618657137
step: 6060 | loss: 1.616326055
step: 6070 | loss: 1.613996908
step: 6080 | loss: 1.611669696
step: 6090 | loss: 1.609344415
step: 6100 | loss: 1.607021062
step: 6110 | loss: 1.604699635
step: 6120 | loss: 1.602380132
step: 6130 | loss: 1.600062550
step: 6140 | loss: 1.597746885
step: 6150 | loss: 1.595433135
step: 6160 | loss: 1.593121297
step: 6170 | loss: 1.590811369
step: 6180 | loss: 1.588503347
step: 6190 | loss: 1.586197228
step: 6200 | loss: 1.583893009
step: 6210 | loss: 1.581590686
step: 6220 | loss: 1.579290258
step: 6230 | loss: 1.576991720
step: 6240 | loss: 1.574695069
step: 6250 | loss: 1.572400302
step: 6260 | loss: 1.570107415
step: 6270 | loss: 1.567816405
step: 6280 | loss: 1.565527268
step: 6290 | loss: 1.563240001
step: 6300 | loss: 1.560954600
step: 6310 | loss: 1.558671061
step: 6320 | loss: 1.556389381
step: 6330 | loss: 1.554109556
step: 6340 | loss: 1.551831583
step: 6350 | loss: 1.549555456
step: 6360 | loss: 1.547281172
step: 6370 | loss: 1.545008728
step: 6380 | loss: 1.542738119
step: 6390 | loss: 1.540469341
step: 6400 | loss: 1.538202390
step: 6410 | loss: 1.535937262
step: 6420 | loss: 1.533673952
step: 6430 | loss: 1.531412456
step: 6440 | loss: 1.529152771
step: 6450 | loss: 1.526894891
step: 6460 | loss: 1.524638812
step: 6470 | loss: 1.522384530
step: 6480 | loss: 1.520132040
step: 6490 | loss: 1.517881338
step: 6500 | loss: 1.515632418
step: 6510 | loss: 1.513385277
step: 6520 | loss: 1.511139910
step: 6530 | loss: 1.508896311
step: 6540 | loss: 1.506654477
step: 6550 | loss: 1.504414402
step: 6560 | loss: 1.502176081
step: 6570 | loss: 1.499939510
step: 6580 | loss: 1.497704683
step: 6590 | loss: 1.495471596
step: 6600 | loss: 1.493240243
step: 6610 | loss: 1.491010620
step: 6620 | loss: 1.488782721
step: 6630 | loss: 1.486556542
step: 6640 | loss: 1.484332076
step: 6650 | loss: 1.482109320
step: 6660 | loss: 1.479888266
step: 6670 | loss: 1.477668912
step: 6680 | loss: 1.475451250
step: 6690 | loss: 1.473235275
step: 6700 | loss: 1.471020983
step: 6710 | loss: 1.468808367
step: 6720 | loss: 1.466597423
step: 6730 | loss: 1.464388145
step: 6740 | loss: 1.462180527
step: 6750 | loss: 1.459974563
step: 6760 | loss: 1.457770249
step: 6770 | loss: 1.455567578
step: 6780 | loss: 1.453366545
step: 6790 | loss: 1.451167144
step: 6800 | loss: 1.448969370
step: 6810 | loss: 1.446773216
step: 6820 | loss: 1.444578678
step: 6830 | loss: 1.442385748
step: 6840 | loss: 1.440194422
step: 6850 | loss: 1.438004694
step: 6860 | loss: 1.435816557
step: 6870 | loss: 1.433630007
step: 6880 | loss: 1.431445036
step: 6890 | loss: 1.429261639
step: 6900 | loss: 1.427079811
step: 6910 | loss: 1.424899544
step: 6920 | loss: 1.422720834
step: 6930 | loss: 1.420543674
step: 6940 | loss: 1.418368058
step: 6950 | loss: 1.416193981
step: 6960 | loss: 1.414021435
step: 6970 | loss: 1.411850416
step: 6980 | loss: 1.409680916
step: 6990 | loss: 1.407512931
step: 7000 | loss: 1.405346453
step: 7010 | loss: 1.403181476
step: 7020 | loss: 1.401017996
step: 7030 | loss: 1.398856004
step: 7040 | loss: 1.396695496
step: 7050 | loss: 1.394536465
step: 7060 | loss: 1.392378905
step: 7070 | loss: 1.390222809
step: 7080 | loss: 1.388068172
step: 7090 | loss: 1.385914987
step: 7100 | loss: 1.383763248
step: 7110 | loss: 1.381612949
step: 7120 | loss: 1.379464083
step: 7130 | loss: 1.377316645
step: 7140 | loss: 1.375170627
step: 7150 | loss: 1.373026025
step: 7160 | loss: 1.370882831
step: 7170 | loss: 1.368741039
step: 7180 | loss: 1.366600643
step: 7190 | loss: 1.364461637
step: 7200 | loss: 1.362324014
step: 7210 | loss: 1.360187769
step: 7220 | loss: 1.358052894
step: 7230 | loss: 1.355919383
step: 7240 | loss: 1.353787231
step: 7250 | loss: 1.351656431
step: 7260 | loss: 1.349526976
step: 7270 | loss: 1.347398861
step: 7280 | loss: 1.345272078
step: 7290 | loss: 1.343146622
step: 7300 | loss: 1.341022486
step: 7310 | loss: 1.338899664
step: 7320 | loss: 1.336778150
step: 7330 | loss: 1.334657937
step: 7340 | loss: 1.332539019
step: 7350 | loss: 1.330421390
step: 7360 | loss: 1.328305042
step: 7370 | loss: 1.326189971
step: 7380 | loss: 1.324076169
step: 7390 | loss: 1.321963631
step: 7400 | loss: 1.319852349
step: 7410 | loss: 1.317742318
step: 7420 | loss: 1.315633530
step: 7430 | loss: 1.313525981
step: 7440 | loss: 1.311419663
step: 7450 | loss: 1.309314570
step: 7460 | loss: 1.307210696
step: 7470 | loss: 1.305108034
step: 7480 | loss: 1.303006578
step: 7490 | loss: 1.300906321
step: 7500 | loss: 1.298807257
step: 7510 | loss: 1.296709381
step: 7520 | loss: 1.294612684
step: 7530 | loss: 1.292517162
step: 7540 | loss: 1.290422807
step: 7550 | loss: 1.288329613
step: 7560 | loss: 1.286237574
step: 7570 | loss: 1.284146683
step: 7580 | loss: 1.282056934
step: 7590 | loss: 1.279968320
step: 7600 | loss: 1.277880835
step: 7610 | loss: 1.275794473
step: 7620 | loss: 1.273709227
step: 7630 | loss: 1.271625091
step: 7640 | loss: 1.269542057
step: 7650 | loss: 1.267460121
step: 7660 | loss: 1.265379274
step: 7670 | loss: 1.263299511
step: 7680 | loss: 1.261220826
step: 7690 | loss: 1.259143211
step: 7700 | loss: 1.257066660
step: 7710 | loss: 1.254991166
step: 7720 | loss: 1.252916724
step: 7730 | loss: 1.250843326
step: 7740 | loss: 1.248770966
step: 7750 | loss: 1.246699637
step: 7760 | loss: 1.244629333
step: 7770 | loss: 1.242560047
step: 7780 | loss: 1.240491773
step: 7790 | loss: 1.238424503
step: 7800 | loss: 1.236358232
step: 7810 | loss: 1.234292952
step: 7820 | loss: 1.232228657
step: 7830 | loss: 1.230165340
step: 7840 | loss: 1.228102995
step: 7850 | loss: 1.226041614
step: 7860 | loss: 1.223981192
step: 7870 | loss: 1.221921720
step: 7880 | loss: 1.219863194
step: 7890 | loss: 1.217805605
step: 7900 | loss: 1.215748947
step: 7910 | loss: 1.213693213
step: 7920 | loss: 1.211638396
step: 7930 | loss: 1.209584490
step: 7940 | loss: 1.207531487
step: 7950 | loss: 1.205479382
step: 7960 | loss: 1.203428166
step: 7970 | loss: 1.201377833
step: 7980 | loss: 1.199328376
step: 7990 | loss: 1.197279789
step: 8000 | loss: 1.195232063
step: 8010 | loss: 1.193185193
step: 8020 | loss: 1.191139171
step: 8030 | loss: 1.189093990
step: 8040 | loss: 1.187049644
step: 8050 | loss: 1.185006125
step: 8060 | loss: 1.182963426
step: 8070 | loss: 1.180921540
step: 8080 | loss: 1.178880461
step: 8090 | loss: 1.176840180
step: 8100 | loss: 1.174800692
step: 8110 | loss: 1.172761988
step: 8120 | loss: 1.170724062
step: 8130 | loss: 1.168686907
step: 8140 | loss: 1.166650515
step: 8150 | loss: 1.164614880
step: 8160 | loss: 1.162579994
step: 8170 | loss: 1.160545850
step: 8180 | loss: 1.158512441
step: 8190 | loss: 1.156479759
step: 8200 | loss: 1.154447799
step: 8210 | loss: 1.152416551
step: 8220 | loss: 1.150386010
step: 8230 | loss: 1.148356168
step: 8240 | loss: 1.146327017
step: 8250 | loss: 1.144298551
step: 8260 | loss: 1.142270762
step: 8270 | loss: 1.140243644
step: 8280 | loss: 1.138217188
step: 8290 | loss: 1.136191388
step: 8300 | loss: 1.134166237
step: 8310 | loss: 1.132141727
step: 8320 | loss: 1.130117851
step: 8330 | loss: 1.128094602
step: 8340 | loss: 1.126071973
step: 8350 | loss: 1.124049956
step: 8360 | loss: 1.122028544
step: 8370 | loss: 1.120007731
step: 8380 | loss: 1.117987508
step: 8390 | loss: 1.115967869
step: 8400 | loss: 1.113948807
step: 8410 | loss: 1.111930315
step: 8420 | loss: 1.109912385
step: 8430 | loss: 1.107895010
step: 8440 | loss: 1.105878183
step: 8450 | loss: 1.103861898
step: 8460 | loss: 1.101846146
step: 8470 | loss: 1.099830922
step: 8480 | loss: 1.097816217
step: 8490 | loss: 1.095802026
step: 8500 | loss: 1.093788340
step: 8510 | loss: 1.091775154
step: 8520 | loss: 1.089762460
step: 8530 | loss: 1.087750251
step: 8540 | loss: 1.085738521
step: 8550 | loss: 1.083727262
step: 8560 | loss: 1.081716468
step: 8570 | loss: 1.079706132
step: 8580 | loss: 1.077696247
step: 8590 | loss: 1.075686807
step: 8600 | loss: 1.073677804
step: 8610 | loss: 1.071669233
step: 8620 | loss: 1.069661087
step: 8630 | loss: 1.067653358
step: 8640 | loss: 1.065646041
step: 8650 | loss: 1.063639129
step: 8660 | loss: 1.061632616
step: 8670 | loss: 1.059626495
step: 8680 | loss: 1.057620760
step: 8690 | loss: 1.055615405
step: 8700 | loss: 1.053610423
step: 8710 | loss: 1.051605808
step: 8720 | loss: 1.049601554
step: 8730 | loss: 1.047597655
step: 8740 | loss: 1.045594104
step: 8750 | loss: 1.043590897
step: 8760 | loss: 1.041588026
step: 8770 | loss: 1.039585487
step: 8780 | loss: 1.037583272
step: 8790 | loss: 1.035581377
step: 8800 | loss: 1.033579795
step: 8810 | loss: 1.031578522
step: 8820 | loss: 1.029577551
step: 8830 | loss: 1.027576876
step: 8840 | loss: 1.025576493
step: 8850 | loss: 1.023576396
step: 8860 | loss: 1.021576579
step: 8870 | loss: 1.019577037
step: 8880 | loss: 1.017577765
step: 8890 | loss: 1.015578758
step: 8900 | loss: 1.013580011
step: 8910 | loss: 1.011581519
step: 8920 | loss: 1.009583276
step: 8930 | loss: 1.007585277
step: 8940 | loss: 1.005587519
step: 8950 | loss: 1.003589995
step: 8960 | loss: 1.001592702
step: 8970 | loss: 0.999595635
step: 8980 | loss: 0.997598788
step: 8990 | loss: 0.995602159
step: 9000 | loss: 0.993605741
step: 9010 | loss: 0.991609531
step: 9020 | loss: 0.989613525
step: 9030 | loss: 0.987617718
step: 9040 | loss: 0.985622105
step: 9050 | loss: 0.983626684
step: 9060 | loss: 0.981631450
step: 9070 | loss: 0.979636398
step: 9080 | loss: 0.977641526
step: 9090 | loss: 0.975646829
step: 9100 | loss: 0.973652303
step: 9110 | loss: 0.971657945
step: 9120 | loss: 0.969663751
step: 9130 | loss: 0.967669717
step: 9140 | loss: 0.965675841
step: 9150 | loss: 0.963682118
step: 9160 | loss: 0.961688545
step: 9170 | loss: 0.959695120
step: 9180 | loss: 0.957701838
step: 9190 | loss: 0.955708696
step: 9200 | loss: 0.953715693
step: 9210 | loss: 0.951722823
step: 9220 | loss: 0.949730085
step: 9230 | loss: 0.947737476
step: 9240 | loss: 0.945744993
step: 9250 | loss: 0.943752632
step: 9260 | loss: 0.941760392
step: 9270 | loss: 0.939768269
step: 9280 | loss: 0.937776261
step: 9290 | loss: 0.935784366
step: 9300 | loss: 0.933792580
step: 9310 | loss: 0.931800903
step: 9320 | loss: 0.929809330
step: 9330 | loss: 0.927817860
step: 9340 | loss: 0.925826491
step: 9350 | loss: 0.923835221
step: 9360 | loss: 0.921844047
step: 9370 | loss: 0.919852967
step: 9380 | loss: 0.917861979
step: 9390 | loss: 0.915871081
step: 9400 | loss: 0.913880272
step: 9410 | loss: 0.911889549
step: 9420 | loss: 0.909898910
step: 9430 | loss: 0.907908355
step: 9440 | loss: 0.905917880
step: 9450 | loss: 0.903927485
step: 9460 | loss: 0.901937167
step: 9470 | loss: 0.899946926
step: 9480 | loss: 0.897956759
step: 9490 | loss: 0.895966665
step: 9500 | loss: 0.893976643
step: 9510 | loss: 0.891986690
step: 9520 | loss: 0.889996807
step: 9530 | loss: 0.888006991
step: 9540 | loss: 0.886017241
step: 9550 | loss: 0.884027555
step: 9560 | loss: 0.882037933
step: 9570 | loss: 0.880048374
step: 9580 | loss: 0.878058876
step: 9590 | loss: 0.876069437
step: 9600 | loss: 0.874080058
step: 9610 | loss: 0.872090736
step: 9620 | loss: 0.870101471
step: 9630 | loss: 0.868112262
step: 9640 | loss: 0.866123107
step: 9650 | loss: 0.864134007
step: 9660 | loss: 0.862144959
step: 9670 | loss: 0.860155963
step: 9680 | loss: 0.858167018
step: 9690 | loss: 0.856178123
step: 9700 | loss: 0.854189278
step: 9710 | loss: 0.852200482
step: 9720 | loss: 0.850211733
step: 9730 | loss: 0.848223031
step: 9740 | loss: 0.846234375
step: 9750 | loss: 0.844245765
step: 9760 | loss: 0.842257200
step: 9770 | loss: 0.840268679
step: 9780 | loss: 0.838280202
step: 9790 | loss: 0.836291767
step: 9800 | loss: 0.834303375
step: 9810 | loss: 0.832315024
step: 9820 | loss: 0.830326715
step: 9830 | loss: 0.828338446
step: 9840 | loss: 0.826350216
step: 9850 | loss: 0.824362026
step: 9860 | loss: 0.822373875
step: 9870 | loss: 0.820385762
step: 9880 | loss: 0.818397687
step: 9890 | loss: 0.816409649
step: 9900 | loss: 0.814421648
step: 9910 | loss: 0.812433683
step: 9920 | loss: 0.810445754
step: 9930 | loss: 0.808457861
step: 9940 | loss: 0.806470002
step: 9950 | loss: 0.804482178
step: 9960 | loss: 0.802494388
step: 9970 | loss: 0.800506632
step: 9980 | loss: 0.798518909
step: 9990 | loss: 0.796531218
step: 10000 | loss: 0.794543561
step: 10010 | loss: 0.792555935
step: 10020 | loss: 0.790568341
step: 10030 | loss: 0.788580779
step: 10040 | loss: 0.786593247
step: 10050 | loss: 0.784605746
step: 10060 | loss: 0.782618276
step: 10070 | loss: 0.780630835
step: 10080 | loss: 0.778643424
step: 10090 | loss: 0.776656043
step: 10100 | loss: 0.774668690
step: 10110 | loss: 0.772681366
step: 10120 | loss: 0.770694070
step: 10130 | loss: 0.768706803
step: 10140 | loss: 0.766719563
step: 10150 | loss: 0.764732350
step: 10160 | loss: 0.762745165
step: 10170 | loss: 0.760758007
step: 10180 | loss: 0.758770875
step: 10190 | loss: 0.756783769
step: 10200 | loss: 0.754796690
step: 10210 | loss: 0.752809636
step: 10220 | loss: 0.750822608
step: 10230 | loss: 0.748835605
step: 10240 | loss: 0.746848627
step: 10250 | loss: 0.744861673
step: 10260 | loss: 0.742874744
step: 10270 | loss: 0.740887840
step: 10280 | loss: 0.738900959
step: 10290 | loss: 0.736914102
step: 10300 | loss: 0.734927268
step: 10310 | loss: 0.732940458
step: 10320 | loss: 0.730953670
step: 10330 | loss: 0.728966905
step: 10340 | loss: 0.726980163
step: 10350 | loss: 0.724993443
step: 10360 | loss: 0.723006746
step: 10370 | loss: 0.721020070
step: 10380 | loss: 0.719033415
step: 10390 | loss: 0.717046782
step: 10400 | loss: 0.715060171
step: 10410 | loss: 0.713073580
step: 10420 | loss: 0.711087010
step: 10430 | loss: 0.709100460
step: 10440 | loss: 0.707113931
step: 10450 | loss: 0.705127422
step: 10460 | loss: 0.703140933
step: 10470 | loss: 0.701154464
step: 10480 | loss: 0.699168014
step: 10490 | loss: 0.697181584
step: 10500 | loss: 0.695195172
step: 10510 | loss: 0.693208780
step: 10520 | loss: 0.691222406
step: 10530 | loss: 0.689236051
step: 10540 | loss: 0.687249715
step: 10550 | loss: 0.685263397
step: 10560 | loss: 0.683277096
step: 10570 | loss: 0.681290814
step: 10580 | loss: 0.679304549
step: 10590 | loss: 0.677318302
step: 10600 | loss: 0.675332072
step: 10610 | loss: 0.673345859
step: 10620 | loss: 0.671359663
step: 10630 | loss: 0.669373484
step: 10640 | loss: 0.667387322
step: 10650 | loss: 0.665401176
step: 10660 | loss: 0.663415046
step: 10670 | loss: 0.661428932
step: 10680 | loss: 0.659442835
step: 10690 | loss: 0.657456753
step: 10700 | loss: 0.655470687
step: 10710 | loss: 0.653484637
step: 10720 | loss: 0.651498602
step: 10730 | loss: 0.649512582
step: 10740 | loss: 0.647526577
step: 10750 | loss: 0.645540587
step: 10760 | loss: 0.643554612
step: 10770 | loss: 0.641568651
step: 10780 | loss: 0.639582705
step: 10790 | loss: 0.637596773
step: 10800 | loss: 0.635610855
step: 10810 | loss: 0.633624952
step: 10820 | loss: 0.631639062
step: 10830 | loss: 0.629653186
step: 10840 | loss: 0.627667323
step: 10850 | loss: 0.625681474
step: 10860 | loss: 0.623695639
step: 10870 | loss: 0.621709816
step: 10880 | loss: 0.619724007
step: 10890 | loss: 0.617738211
step: 10900 | loss: 0.615752427
step: 10910 | loss: 0.613766656
step: 10920 | loss: 0.611780898
step: 10930 | loss: 0.609795152
step: 10940 | loss: 0.607809419
step: 10950 | loss: 0.605823698
step: 10960 | loss: 0.603837988
step: 10970 | loss: 0.601852291
step: 10980 | loss: 0.599866605
step: 10990 | loss: 0.597880932
step: 11000 | loss: 0.595895270
step: 11010 | loss: 0.593909619
step: 11020 | loss: 0.591923980
step: 11030 | loss: 0.589938352
step: 11040 | loss: 0.587952735
step: 11050 | loss: 0.585967129
step: 11060 | loss: 0.583981534
step: 11070 | loss: 0.581995950
step: 11080 | loss: 0.580010376
step: 11090 | loss: 0.578024813
step: 11100 | loss: 0.576039261
step: 11110 | loss: 0.574053719
step: 11120 | loss: 0.572068187
step: 11130 | loss: 0.570082666
step: 11140 | loss: 0.568097154
step: 11150 | loss: 0.566111653
step: 11160 | loss: 0.564126161
step: 11170 | loss: 0.562140679
step: 11180 | loss: 0.560155207
step: 11190 | loss: 0.558169745
step: 11200 | loss: 0.556184292
step: 11210 | loss: 0.554198848
step: 11220 | loss: 0.552213414
step: 11230 | loss: 0.550227989
step: 11240 | loss: 0.548242573
step: 11250 | loss: 0.546257166
step: 11260 | loss: 0.544271768
step: 11270 | loss: 0.542286378
step: 11280 | loss: 0.540300998
step: 11290 | loss: 0.538315626
step: 11300 | loss: 0.536330263
step: 11310 | loss: 0.534344908
step: 11320 | loss: 0.532359562
step: 11330 | loss: 0.530374224
step: 11340 | loss: 0.528388894
step: 11350 | loss: 0.526403573
step: 11360 | loss: 0.524418259
step: 11370 | loss: 0.522432954
step: 11380 | loss: 0.520447656
step: 11390 | loss: 0.518462367
step: 11400 | loss: 0.516477085
step: 11410 | loss: 0.514491811
step: 11420 | loss: 0.512506544
step: 11430 | loss: 0.510521285
step: 11440 | loss: 0.508536033
step: 11450 | loss: 0.506550789
step: 11460 | loss: 0.504565552
step: 11470 | loss: 0.502580323
step: 11480 | loss: 0.500595100
step: 11490 | loss: 0.498609885
step: 11500 | loss: 0.496624677
step: 11510 | loss: 0.494639475
step: 11520 | loss: 0.492654281
step: 11530 | loss: 0.490669093
step: 11540 | loss: 0.488683912
step: 11550 | loss: 0.486698738
step: 11560 | loss: 0.484713570
step: 11570 | loss: 0.482728409
step: 11580 | loss: 0.480743254
step: 11590 | loss: 0.478758106
step: 11600 | loss: 0.476772964
step: 11610 | loss: 0.474787829
step: 11620 | loss: 0.472802700
step: 11630 | loss: 0.470817576
step: 11640 | loss: 0.468832459
step: 11650 | loss: 0.466847348
step: 11660 | loss: 0.464862243
step: 11670 | loss: 0.462877144
step: 11680 | loss: 0.460892051
step: 11690 | loss: 0.458906964
step: 11700 | loss: 0.456921882
step: 11710 | loss: 0.454936806
step: 11720 | loss: 0.452951736
step: 11730 | loss: 0.450966671
step: 11740 | loss: 0.448981612
step: 11750 | loss: 0.446996558
step: 11760 | loss: 0.445011510
step: 11770 | loss: 0.443026467
step: 11780 | loss: 0.441041429
step: 11790 | loss: 0.439056397
step: 11800 | loss: 0.437071370
step: 11810 | loss: 0.435086348
step: 11820 | loss: 0.433101331
step: 11830 | loss: 0.431116319
step: 11840 | loss: 0.429131312
step: 11850 | loss: 0.427146310
step: 11860 | loss: 0.425161313
step: 11870 | loss: 0.423176321
step: 11880 | loss: 0.421191334
step: 11890 | loss: 0.419206351
step: 11900 | loss: 0.417221373
step: 11910 | loss: 0.415236400
step: 11920 | loss: 0.413251431
step: 11930 | loss: 0.411266467
step: 11940 | loss: 0.409281508
step: 11950 | loss: 0.407296553
step: 11960 | loss: 0.405311602
step: 11970 | loss: 0.403326656
step: 11980 | loss: 0.401341714
step: 11990 | loss: 0.399356776
step: 12000 | loss: 0.397371843
step: 12010 | loss: 0.395386914
step: 12020 | loss: 0.393401989
step: 12030 | loss: 0.391417068
step: 12040 | loss: 0.389432151
step: 12050 | loss: 0.387447239
step: 12060 | loss: 0.385462330
step: 12070 | loss: 0.383477425
step: 12080 | loss: 0.381492525
step: 12090 | loss: 0.379507628
step: 12100 | loss: 0.377522735
step: 12110 | loss: 0.375537845
step: 12120 | loss: 0.373552960
step: 12130 | loss: 0.371568078
step: 12140 | loss: 0.369583200
step: 12150 | loss: 0.367598326
step: 12160 | loss: 0.365613455
step: 12170 | loss: 0.363628588
step: 12180 | loss: 0.361643725
step: 12190 | loss: 0.359658865
step: 12200 | loss: 0.357674008
step: 12210 | loss: 0.355689155
step: 12220 | loss: 0.353704306
step: 12230 | loss: 0.351719459
step: 12240 | loss: 0.349734616
step: 12250 | loss: 0.347749777
step: 12260 | loss: 0.345764940
step: 12270 | loss: 0.343780107
step: 12280 | loss: 0.341795278
step: 12290 | loss: 0.339810451
step: 12300 | loss: 0.337825627
step: 12310 | loss: 0.335840807
step: 12320 | loss: 0.333855990
step: 12330 | loss: 0.331871175
step: 12340 | loss: 0.329886364
step: 12350 | loss: 0.327901556
step: 12360 | loss: 0.325916751
step: 12370 | loss: 0.323931949
step: 12380 | loss: 0.321947149
step: 12390 | loss: 0.319962353
step: 12400 | loss: 0.317977559
step: 12410 | loss: 0.315992768
step: 12420 | loss: 0.314007980
step: 12430 | loss: 0.312023195
step: 12440 | loss: 0.310038412
step: 12450 | loss: 0.308053633
step: 12460 | loss: 0.306068856
step: 12470 | loss: 0.304084081
step: 12480 | loss: 0.302099309
step: 12490 | loss: 0.300114540
step: 12500 | loss: 0.298129773
step: 12510 | loss: 0.296145009
step: 12520 | loss: 0.294160248
step: 12530 | loss: 0.292175489
step: 12540 | loss: 0.290190732
step: 12550 | loss: 0.288205978
step: 12560 | loss: 0.286221226
step: 12570 | loss: 0.284236477
step: 12580 | loss: 0.282251730
step: 12590 | loss: 0.280266986
step: 12600 | loss: 0.278282244
step: 12610 | loss: 0.276297504
step: 12620 | loss: 0.274312766
step: 12630 | loss: 0.272328031
step: 12640 | loss: 0.270343298
step: 12650 | loss: 0.268358567
step: 12660 | loss: 0.266373838
step: 12670 | loss: 0.264389112
step: 12680 | loss: 0.262404388
step: 12690 | loss: 0.260419665
step: 12700 | loss: 0.258434945
step: 12710 | loss: 0.256450227
step: 12720 | loss: 0.254465512
step: 12730 | loss: 0.252480798
step: 12740 | loss: 0.250496086
step: 12750 | loss: 0.248511376
step: 12760 | loss: 0.246526668
step: 12770 | loss: 0.244541962
step: 12780 | loss: 0.242557259
step: 12790 | loss: 0.240572557
step: 12800 | loss: 0.238587857
step: 12810 | loss: 0.236603158
step: 12820 | loss: 0.234618462
step: 12830 | loss: 0.232633768
step: 12840 | loss: 0.230649075
step: 12850 | loss: 0.228664384
step: 12860 | loss: 0.226679695
step: 12870 | loss: 0.224695008
step: 12880 | loss: 0.222710323
step: 12890 | loss: 0.220725639
step: 12900 | loss: 0.218740957
step: 12910 | loss: 0.216756277
step: 12920 | loss: 0.214771598
step: 12930 | loss: 0.212786922
step: 12940 | loss: 0.210802246
step: 12950 | loss: 0.208817573
step: 12960 | loss: 0.206832901
step: 12970 | loss: 0.204848231
step: 12980 | loss: 0.202863562
step: 12990 | loss: 0.200878895
step: 13000 | loss: 0.198894229
step: 13010 | loss: 0.196909565
step: 13020 | loss: 0.194924903
step: 13030 | loss: 0.192940242
step: 13040 | loss: 0.190955582
step: 13050 | loss: 0.188970924
step: 13060 | loss: 0.186986268
step: 13070 | loss: 0.185001613
step: 13080 | loss: 0.183016959
step: 13090 | loss: 0.181032307
step: 13100 | loss: 0.179047657
step: 13110 | loss: 0.177063007
step: 13120 | loss: 0.175078359
step: 13130 | loss: 0.173093713
step: 13140 | loss: 0.171109067
step: 13150 | loss: 0.169124424
step: 13160 | loss: 0.167139781
step: 13170 | loss: 0.165155140
step: 13180 | loss: 0.163170500
step: 13190 | loss: 0.161185861
step: 13200 | loss: 0.159201224
step: 13210 | loss: 0.157216588
step: 13220 | loss: 0.155231953
step: 13230 | loss: 0.153247320
step: 13240 | loss: 0.151262687
step: 13250 | loss: 0.149278056
step: 13260 | loss: 0.147293426
step: 13270 | loss: 0.145308798
step: 13280 | loss: 0.143324170
step: 13290 | loss: 0.141339544
step: 13300 | loss: 0.139354919
step: 13310 | loss: 0.137370295
step: 13320 | loss: 0.135385672
step: 13330 | loss: 0.133401050
step: 13340 | loss: 0.131416429
step: 13350 | loss: 0.129431810
step: 13360 | loss: 0.127447191
step: 13370 | loss: 0.125462574
step: 13380 | loss: 0.123477957
step: 13390 | loss: 0.121493342
step: 13400 | loss: 0.119508728
step: 13410 | loss: 0.117524115
step: 13420 | loss: 0.115539503
step: 13430 | loss: 0.113554892
step: 13440 | loss: 0.111570281
step: 13450 | loss: 0.109585672
step: 13460 | loss: 0.107601064
step: 13470 | loss: 0.105616457
step: 13480 | loss: 0.103631851
step: 13490 | loss: 0.101647246
step: 13500 | loss: 0.099662641
step: 13510 | loss: 0.097678038
step: 13520 | loss: 0.095693436
step: 13530 | loss: 0.093708834
step: 13540 | loss: 0.091724234
step: 13550 | loss: 0.089739634
step: 13560 | loss: 0.087755035
step: 13570 | loss: 0.085770437
step: 13580 | loss: 0.083785840
step: 13590 | loss: 0.081801244
step: 13600 | loss: 0.079816649
step: 13610 | loss: 0.077832054
step: 13620 | loss: 0.075847461
step: 13630 | loss: 0.073862868
step: 13640 | loss: 0.071878276
step: 13650 | loss: 0.069893685
step: 13660 | loss: 0.067909095
step: 13670 | loss: 0.065924505
step: 13680 | loss: 0.063939916
step: 13690 | loss: 0.061955328
step: 13700 | loss: 0.059970741
step: 13710 | loss: 0.057986155
step: 13720 | loss: 0.056001569
step: 13730 | loss: 0.054016984
step: 13740 | loss: 0.052032400
step: 13750 | loss: 0.050047817
step: 13760 | loss: 0.048063234
step: 13770 | loss: 0.046078652
step: 13780 | loss: 0.044094071
step: 13790 | loss: 0.042109491
step: 13800 | loss: 0.040124911
step: 13810 | loss: 0.038140332
step: 13820 | loss: 0.036155753
step: 13830 | loss: 0.034171176
step: 13840 | loss: 0.032186599
step: 13850 | loss: 0.030202022
step: 13860 | loss: 0.028217446
step: 13870 | loss: 0.026232871
step: 13880 | loss: 0.024248297
step: 13890 | loss: 0.022263723
step: 13900 | loss: 0.020279150
step: 13910 | loss: 0.018294577
step: 13920 | loss: 0.016310006
step: 13930 | loss: 0.014325434
step: 13940 | loss: 0.012340864
step: 13950 | loss: 0.010356294
step: 13960 | loss: 0.008371724
step: 13970 | loss: 0.006387155
step: 13980 | loss: 0.004402587
step: 13990 | loss: 0.002418019
- final loss: 0.000830
- (cd _build/default/examples/opt && ./pair.exe)
-
step: 0 | loss: 3.523292206
step: 10 | loss: 3.520253339
step: 20 | loss: 3.516878917
step: 30 | loss: 3.513506956
step: 40 | loss: 3.510137635
step: 50 | loss: 3.506771059
step: 60 | loss: 3.503407281
step: 70 | loss: 3.500046327
step: 80 | loss: 3.496688206
step: 90 | loss: 3.493332924
step: 100 | loss: 3.489980481
step: 110 | loss: 3.486630876
step: 120 | loss: 3.483284108
step: 130 | loss: 3.479940178
step: 140 | loss: 3.476599082
step: 150 | loss: 3.473260821
step: 160 | loss: 3.469925393
step: 170 | loss: 3.466592797
step: 180 | loss: 3.463263032
step: 190 | loss: 3.459936096
step: 200 | loss: 3.456611988
step: 210 | loss: 3.453290708
step: 220 | loss: 3.449972254
step: 230 | loss: 3.446656625
step: 240 | loss: 3.443343819
step: 250 | loss: 3.440033836
step: 260 | loss: 3.436726674
step: 270 | loss: 3.433422332
step: 280 | loss: 3.430120810
step: 290 | loss: 3.426822105
step: 300 | loss: 3.423526217
step: 310 | loss: 3.420233144
step: 320 | loss: 3.416942886
step: 330 | loss: 3.413655441
step: 340 | loss: 3.410370808
step: 350 | loss: 3.407088985
step: 360 | loss: 3.403809972
step: 370 | loss: 3.400533768
step: 380 | loss: 3.397260371
step: 390 | loss: 3.393989780
step: 400 | loss: 3.390721994
step: 410 | loss: 3.387457011
step: 420 | loss: 3.384194831
step: 430 | loss: 3.380935453
step: 440 | loss: 3.377678874
step: 450 | loss: 3.374425094
step: 460 | loss: 3.371174113
step: 470 | loss: 3.367925927
step: 480 | loss: 3.364680537
step: 490 | loss: 3.361437941
step: 500 | loss: 3.358198138
step: 510 | loss: 3.354961127
step: 520 | loss: 3.351726906
step: 530 | loss: 3.348495474
step: 540 | loss: 3.345266831
step: 550 | loss: 3.342040974
step: 560 | loss: 3.338817903
step: 570 | loss: 3.335597616
step: 580 | loss: 3.332380112
step: 590 | loss: 3.329165390
step: 600 | loss: 3.325953449
step: 610 | loss: 3.322744287
step: 620 | loss: 3.319537903
step: 630 | loss: 3.316334296
step: 640 | loss: 3.313133464
step: 650 | loss: 3.309935407
step: 660 | loss: 3.306740123
step: 670 | loss: 3.303547611
step: 680 | loss: 3.300357869
step: 690 | loss: 3.297170896
step: 700 | loss: 3.293986691
step: 710 | loss: 3.290805253
step: 720 | loss: 3.287626580
step: 730 | loss: 3.284450671
step: 740 | loss: 3.281277524
step: 750 | loss: 3.278107139
step: 760 | loss: 3.274939514
step: 770 | loss: 3.271774648
step: 780 | loss: 3.268612538
step: 790 | loss: 3.265453185
step: 800 | loss: 3.262296586
step: 810 | loss: 3.259142740
step: 820 | loss: 3.255991647
step: 830 | loss: 3.252843303
step: 840 | loss: 3.249697709
step: 850 | loss: 3.246554862
step: 860 | loss: 3.243414762
step: 870 | loss: 3.240277407
step: 880 | loss: 3.237142795
step: 890 | loss: 3.234010924
step: 900 | loss: 3.230881795
step: 910 | loss: 3.227755405
step: 920 | loss: 3.224631752
step: 930 | loss: 3.221510835
step: 940 | loss: 3.218392654
step: 950 | loss: 3.215277205
step: 960 | loss: 3.212164489
step: 970 | loss: 3.209054502
step: 980 | loss: 3.205947245
step: 990 | loss: 3.202842715
step: 1000 | loss: 3.199740910
step: 1010 | loss: 3.196641830
step: 1020 | loss: 3.193545472
step: 1030 | loss: 3.190451836
step: 1040 | loss: 3.187360919
step: 1050 | loss: 3.184272721
step: 1060 | loss: 3.181187238
step: 1070 | loss: 3.178104471
step: 1080 | loss: 3.175024417
step: 1090 | loss: 3.171947074
step: 1100 | loss: 3.168872442
step: 1110 | loss: 3.165800518
step: 1120 | loss: 3.162731301
step: 1130 | loss: 3.159664789
step: 1140 | loss: 3.156600981
step: 1150 | loss: 3.153539874
step: 1160 | loss: 3.150481468
step: 1170 | loss: 3.147425760
step: 1180 | loss: 3.144372749
step: 1190 | loss: 3.141322432
step: 1200 | loss: 3.138274810
step: 1210 | loss: 3.135229879
step: 1220 | loss: 3.132187638
step: 1230 | loss: 3.129148085
step: 1240 | loss: 3.126111218
step: 1250 | loss: 3.123077036
step: 1260 | loss: 3.120045537
step: 1270 | loss: 3.117016719
step: 1280 | loss: 3.113990580
step: 1290 | loss: 3.110967119
step: 1300 | loss: 3.107946333
step: 1310 | loss: 3.104928221
step: 1320 | loss: 3.101912782
step: 1330 | loss: 3.098900012
step: 1340 | loss: 3.095889910
step: 1350 | loss: 3.092882475
step: 1360 | loss: 3.089877704
step: 1370 | loss: 3.086875596
step: 1380 | loss: 3.083876149
step: 1390 | loss: 3.080879360
step: 1400 | loss: 3.077885228
step: 1410 | loss: 3.074893751
step: 1420 | loss: 3.071904927
step: 1430 | loss: 3.068918754
step: 1440 | loss: 3.065935230
step: 1450 | loss: 3.062954352
step: 1460 | loss: 3.059976120
step: 1470 | loss: 3.057000531
step: 1480 | loss: 3.054027583
step: 1490 | loss: 3.051057274
step: 1500 | loss: 3.048089602
step: 1510 | loss: 3.045124565
step: 1520 | loss: 3.042162161
step: 1530 | loss: 3.039202388
step: 1540 | loss: 3.036245243
step: 1550 | loss: 3.033290725
step: 1560 | loss: 3.030338832
step: 1570 | loss: 3.027389561
step: 1580 | loss: 3.024442911
step: 1590 | loss: 3.021498878
step: 1600 | loss: 3.018557462
step: 1610 | loss: 3.015618660
step: 1620 | loss: 3.012682470
step: 1630 | loss: 3.009748890
step: 1640 | loss: 3.006817917
step: 1650 | loss: 3.003889549
step: 1660 | loss: 3.000963785
step: 1670 | loss: 2.998040622
step: 1680 | loss: 2.995120057
step: 1690 | loss: 2.992202090
step: 1700 | loss: 2.989286716
step: 1710 | loss: 2.986373935
step: 1720 | loss: 2.983463745
step: 1730 | loss: 2.980556142
step: 1740 | loss: 2.977651124
step: 1750 | loss: 2.974748690
step: 1760 | loss: 2.971848837
step: 1770 | loss: 2.968951563
step: 1780 | loss: 2.966056865
step: 1790 | loss: 2.963164742
step: 1800 | loss: 2.960275192
step: 1810 | loss: 2.957388211
step: 1820 | loss: 2.954503797
step: 1830 | loss: 2.951621949
step: 1840 | loss: 2.948742664
step: 1850 | loss: 2.945865940
step: 1860 | loss: 2.942991774
step: 1870 | loss: 2.940120165
step: 1880 | loss: 2.937251110
step: 1890 | loss: 2.934384606
step: 1900 | loss: 2.931520652
step: 1910 | loss: 2.928659245
step: 1920 | loss: 2.925800382
step: 1930 | loss: 2.922944062
step: 1940 | loss: 2.920090283
step: 1950 | loss: 2.917239042
step: 1960 | loss: 2.914390336
step: 1970 | loss: 2.911544164
step: 1980 | loss: 2.908700523
step: 1990 | loss: 2.905859410
step: 2000 | loss: 2.903020825
step: 2010 | loss: 2.900184764
step: 2020 | loss: 2.897351225
step: 2030 | loss: 2.894520205
step: 2040 | loss: 2.891691704
step: 2050 | loss: 2.888865717
step: 2060 | loss: 2.886042244
step: 2070 | loss: 2.883221282
step: 2080 | loss: 2.880402828
step: 2090 | loss: 2.877586880
step: 2100 | loss: 2.874773437
step: 2110 | loss: 2.871962496
step: 2120 | loss: 2.869154054
step: 2130 | loss: 2.866348110
step: 2140 | loss: 2.863544661
step: 2150 | loss: 2.860743706
step: 2160 | loss: 2.857945241
step: 2170 | loss: 2.855149265
step: 2180 | loss: 2.852355777
step: 2190 | loss: 2.849564772
step: 2200 | loss: 2.846776250
step: 2210 | loss: 2.843990209
step: 2220 | loss: 2.841206645
step: 2230 | loss: 2.838425558
step: 2240 | loss: 2.835646945
step: 2250 | loss: 2.832870804
step: 2260 | loss: 2.830097133
step: 2270 | loss: 2.827325930
step: 2280 | loss: 2.824557192
step: 2290 | loss: 2.821790919
step: 2300 | loss: 2.819027108
step: 2310 | loss: 2.816265756
step: 2320 | loss: 2.813506863
step: 2330 | loss: 2.810750426
step: 2340 | loss: 2.807996443
step: 2350 | loss: 2.805244913
step: 2360 | loss: 2.802495832
step: 2370 | loss: 2.799749201
step: 2380 | loss: 2.797005016
step: 2390 | loss: 2.794263276
step: 2400 | loss: 2.791523980
step: 2410 | loss: 2.788787124
step: 2420 | loss: 2.786052709
step: 2430 | loss: 2.783320731
step: 2440 | loss: 2.780591189
step: 2450 | loss: 2.777864082
step: 2460 | loss: 2.775139408
step: 2470 | loss: 2.772417165
step: 2480 | loss: 2.769697352
step: 2490 | loss: 2.766979967
step: 2500 | loss: 2.764265008
step: 2510 | loss: 2.761552474
step: 2520 | loss: 2.758842364
step: 2530 | loss: 2.756134675
step: 2540 | loss: 2.753429407
step: 2550 | loss: 2.750726558
step: 2560 | loss: 2.748026127
step: 2570 | loss: 2.745328112
step: 2580 | loss: 2.742632511
step: 2590 | loss: 2.739939325
step: 2600 | loss: 2.737248550
step: 2610 | loss: 2.734560187
step: 2620 | loss: 2.731874233
step: 2630 | loss: 2.729190688
step: 2640 | loss: 2.726509550
step: 2650 | loss: 2.723830818
step: 2660 | loss: 2.721154492
step: 2670 | loss: 2.718480569
step: 2680 | loss: 2.715809049
step: 2690 | loss: 2.713139931
step: 2700 | loss: 2.710473213
step: 2710 | loss: 2.707808895
step: 2720 | loss: 2.705146977
step: 2730 | loss: 2.702487455
step: 2740 | loss: 2.699830331
step: 2750 | loss: 2.697175603
step: 2760 | loss: 2.694523270
step: 2770 | loss: 2.691873332
step: 2780 | loss: 2.689225787
step: 2790 | loss: 2.686580635
step: 2800 | loss: 2.683937875
step: 2810 | loss: 2.681297506
step: 2820 | loss: 2.678659528
step: 2830 | loss: 2.676023940
step: 2840 | loss: 2.673390742
step: 2850 | loss: 2.670759932
step: 2860 | loss: 2.668131510
step: 2870 | loss: 2.665505476
step: 2880 | loss: 2.662881829
step: 2890 | loss: 2.660260569
step: 2900 | loss: 2.657641695
step: 2910 | loss: 2.655025206
step: 2920 | loss: 2.652411103
step: 2930 | loss: 2.649799385
step: 2940 | loss: 2.647190051
step: 2950 | loss: 2.644583101
step: 2960 | loss: 2.641978536
step: 2970 | loss: 2.639376353
step: 2980 | loss: 2.636776554
step: 2990 | loss: 2.634179138
step: 3000 | loss: 2.631584105
step: 3010 | loss: 2.628991454
step: 3020 | loss: 2.626401185
step: 3030 | loss: 2.623813299
step: 3040 | loss: 2.621227794
step: 3050 | loss: 2.618644671
step: 3060 | loss: 2.616063930
step: 3070 | loss: 2.613485571
step: 3080 | loss: 2.610909593
step: 3090 | loss: 2.608335997
step: 3100 | loss: 2.605764782
step: 3110 | loss: 2.603195949
step: 3120 | loss: 2.600629497
step: 3130 | loss: 2.598065426
step: 3140 | loss: 2.595503737
step: 3150 | loss: 2.592944429
step: 3160 | loss: 2.590387502
step: 3170 | loss: 2.587832957
step: 3180 | loss: 2.585280794
step: 3190 | loss: 2.582731012
step: 3200 | loss: 2.580183612
step: 3210 | loss: 2.577638593
step: 3220 | loss: 2.575095957
step: 3230 | loss: 2.572555702
step: 3240 | loss: 2.570017830
step: 3250 | loss: 2.567482339
step: 3260 | loss: 2.564949231
step: 3270 | loss: 2.562418506
step: 3280 | loss: 2.559890163
step: 3290 | loss: 2.557364202
step: 3300 | loss: 2.554840625
step: 3310 | loss: 2.552319430
step: 3320 | loss: 2.549800619
step: 3330 | loss: 2.547284191
step: 3340 | loss: 2.544770146
step: 3350 | loss: 2.542258485
step: 3360 | loss: 2.539749208
step: 3370 | loss: 2.537242314
step: 3380 | loss: 2.534737804
step: 3390 | loss: 2.532235678
step: 3400 | loss: 2.529735937
step: 3410 | loss: 2.527238579
step: 3420 | loss: 2.524743607
step: 3430 | loss: 2.522251018
step: 3440 | loss: 2.519760815
step: 3450 | loss: 2.517272995
step: 3460 | loss: 2.514787561
step: 3470 | loss: 2.512304512
step: 3480 | loss: 2.509823847
step: 3490 | loss: 2.507345567
step: 3500 | loss: 2.504869673
step: 3510 | loss: 2.502396163
step: 3520 | loss: 2.499925038
step: 3530 | loss: 2.497456298
step: 3540 | loss: 2.494989943
step: 3550 | loss: 2.492525973
step: 3560 | loss: 2.490064388
step: 3570 | loss: 2.487605188
step: 3580 | loss: 2.485148372
step: 3590 | loss: 2.482693941
step: 3600 | loss: 2.480241894
step: 3610 | loss: 2.477792232
step: 3620 | loss: 2.475344953
step: 3630 | loss: 2.472900059
step: 3640 | loss: 2.470457548
step: 3650 | loss: 2.468017421
step: 3660 | loss: 2.465579677
step: 3670 | loss: 2.463144316
step: 3680 | loss: 2.460711338
step: 3690 | loss: 2.458280741
step: 3700 | loss: 2.455852527
step: 3710 | loss: 2.453426695
step: 3720 | loss: 2.451003243
step: 3730 | loss: 2.448582172
step: 3740 | loss: 2.446163482
step: 3750 | loss: 2.443747171
step: 3760 | loss: 2.441333240
step: 3770 | loss: 2.438921687
step: 3780 | loss: 2.436512512
step: 3790 | loss: 2.434105715
step: 3800 | loss: 2.431701295
step: 3810 | loss: 2.429299251
step: 3820 | loss: 2.426899582
step: 3830 | loss: 2.424502288
step: 3840 | loss: 2.422107368
step: 3850 | loss: 2.419714822
step: 3860 | loss: 2.417324647
step: 3870 | loss: 2.414936844
step: 3880 | loss: 2.412551412
step: 3890 | loss: 2.410168349
step: 3900 | loss: 2.407787655
step: 3910 | loss: 2.405409329
step: 3920 | loss: 2.403033369
step: 3930 | loss: 2.400659775
step: 3940 | loss: 2.398288545
step: 3950 | loss: 2.395919678
step: 3960 | loss: 2.393553173
step: 3970 | loss: 2.391189029
step: 3980 | loss: 2.388827244
step: 3990 | loss: 2.386467818
step: 4000 | loss: 2.384110748
step: 4010 | loss: 2.381756034
step: 4020 | loss: 2.379403674
step: 4030 | loss: 2.377053666
step: 4040 | loss: 2.374706009
step: 4050 | loss: 2.372360701
step: 4060 | loss: 2.370017742
step: 4070 | loss: 2.367677128
step: 4080 | loss: 2.365338859
step: 4090 | loss: 2.363002932
step: 4100 | loss: 2.360669347
step: 4110 | loss: 2.358338100
step: 4120 | loss: 2.356009191
step: 4130 | loss: 2.353682617
step: 4140 | loss: 2.351358376
step: 4150 | loss: 2.349036466
step: 4160 | loss: 2.346716886
step: 4170 | loss: 2.344399633
step: 4180 | loss: 2.342084705
step: 4190 | loss: 2.339772099
step: 4200 | loss: 2.337461815
step: 4210 | loss: 2.335153848
step: 4220 | loss: 2.332848198
step: 4230 | loss: 2.330544860
step: 4240 | loss: 2.328243835
step: 4250 | loss: 2.325945117
step: 4260 | loss: 2.323648706
step: 4270 | loss: 2.321354598
step: 4280 | loss: 2.319062792
step: 4290 | loss: 2.316773283
step: 4300 | loss: 2.314486070
step: 4310 | loss: 2.312201149
step: 4320 | loss: 2.309918519
step: 4330 | loss: 2.307638175
step: 4340 | loss: 2.305360116
step: 4350 | loss: 2.303084337
step: 4360 | loss: 2.300810837
step: 4370 | loss: 2.298539611
step: 4380 | loss: 2.296270657
step: 4390 | loss: 2.294003972
step: 4400 | loss: 2.291739552
step: 4410 | loss: 2.289477394
step: 4420 | loss: 2.287217495
step: 4430 | loss: 2.284959851
step: 4440 | loss: 2.282704459
step: 4450 | loss: 2.280451315
step: 4460 | loss: 2.278200416
step: 4470 | loss: 2.275951758
step: 4480 | loss: 2.273705337
step: 4490 | loss: 2.271461150
step: 4500 | loss: 2.269219193
step: 4510 | loss: 2.266979462
step: 4520 | loss: 2.264741954
step: 4530 | loss: 2.262506663
step: 4540 | loss: 2.260273587
step: 4550 | loss: 2.258042720
step: 4560 | loss: 2.255814060
step: 4570 | loss: 2.253587602
step: 4580 | loss: 2.251363341
step: 4590 | loss: 2.249141273
step: 4600 | loss: 2.246921395
step: 4610 | loss: 2.244703701
step: 4620 | loss: 2.242488187
step: 4630 | loss: 2.240274848
step: 4640 | loss: 2.238063681
step: 4650 | loss: 2.235854680
step: 4660 | loss: 2.233647841
step: 4670 | loss: 2.231443159
step: 4680 | loss: 2.229240629
step: 4690 | loss: 2.227040246
step: 4700 | loss: 2.224842005
step: 4710 | loss: 2.222645902
step: 4720 | loss: 2.220451932
step: 4730 | loss: 2.218260088
step: 4740 | loss: 2.216070367
step: 4750 | loss: 2.213882763
step: 4760 | loss: 2.211697270
step: 4770 | loss: 2.209513884
step: 4780 | loss: 2.207332598
step: 4790 | loss: 2.205153409
step: 4800 | loss: 2.202976309
step: 4810 | loss: 2.200801294
step: 4820 | loss: 2.198628357
step: 4830 | loss: 2.196457494
step: 4840 | loss: 2.194288699
step: 4850 | loss: 2.192121965
step: 4860 | loss: 2.189957287
step: 4870 | loss: 2.187794659
step: 4880 | loss: 2.185634074
step: 4890 | loss: 2.183475528
step: 4900 | loss: 2.181319014
step: 4910 | loss: 2.179164526
step: 4920 | loss: 2.177012057
step: 4930 | loss: 2.174861602
step: 4940 | loss: 2.172713154
step: 4950 | loss: 2.170566707
step: 4960 | loss: 2.168422253
step: 4970 | loss: 2.166279788
step: 4980 | loss: 2.164139304
step: 4990 | loss: 2.162000795
step: 5000 | loss: 2.159864254
step: 5010 | loss: 2.157729674
step: 5020 | loss: 2.155597049
step: 5030 | loss: 2.153466372
step: 5040 | loss: 2.151337636
step: 5050 | loss: 2.149210834
step: 5060 | loss: 2.147085959
step: 5070 | loss: 2.144963004
step: 5080 | loss: 2.142841963
step: 5090 | loss: 2.140722827
step: 5100 | loss: 2.138605590
step: 5110 | loss: 2.136490244
step: 5120 | loss: 2.134376783
step: 5130 | loss: 2.132265198
step: 5140 | loss: 2.130155483
step: 5150 | loss: 2.128047629
step: 5160 | loss: 2.125941631
step: 5170 | loss: 2.123837479
step: 5180 | loss: 2.121735166
step: 5190 | loss: 2.119634685
step: 5200 | loss: 2.117536028
step: 5210 | loss: 2.115439188
step: 5220 | loss: 2.113344155
step: 5230 | loss: 2.111250924
step: 5240 | loss: 2.109159484
step: 5250 | loss: 2.107069830
step: 5260 | loss: 2.104981952
step: 5270 | loss: 2.102895843
step: 5280 | loss: 2.100811494
step: 5290 | loss: 2.098728898
step: 5300 | loss: 2.096648046
step: 5310 | loss: 2.094568929
step: 5320 | loss: 2.092491541
step: 5330 | loss: 2.090415872
step: 5340 | loss: 2.088341913
step: 5350 | loss: 2.086269658
step: 5360 | loss: 2.084199096
step: 5370 | loss: 2.082130220
step: 5380 | loss: 2.080063021
step: 5390 | loss: 2.077997491
step: 5400 | loss: 2.075933620
step: 5410 | loss: 2.073871401
step: 5420 | loss: 2.071810824
step: 5430 | loss: 2.069751880
step: 5440 | loss: 2.067694562
step: 5450 | loss: 2.065638860
step: 5460 | loss: 2.063584765
step: 5470 | loss: 2.061532268
step: 5480 | loss: 2.059481361
step: 5490 | loss: 2.057432034
step: 5500 | loss: 2.055384279
step: 5510 | loss: 2.053338086
step: 5520 | loss: 2.051293446
step: 5530 | loss: 2.049250351
step: 5540 | loss: 2.047208790
step: 5550 | loss: 2.045168756
step: 5560 | loss: 2.043130238
step: 5570 | loss: 2.041093228
step: 5580 | loss: 2.039057716
step: 5590 | loss: 2.037023693
step: 5600 | loss: 2.034991150
step: 5610 | loss: 2.032960077
step: 5620 | loss: 2.030930465
step: 5630 | loss: 2.028902304
step: 5640 | loss: 2.026875586
step: 5650 | loss: 2.024850300
step: 5660 | loss: 2.022826438
step: 5670 | loss: 2.020803989
step: 5680 | loss: 2.018782944
step: 5690 | loss: 2.016763294
step: 5700 | loss: 2.014745029
step: 5710 | loss: 2.012728140
step: 5720 | loss: 2.010712617
step: 5730 | loss: 2.008698450
step: 5740 | loss: 2.006685630
step: 5750 | loss: 2.004674147
step: 5760 | loss: 2.002663991
step: 5770 | loss: 2.000655153
step: 5780 | loss: 1.998647623
step: 5790 | loss: 1.996641392
step: 5800 | loss: 1.994636449
step: 5810 | loss: 1.992632785
step: 5820 | loss: 1.990630391
step: 5830 | loss: 1.988629256
step: 5840 | loss: 1.986629371
step: 5850 | loss: 1.984630726
step: 5860 | loss: 1.982633311
step: 5870 | loss: 1.980637116
step: 5880 | loss: 1.978642133
step: 5890 | loss: 1.976648350
step: 5900 | loss: 1.974655759
step: 5910 | loss: 1.972664348
step: 5920 | loss: 1.970674110
step: 5930 | loss: 1.968685033
step: 5940 | loss: 1.966697109
step: 5950 | loss: 1.964710326
step: 5960 | loss: 1.962724676
step: 5970 | loss: 1.960740149
step: 5980 | loss: 1.958756735
step: 5990 | loss: 1.956774424
step: 6000 | loss: 1.954793207
step: 6010 | loss: 1.952813073
step: 6020 | loss: 1.950834013
step: 6030 | loss: 1.948856017
step: 6040 | loss: 1.946879076
step: 6050 | loss: 1.944903180
step: 6060 | loss: 1.942928318
step: 6070 | loss: 1.940954482
step: 6080 | loss: 1.938981662
step: 6090 | loss: 1.937009848
step: 6100 | loss: 1.935039030
step: 6110 | loss: 1.933069198
step: 6120 | loss: 1.931100344
step: 6130 | loss: 1.929132458
step: 6140 | loss: 1.927165529
step: 6150 | loss: 1.925199548
step: 6160 | loss: 1.923234507
step: 6170 | loss: 1.921270395
step: 6180 | loss: 1.919307202
step: 6190 | loss: 1.917344920
step: 6200 | loss: 1.915383538
step: 6210 | loss: 1.913423048
step: 6220 | loss: 1.911463440
step: 6230 | loss: 1.909504705
step: 6240 | loss: 1.907546832
step: 6250 | loss: 1.905589814
step: 6260 | loss: 1.903633640
step: 6270 | loss: 1.901678302
step: 6280 | loss: 1.899723789
step: 6290 | loss: 1.897770094
step: 6300 | loss: 1.895817206
step: 6310 | loss: 1.893865116
step: 6320 | loss: 1.891913816
step: 6330 | loss: 1.889963297
step: 6340 | loss: 1.888013549
step: 6350 | loss: 1.886064562
step: 6360 | loss: 1.884116330
step: 6370 | loss: 1.882168841
step: 6380 | loss: 1.880222088
step: 6390 | loss: 1.878276062
step: 6400 | loss: 1.876330753
step: 6410 | loss: 1.874386153
step: 6420 | loss: 1.872442254
step: 6430 | loss: 1.870499046
step: 6440 | loss: 1.868556521
step: 6450 | loss: 1.866614670
step: 6460 | loss: 1.864673485
step: 6470 | loss: 1.862732957
step: 6480 | loss: 1.860793078
step: 6490 | loss: 1.858853839
step: 6500 | loss: 1.856915231
step: 6510 | loss: 1.854977247
step: 6520 | loss: 1.853039879
step: 6530 | loss: 1.851103117
step: 6540 | loss: 1.849166954
step: 6550 | loss: 1.847231382
step: 6560 | loss: 1.845296392
step: 6570 | loss: 1.843361977
step: 6580 | loss: 1.841428128
step: 6590 | loss: 1.839494838
step: 6600 | loss: 1.837562098
step: 6610 | loss: 1.835629901
step: 6620 | loss: 1.833698239
step: 6630 | loss: 1.831767104
step: 6640 | loss: 1.829836489
step: 6650 | loss: 1.827906386
step: 6660 | loss: 1.825976788
step: 6670 | loss: 1.824047686
step: 6680 | loss: 1.822119074
step: 6690 | loss: 1.820190944
step: 6700 | loss: 1.818263289
step: 6710 | loss: 1.816336101
step: 6720 | loss: 1.814409374
step: 6730 | loss: 1.812483100
step: 6740 | loss: 1.810557272
step: 6750 | loss: 1.808631883
step: 6760 | loss: 1.806706926
step: 6770 | loss: 1.804782394
step: 6780 | loss: 1.802858281
step: 6790 | loss: 1.800934580
step: 6800 | loss: 1.799011283
step: 6810 | loss: 1.797088384
step: 6820 | loss: 1.795165877
step: 6830 | loss: 1.793243756
step: 6840 | loss: 1.791322013
step: 6850 | loss: 1.789400642
step: 6860 | loss: 1.787479637
step: 6870 | loss: 1.785558992
step: 6880 | loss: 1.783638701
step: 6890 | loss: 1.781718756
step: 6900 | loss: 1.779799154
step: 6910 | loss: 1.777879886
step: 6920 | loss: 1.775960948
step: 6930 | loss: 1.774042334
step: 6940 | loss: 1.772124037
step: 6950 | loss: 1.770206052
step: 6960 | loss: 1.768288374
step: 6970 | loss: 1.766370996
step: 6980 | loss: 1.764453914
step: 6990 | loss: 1.762537122
step: 7000 | loss: 1.760620614
step: 7010 | loss: 1.758704385
step: 7020 | loss: 1.756788430
step: 7030 | loss: 1.754872744
step: 7040 | loss: 1.752957321
step: 7050 | loss: 1.751042157
step: 7060 | loss: 1.749127246
step: 7070 | loss: 1.747212585
step: 7080 | loss: 1.745298167
step: 7090 | loss: 1.743383987
step: 7100 | loss: 1.741470043
step: 7110 | loss: 1.739556328
step: 7120 | loss: 1.737642838
step: 7130 | loss: 1.735729569
step: 7140 | loss: 1.733816516
step: 7150 | loss: 1.731903675
step: 7160 | loss: 1.729991041
step: 7170 | loss: 1.728078611
step: 7180 | loss: 1.726166380
step: 7190 | loss: 1.724254344
step: 7200 | loss: 1.722342499
step: 7210 | loss: 1.720430841
step: 7220 | loss: 1.718519366
step: 7230 | loss: 1.716608071
step: 7240 | loss: 1.714696951
step: 7250 | loss: 1.712786003
step: 7260 | loss: 1.710875223
step: 7270 | loss: 1.708964608
step: 7280 | loss: 1.707054153
step: 7290 | loss: 1.705143857
step: 7300 | loss: 1.703233714
step: 7310 | loss: 1.701323723
step: 7320 | loss: 1.699413879
step: 7330 | loss: 1.697504180
step: 7340 | loss: 1.695594622
step: 7350 | loss: 1.693685202
step: 7360 | loss: 1.691775917
step: 7370 | loss: 1.689866765
step: 7380 | loss: 1.687957742
step: 7390 | loss: 1.686048845
step: 7400 | loss: 1.684140072
step: 7410 | loss: 1.682231420
step: 7420 | loss: 1.680322886
step: 7430 | loss: 1.678414467
step: 7440 | loss: 1.676506162
step: 7450 | loss: 1.674597967
step: 7460 | loss: 1.672689880
step: 7470 | loss: 1.670781899
step: 7480 | loss: 1.668874021
step: 7490 | loss: 1.666966244
step: 7500 | loss: 1.665058566
step: 7510 | loss: 1.663150984
step: 7520 | loss: 1.661243496
step: 7530 | loss: 1.659336100
step: 7540 | loss: 1.657428795
step: 7550 | loss: 1.655521578
step: 7560 | loss: 1.653614447
step: 7570 | loss: 1.651707400
step: 7580 | loss: 1.649800435
step: 7590 | loss: 1.647893551
step: 7600 | loss: 1.645986746
step: 7610 | loss: 1.644080018
step: 7620 | loss: 1.642173364
step: 7630 | loss: 1.640266785
step: 7640 | loss: 1.638360277
step: 7650 | loss: 1.636453840
step: 7660 | loss: 1.634547471
step: 7670 | loss: 1.632641170
step: 7680 | loss: 1.630734935
step: 7690 | loss: 1.628828764
step: 7700 | loss: 1.626922656
step: 7710 | loss: 1.625016610
step: 7720 | loss: 1.623110624
step: 7730 | loss: 1.621204698
step: 7740 | loss: 1.619298829
step: 7750 | loss: 1.617393016
step: 7760 | loss: 1.615487259
step: 7770 | loss: 1.613581556
step: 7780 | loss: 1.611675907
step: 7790 | loss: 1.609770309
step: 7800 | loss: 1.607864762
step: 7810 | loss: 1.605959265
step: 7820 | loss: 1.604053817
step: 7830 | loss: 1.602148417
step: 7840 | loss: 1.600243064
step: 7850 | loss: 1.598337756
step: 7860 | loss: 1.596432494
step: 7870 | loss: 1.594527276
step: 7880 | loss: 1.592622101
step: 7890 | loss: 1.590716969
step: 7900 | loss: 1.588811878
step: 7910 | loss: 1.586906828
step: 7920 | loss: 1.585001819
step: 7930 | loss: 1.583096848
step: 7940 | loss: 1.581191917
step: 7950 | loss: 1.579287023
step: 7960 | loss: 1.577382167
step: 7970 | loss: 1.575477347
step: 7980 | loss: 1.573572563
step: 7990 | loss: 1.571667814
step: 8000 | loss: 1.569763100
step: 8010 | loss: 1.567858420
step: 8020 | loss: 1.565953774
step: 8030 | loss: 1.564049160
step: 8040 | loss: 1.562144579
step: 8050 | loss: 1.560240030
step: 8060 | loss: 1.558335512
step: 8070 | loss: 1.556431025
step: 8080 | loss: 1.554526568
step: 8090 | loss: 1.552622141
step: 8100 | loss: 1.550717743
step: 8110 | loss: 1.548813374
step: 8120 | loss: 1.546909034
step: 8130 | loss: 1.545004722
step: 8140 | loss: 1.543100437
step: 8150 | loss: 1.541196179
step: 8160 | loss: 1.539291949
step: 8170 | loss: 1.537387744
step: 8180 | loss: 1.535483566
step: 8190 | loss: 1.533579413
step: 8200 | loss: 1.531675286
step: 8210 | loss: 1.529771184
step: 8220 | loss: 1.527867106
step: 8230 | loss: 1.525963052
step: 8240 | loss: 1.524059022
step: 8250 | loss: 1.522155016
step: 8260 | loss: 1.520251033
step: 8270 | loss: 1.518347074
step: 8280 | loss: 1.516443136
step: 8290 | loss: 1.514539222
step: 8300 | loss: 1.512635329
step: 8310 | loss: 1.510731458
step: 8320 | loss: 1.508827609
step: 8330 | loss: 1.506923781
step: 8340 | loss: 1.505019974
step: 8350 | loss: 1.503116188
step: 8360 | loss: 1.501212422
step: 8370 | loss: 1.499308676
step: 8380 | loss: 1.497404951
step: 8390 | loss: 1.495501245
step: 8400 | loss: 1.493597559
step: 8410 | loss: 1.491693892
step: 8420 | loss: 1.489790245
step: 8430 | loss: 1.487886616
step: 8440 | loss: 1.485983006
step: 8450 | loss: 1.484079414
step: 8460 | loss: 1.482175840
step: 8470 | loss: 1.480272285
step: 8480 | loss: 1.478368747
step: 8490 | loss: 1.476465227
step: 8500 | loss: 1.474561725
step: 8510 | loss: 1.472658240
step: 8520 | loss: 1.470754771
step: 8530 | loss: 1.468851320
step: 8540 | loss: 1.466947885
step: 8550 | loss: 1.465044467
step: 8560 | loss: 1.463141065
step: 8570 | loss: 1.461237680
step: 8580 | loss: 1.459334310
step: 8590 | loss: 1.457430956
step: 8600 | loss: 1.455527618
step: 8610 | loss: 1.453624295
step: 8620 | loss: 1.451720988
step: 8630 | loss: 1.449817695
step: 8640 | loss: 1.447914418
step: 8650 | loss: 1.446011156
step: 8660 | loss: 1.444107908
step: 8670 | loss: 1.442204675
step: 8680 | loss: 1.440301456
step: 8690 | loss: 1.438398252
step: 8700 | loss: 1.436495061
step: 8710 | loss: 1.434591885
step: 8720 | loss: 1.432688722
step: 8730 | loss: 1.430785573
step: 8740 | loss: 1.428882437
step: 8750 | loss: 1.426979315
step: 8760 | loss: 1.425076206
step: 8770 | loss: 1.423173111
step: 8780 | loss: 1.421270028
step: 8790 | loss: 1.419366958
step: 8800 | loss: 1.417463901
step: 8810 | loss: 1.415560856
step: 8820 | loss: 1.413657824
step: 8830 | loss: 1.411754804
step: 8840 | loss: 1.409851797
step: 8850 | loss: 1.407948801
step: 8860 | loss: 1.406045818
step: 8870 | loss: 1.404142846
step: 8880 | loss: 1.402239886
step: 8890 | loss: 1.400336938
step: 8900 | loss: 1.398434001
step: 8910 | loss: 1.396531076
step: 8920 | loss: 1.394628161
step: 8930 | loss: 1.392725258
step: 8940 | loss: 1.390822366
step: 8950 | loss: 1.388919485
step: 8960 | loss: 1.387016615
step: 8970 | loss: 1.385113756
step: 8980 | loss: 1.383210907
step: 8990 | loss: 1.381308068
step: 9000 | loss: 1.379405240
step: 9010 | loss: 1.377502423
step: 9020 | loss: 1.375599615
step: 9030 | loss: 1.373696818
step: 9040 | loss: 1.371794030
step: 9050 | loss: 1.369891253
step: 9060 | loss: 1.367988485
step: 9070 | loss: 1.366085727
step: 9080 | loss: 1.364182978
step: 9090 | loss: 1.362280239
step: 9100 | loss: 1.360377510
step: 9110 | loss: 1.358474789
step: 9120 | loss: 1.356572078
step: 9130 | loss: 1.354669376
step: 9140 | loss: 1.352766683
step: 9150 | loss: 1.350863999
step: 9160 | loss: 1.348961324
step: 9170 | loss: 1.347058658
step: 9180 | loss: 1.345156000
step: 9190 | loss: 1.343253351
step: 9200 | loss: 1.341350710
step: 9210 | loss: 1.339448078
step: 9220 | loss: 1.337545454
step: 9230 | loss: 1.335642839
step: 9240 | loss: 1.333740231
step: 9250 | loss: 1.331837632
step: 9260 | loss: 1.329935041
step: 9270 | loss: 1.328032457
step: 9280 | loss: 1.326129882
step: 9290 | loss: 1.324227314
step: 9300 | loss: 1.322324754
step: 9310 | loss: 1.320422201
step: 9320 | loss: 1.318519656
step: 9330 | loss: 1.316617119
step: 9340 | loss: 1.314714589
step: 9350 | loss: 1.312812066
step: 9360 | loss: 1.310909551
step: 9370 | loss: 1.309007042
step: 9380 | loss: 1.307104541
step: 9390 | loss: 1.305202047
step: 9400 | loss: 1.303299560
step: 9410 | loss: 1.301397079
step: 9420 | loss: 1.299494606
step: 9430 | loss: 1.297592139
step: 9440 | loss: 1.295689679
step: 9450 | loss: 1.293787225
step: 9460 | loss: 1.291884778
step: 9470 | loss: 1.289982338
step: 9480 | loss: 1.288079904
step: 9490 | loss: 1.286177476
step: 9500 | loss: 1.284275055
step: 9510 | loss: 1.282372640
step: 9520 | loss: 1.280470231
step: 9530 | loss: 1.278567828
step: 9540 | loss: 1.276665431
step: 9550 | loss: 1.274763040
step: 9560 | loss: 1.272860656
step: 9570 | loss: 1.270958277
step: 9580 | loss: 1.269055903
step: 9590 | loss: 1.267153536
step: 9600 | loss: 1.265251174
step: 9610 | loss: 1.263348818
step: 9620 | loss: 1.261446468
step: 9630 | loss: 1.259544123
step: 9640 | loss: 1.257641783
step: 9650 | loss: 1.255739449
step: 9660 | loss: 1.253837120
step: 9670 | loss: 1.251934797
step: 9680 | loss: 1.250032479
step: 9690 | loss: 1.248130166
step: 9700 | loss: 1.246227858
step: 9710 | loss: 1.244325555
step: 9720 | loss: 1.242423258
step: 9730 | loss: 1.240520965
step: 9740 | loss: 1.238618677
step: 9750 | loss: 1.236716395
step: 9760 | loss: 1.234814117
step: 9770 | loss: 1.232911843
step: 9780 | loss: 1.231009575
step: 9790 | loss: 1.229107311
step: 9800 | loss: 1.227205052
step: 9810 | loss: 1.225302798
step: 9820 | loss: 1.223400548
step: 9830 | loss: 1.221498303
step: 9840 | loss: 1.219596062
step: 9850 | loss: 1.217693826
step: 9860 | loss: 1.215791594
step: 9870 | loss: 1.213889366
step: 9880 | loss: 1.211987143
step: 9890 | loss: 1.210084924
step: 9900 | loss: 1.208182709
step: 9910 | loss: 1.206280498
step: 9920 | loss: 1.204378292
step: 9930 | loss: 1.202476089
step: 9940 | loss: 1.200573891
step: 9950 | loss: 1.198671696
step: 9960 | loss: 1.196769506
step: 9970 | loss: 1.194867319
step: 9980 | loss: 1.192965137
step: 9990 | loss: 1.191062958
step: 10000 | loss: 1.189160783
step: 10010 | loss: 1.187258612
step: 10020 | loss: 1.185356445
step: 10030 | loss: 1.183454281
step: 10040 | loss: 1.181552121
step: 10050 | loss: 1.179649964
step: 10060 | loss: 1.177747812
step: 10070 | loss: 1.175845662
step: 10080 | loss: 1.173943517
step: 10090 | loss: 1.172041374
step: 10100 | loss: 1.170139236
step: 10110 | loss: 1.168237100
step: 10120 | loss: 1.166334968
step: 10130 | loss: 1.164432840
step: 10140 | loss: 1.162530714
step: 10150 | loss: 1.160628592
step: 10160 | loss: 1.158726474
step: 10170 | loss: 1.156824358
step: 10180 | loss: 1.154922246
step: 10190 | loss: 1.153020136
step: 10200 | loss: 1.151118030
step: 10210 | loss: 1.149215927
step: 10220 | loss: 1.147313827
step: 10230 | loss: 1.145411730
step: 10240 | loss: 1.143509637
step: 10250 | loss: 1.141607546
step: 10260 | loss: 1.139705458
step: 10270 | loss: 1.137803373
step: 10280 | loss: 1.135901290
step: 10290 | loss: 1.133999211
step: 10300 | loss: 1.132097135
step: 10310 | loss: 1.130195061
step: 10320 | loss: 1.128292990
step: 10330 | loss: 1.126390922
step: 10340 | loss: 1.124488856
step: 10350 | loss: 1.122586793
step: 10360 | loss: 1.120684733
step: 10370 | loss: 1.118782676
step: 10380 | loss: 1.116880621
step: 10390 | loss: 1.114978568
step: 10400 | loss: 1.113076519
step: 10410 | loss: 1.111174472
step: 10420 | loss: 1.109272427
step: 10430 | loss: 1.107370385
step: 10440 | loss: 1.105468345
step: 10450 | loss: 1.103566307
step: 10460 | loss: 1.101664272
step: 10470 | loss: 1.099762240
step: 10480 | loss: 1.097860210
step: 10490 | loss: 1.095958182
step: 10500 | loss: 1.094056156
step: 10510 | loss: 1.092154133
step: 10520 | loss: 1.090252112
step: 10530 | loss: 1.088350093
step: 10540 | loss: 1.086448077
step: 10550 | loss: 1.084546063
step: 10560 | loss: 1.082644050
step: 10570 | loss: 1.080742040
step: 10580 | loss: 1.078840033
step: 10590 | loss: 1.076938027
step: 10600 | loss: 1.075036023
step: 10610 | loss: 1.073134022
step: 10620 | loss: 1.071232022
step: 10630 | loss: 1.069330025
step: 10640 | loss: 1.067428029
step: 10650 | loss: 1.065526036
step: 10660 | loss: 1.063624044
step: 10670 | loss: 1.061722055
step: 10680 | loss: 1.059820067
step: 10690 | loss: 1.057918081
step: 10700 | loss: 1.056016097
step: 10710 | loss: 1.054114116
step: 10720 | loss: 1.052212135
step: 10730 | loss: 1.050310157
step: 10740 | loss: 1.048408181
step: 10750 | loss: 1.046506206
step: 10760 | loss: 1.044604233
step: 10770 | loss: 1.042702262
step: 10780 | loss: 1.040800293
step: 10790 | loss: 1.038898325
step: 10800 | loss: 1.036996359
step: 10810 | loss: 1.035094395
step: 10820 | loss: 1.033192433
step: 10830 | loss: 1.031290472
step: 10840 | loss: 1.029388513
step: 10850 | loss: 1.027486555
step: 10860 | loss: 1.025584599
step: 10870 | loss: 1.023682645
step: 10880 | loss: 1.021780692
step: 10890 | loss: 1.019878741
step: 10900 | loss: 1.017976791
step: 10910 | loss: 1.016074843
step: 10920 | loss: 1.014172896
step: 10930 | loss: 1.012270951
step: 10940 | loss: 1.010369008
step: 10950 | loss: 1.008467065
step: 10960 | loss: 1.006565125
step: 10970 | loss: 1.004663186
step: 10980 | loss: 1.002761248
step: 10990 | loss: 1.000859311
step: 11000 | loss: 0.998957376
step: 11010 | loss: 0.997055443
step: 11020 | loss: 0.995153511
step: 11030 | loss: 0.993251580
step: 11040 | loss: 0.991349650
step: 11050 | loss: 0.989447722
step: 11060 | loss: 0.987545795
step: 11070 | loss: 0.985643870
step: 11080 | loss: 0.983741945
step: 11090 | loss: 0.981840022
step: 11100 | loss: 0.979938101
step: 11110 | loss: 0.978036180
step: 11120 | loss: 0.976134261
step: 11130 | loss: 0.974232343
step: 11140 | loss: 0.972330426
step: 11150 | loss: 0.970428511
step: 11160 | loss: 0.968526596
step: 11170 | loss: 0.966624683
step: 11180 | loss: 0.964722771
step: 11190 | loss: 0.962820860
step: 11200 | loss: 0.960918951
step: 11210 | loss: 0.959017042
step: 11220 | loss: 0.957115135
step: 11230 | loss: 0.955213228
step: 11240 | loss: 0.953311323
step: 11250 | loss: 0.951409419
step: 11260 | loss: 0.949507516
step: 11270 | loss: 0.947605614
step: 11280 | loss: 0.945703713
step: 11290 | loss: 0.943801813
step: 11300 | loss: 0.941899915
step: 11310 | loss: 0.939998017
step: 11320 | loss: 0.938096120
step: 11330 | loss: 0.936194224
step: 11340 | loss: 0.934292329
step: 11350 | loss: 0.932390436
step: 11360 | loss: 0.930488543
step: 11370 | loss: 0.928586651
step: 11380 | loss: 0.926684760
step: 11390 | loss: 0.924782870
step: 11400 | loss: 0.922880981
step: 11410 | loss: 0.920979093
step: 11420 | loss: 0.919077206
step: 11430 | loss: 0.917175320
step: 11440 | loss: 0.915273435
step: 11450 | loss: 0.913371550
step: 11460 | loss: 0.911469667
step: 11470 | loss: 0.909567784
step: 11480 | loss: 0.907665902
step: 11490 | loss: 0.905764021
step: 11500 | loss: 0.903862141
step: 11510 | loss: 0.901960262
step: 11520 | loss: 0.900058384
step: 11530 | loss: 0.898156506
step: 11540 | loss: 0.896254629
step: 11550 | loss: 0.894352753
step: 11560 | loss: 0.892450878
step: 11570 | loss: 0.890549004
step: 11580 | loss: 0.888647130
step: 11590 | loss: 0.886745257
step: 11600 | loss: 0.884843385
step: 11610 | loss: 0.882941514
step: 11620 | loss: 0.881039644
step: 11630 | loss: 0.879137774
step: 11640 | loss: 0.877235905
step: 11650 | loss: 0.875334036
step: 11660 | loss: 0.873432169
step: 11670 | loss: 0.871530302
step: 11680 | loss: 0.869628436
step: 11690 | loss: 0.867726570
step: 11700 | loss: 0.865824706
step: 11710 | loss: 0.863922842
step: 11720 | loss: 0.862020978
step: 11730 | loss: 0.860119116
step: 11740 | loss: 0.858217254
step: 11750 | loss: 0.856315392
step: 11760 | loss: 0.854413532
step: 11770 | loss: 0.852511671
step: 11780 | loss: 0.850609812
step: 11790 | loss: 0.848707953
step: 11800 | loss: 0.846806095
step: 11810 | loss: 0.844904238
step: 11820 | loss: 0.843002381
step: 11830 | loss: 0.841100524
step: 11840 | loss: 0.839198669
step: 11850 | loss: 0.837296814
step: 11860 | loss: 0.835394959
step: 11870 | loss: 0.833493105
step: 11880 | loss: 0.831591252
step: 11890 | loss: 0.829689399
step: 11900 | loss: 0.827787547
step: 11910 | loss: 0.825885695
step: 11920 | loss: 0.823983844
step: 11930 | loss: 0.822081994
step: 11940 | loss: 0.820180144
step: 11950 | loss: 0.818278294
step: 11960 | loss: 0.816376445
step: 11970 | loss: 0.814474597
step: 11980 | loss: 0.812572749
step: 11990 | loss: 0.810670902
step: 12000 | loss: 0.808769055
step: 12010 | loss: 0.806867209
step: 12020 | loss: 0.804965363
step: 12030 | loss: 0.803063518
step: 12040 | loss: 0.801161673
step: 12050 | loss: 0.799259829
step: 12060 | loss: 0.797357985
step: 12070 | loss: 0.795456142
step: 12080 | loss: 0.793554299
step: 12090 | loss: 0.791652457
step: 12100 | loss: 0.789750615
step: 12110 | loss: 0.787848773
step: 12120 | loss: 0.785946932
step: 12130 | loss: 0.784045092
step: 12140 | loss: 0.782143252
step: 12150 | loss: 0.780241412
step: 12160 | loss: 0.778339573
step: 12170 | loss: 0.776437734
step: 12180 | loss: 0.774535896
step: 12190 | loss: 0.772634058
step: 12200 | loss: 0.770732220
step: 12210 | loss: 0.768830383
step: 12220 | loss: 0.766928546
step: 12230 | loss: 0.765026710
step: 12240 | loss: 0.763124874
step: 12250 | loss: 0.761223039
step: 12260 | loss: 0.759321204
step: 12270 | loss: 0.757419369
step: 12280 | loss: 0.755517535
step: 12290 | loss: 0.753615701
step: 12300 | loss: 0.751713868
step: 12310 | loss: 0.749812035
step: 12320 | loss: 0.747910202
step: 12330 | loss: 0.746008369
step: 12340 | loss: 0.744106537
step: 12350 | loss: 0.742204706
step: 12360 | loss: 0.740302875
step: 12370 | loss: 0.738401044
step: 12380 | loss: 0.736499213
step: 12390 | loss: 0.734597383
step: 12400 | loss: 0.732695553
step: 12410 | loss: 0.730793723
step: 12420 | loss: 0.728891894
step: 12430 | loss: 0.726990065
step: 12440 | loss: 0.725088237
step: 12450 | loss: 0.723186409
step: 12460 | loss: 0.721284581
step: 12470 | loss: 0.719382753
step: 12480 | loss: 0.717480926
step: 12490 | loss: 0.715579099
step: 12500 | loss: 0.713677272
step: 12510 | loss: 0.711775446
step: 12520 | loss: 0.709873620
step: 12530 | loss: 0.707971795
step: 12540 | loss: 0.706069969
step: 12550 | loss: 0.704168144
step: 12560 | loss: 0.702266319
step: 12570 | loss: 0.700364495
step: 12580 | loss: 0.698462671
step: 12590 | loss: 0.696560847
step: 12600 | loss: 0.694659023
step: 12610 | loss: 0.692757200
step: 12620 | loss: 0.690855377
step: 12630 | loss: 0.688953554
step: 12640 | loss: 0.687051731
step: 12650 | loss: 0.685149909
step: 12660 | loss: 0.683248087
step: 12670 | loss: 0.681346265
step: 12680 | loss: 0.679444444
step: 12690 | loss: 0.677542622
step: 12700 | loss: 0.675640802
step: 12710 | loss: 0.673738981
step: 12720 | loss: 0.671837160
step: 12730 | loss: 0.669935340
step: 12740 | loss: 0.668033520
step: 12750 | loss: 0.666131700
step: 12760 | loss: 0.664229881
step: 12770 | loss: 0.662328062
step: 12780 | loss: 0.660426243
step: 12790 | loss: 0.658524424
step: 12800 | loss: 0.656622605
step: 12810 | loss: 0.654720787
step: 12820 | loss: 0.652818969
step: 12830 | loss: 0.650917151
step: 12840 | loss: 0.649015334
step: 12850 | loss: 0.647113516
step: 12860 | loss: 0.645211699
step: 12870 | loss: 0.643309882
step: 12880 | loss: 0.641408065
step: 12890 | loss: 0.639506249
step: 12900 | loss: 0.637604432
step: 12910 | loss: 0.635702616
step: 12920 | loss: 0.633800800
step: 12930 | loss: 0.631898984
step: 12940 | loss: 0.629997169
step: 12950 | loss: 0.628095354
step: 12960 | loss: 0.626193538
step: 12970 | loss: 0.624291724
step: 12980 | loss: 0.622389909
step: 12990 | loss: 0.620488094
step: 13000 | loss: 0.618586280
step: 13010 | loss: 0.616684466
step: 13020 | loss: 0.614782652
step: 13030 | loss: 0.612880838
step: 13040 | loss: 0.610979024
step: 13050 | loss: 0.609077211
step: 13060 | loss: 0.607175398
step: 13070 | loss: 0.605273585
step: 13080 | loss: 0.603371772
step: 13090 | loss: 0.601469959
step: 13100 | loss: 0.599568146
step: 13110 | loss: 0.597666334
step: 13120 | loss: 0.595764522
step: 13130 | loss: 0.593862710
step: 13140 | loss: 0.591960898
step: 13150 | loss: 0.590059086
step: 13160 | loss: 0.588157274
step: 13170 | loss: 0.586255463
step: 13180 | loss: 0.584353652
step: 13190 | loss: 0.582451841
step: 13200 | loss: 0.580550030
step: 13210 | loss: 0.578648219
step: 13220 | loss: 0.576746408
step: 13230 | loss: 0.574844598
step: 13240 | loss: 0.572942787
step: 13250 | loss: 0.571040977
step: 13260 | loss: 0.569139167
step: 13270 | loss: 0.567237357
step: 13280 | loss: 0.565335548
step: 13290 | loss: 0.563433738
step: 13300 | loss: 0.561531928
step: 13310 | loss: 0.559630119
step: 13320 | loss: 0.557728310
step: 13330 | loss: 0.555826501
step: 13340 | loss: 0.553924692
step: 13350 | loss: 0.552022883
step: 13360 | loss: 0.550121075
step: 13370 | loss: 0.548219266
step: 13380 | loss: 0.546317458
step: 13390 | loss: 0.544415649
step: 13400 | loss: 0.542513841
step: 13410 | loss: 0.540612033
step: 13420 | loss: 0.538710225
step: 13430 | loss: 0.536808417
step: 13440 | loss: 0.534906610
step: 13450 | loss: 0.533004802
step: 13460 | loss: 0.531102995
step: 13470 | loss: 0.529201188
step: 13480 | loss: 0.527299380
step: 13490 | loss: 0.525397573
step: 13500 | loss: 0.523495766
step: 13510 | loss: 0.521593960
step: 13520 | loss: 0.519692153
step: 13530 | loss: 0.517790346
step: 13540 | loss: 0.515888540
step: 13550 | loss: 0.513986733
step: 13560 | loss: 0.512084927
step: 13570 | loss: 0.510183121
step: 13580 | loss: 0.508281315
step: 13590 | loss: 0.506379509
step: 13600 | loss: 0.504477703
step: 13610 | loss: 0.502575897
step: 13620 | loss: 0.500674092
step: 13630 | loss: 0.498772286
step: 13640 | loss: 0.496870481
step: 13650 | loss: 0.494968675
step: 13660 | loss: 0.493066870
step: 13670 | loss: 0.491165065
step: 13680 | loss: 0.489263260
step: 13690 | loss: 0.487361455
step: 13700 | loss: 0.485459650
step: 13710 | loss: 0.483557845
step: 13720 | loss: 0.481656041
step: 13730 | loss: 0.479754236
step: 13740 | loss: 0.477852432
step: 13750 | loss: 0.475950627
step: 13760 | loss: 0.474048823
step: 13770 | loss: 0.472147019
step: 13780 | loss: 0.470245214
step: 13790 | loss: 0.468343410
step: 13800 | loss: 0.466441606
step: 13810 | loss: 0.464539803
step: 13820 | loss: 0.462637999
step: 13830 | loss: 0.460736195
step: 13840 | loss: 0.458834391
step: 13850 | loss: 0.456932588
step: 13860 | loss: 0.455030784
step: 13870 | loss: 0.453128981
step: 13880 | loss: 0.451227178
step: 13890 | loss: 0.449325374
step: 13900 | loss: 0.447423571
step: 13910 | loss: 0.445521768
step: 13920 | loss: 0.443619965
step: 13930 | loss: 0.441718162
step: 13940 | loss: 0.439816359
step: 13950 | loss: 0.437914556
step: 13960 | loss: 0.436012754
step: 13970 | loss: 0.434110951
step: 13980 | loss: 0.432209148
step: 13990 | loss: 0.430307346
step: 14000 | loss: 0.428405544
step: 14010 | loss: 0.426503741
step: 14020 | loss: 0.424601939
step: 14030 | loss: 0.422700137
step: 14040 | loss: 0.420798334
step: 14050 | loss: 0.418896532
step: 14060 | loss: 0.416994730
step: 14070 | loss: 0.415092928
step: 14080 | loss: 0.413191126
step: 14090 | loss: 0.411289325
step: 14100 | loss: 0.409387523
step: 14110 | loss: 0.407485721
step: 14120 | loss: 0.405583919
step: 14130 | loss: 0.403682118
step: 14140 | loss: 0.401780316
step: 14150 | loss: 0.399878515
step: 14160 | loss: 0.397976713
step: 14170 | loss: 0.396074912
step: 14180 | loss: 0.394173111
step: 14190 | loss: 0.392271310
step: 14200 | loss: 0.390369508
step: 14210 | loss: 0.388467707
step: 14220 | loss: 0.386565906
step: 14230 | loss: 0.384664105
step: 14240 | loss: 0.382762304
step: 14250 | loss: 0.380860503
step: 14260 | loss: 0.378958702
step: 14270 | loss: 0.377056902
step: 14280 | loss: 0.375155101
step: 14290 | loss: 0.373253300
step: 14300 | loss: 0.371351500
step: 14310 | loss: 0.369449699
step: 14320 | loss: 0.367547898
step: 14330 | loss: 0.365646098
step: 14340 | loss: 0.363744298
step: 14350 | loss: 0.361842497
step: 14360 | loss: 0.359940697
step: 14370 | loss: 0.358038896
step: 14380 | loss: 0.356137096
step: 14390 | loss: 0.354235296
step: 14400 | loss: 0.352333496
step: 14410 | loss: 0.350431696
step: 14420 | loss: 0.348529896
step: 14430 | loss: 0.346628096
step: 14440 | loss: 0.344726296
step: 14450 | loss: 0.342824496
step: 14460 | loss: 0.340922696
step: 14470 | loss: 0.339020896
step: 14480 | loss: 0.337119096
step: 14490 | loss: 0.335217297
step: 14500 | loss: 0.333315497
step: 14510 | loss: 0.331413697
step: 14520 | loss: 0.329511898
step: 14530 | loss: 0.327610098
step: 14540 | loss: 0.325708298
step: 14550 | loss: 0.323806499
step: 14560 | loss: 0.321904699
step: 14570 | loss: 0.320002900
step: 14580 | loss: 0.318101101
step: 14590 | loss: 0.316199301
step: 14600 | loss: 0.314297502
step: 14610 | loss: 0.312395703
step: 14620 | loss: 0.310493903
step: 14630 | loss: 0.308592104
step: 14640 | loss: 0.306690305
step: 14650 | loss: 0.304788506
step: 14660 | loss: 0.302886707
step: 14670 | loss: 0.300984908
step: 14680 | loss: 0.299083109
step: 14690 | loss: 0.297181310
step: 14700 | loss: 0.295279511
step: 14710 | loss: 0.293377712
step: 14720 | loss: 0.291475913
step: 14730 | loss: 0.289574114
step: 14740 | loss: 0.287672315
step: 14750 | loss: 0.285770517
step: 14760 | loss: 0.283868718
step: 14770 | loss: 0.281966919
step: 14780 | loss: 0.280065120
step: 14790 | loss: 0.278163322
step: 14800 | loss: 0.276261523
step: 14810 | loss: 0.274359725
step: 14820 | loss: 0.272457926
step: 14830 | loss: 0.270556128
step: 14840 | loss: 0.268654329
step: 14850 | loss: 0.266752531
step: 14860 | loss: 0.264850732
step: 14870 | loss: 0.262948934
step: 14880 | loss: 0.261047135
step: 14890 | loss: 0.259145337
step: 14900 | loss: 0.257243539
step: 14910 | loss: 0.255341740
step: 14920 | loss: 0.253439942
step: 14930 | loss: 0.251538144
step: 14940 | loss: 0.249636346
step: 14950 | loss: 0.247734547
step: 14960 | loss: 0.245832749
step: 14970 | loss: 0.243930951
step: 14980 | loss: 0.242029153
step: 14990 | loss: 0.240127355
step: 15000 | loss: 0.238225557
step: 15010 | loss: 0.236323759
step: 15020 | loss: 0.234421961
step: 15030 | loss: 0.232520163
step: 15040 | loss: 0.230618365
step: 15050 | loss: 0.228716567
step: 15060 | loss: 0.226814769
step: 15070 | loss: 0.224912971
step: 15080 | loss: 0.223011173
step: 15090 | loss: 0.221109376
step: 15100 | loss: 0.219207578
step: 15110 | loss: 0.217305780
step: 15120 | loss: 0.215403982
step: 15130 | loss: 0.213502184
step: 15140 | loss: 0.211600387
step: 15150 | loss: 0.209698589
step: 15160 | loss: 0.207796791
step: 15170 | loss: 0.205894994
step: 15180 | loss: 0.203993196
step: 15190 | loss: 0.202091398
step: 15200 | loss: 0.200189601
step: 15210 | loss: 0.198287803
step: 15220 | loss: 0.196386006
step: 15230 | loss: 0.194484208
step: 15240 | loss: 0.192582411
step: 15250 | loss: 0.190680613
step: 15260 | loss: 0.188778816
step: 15270 | loss: 0.186877018
step: 15280 | loss: 0.184975221
step: 15290 | loss: 0.183073423
step: 15300 | loss: 0.181171626
step: 15310 | loss: 0.179269829
step: 15320 | loss: 0.177368031
step: 15330 | loss: 0.175466234
step: 15340 | loss: 0.173564437
step: 15350 | loss: 0.171662639
step: 15360 | loss: 0.169760842
step: 15370 | loss: 0.167859045
step: 15380 | loss: 0.165957247
step: 15390 | loss: 0.164055450
step: 15400 | loss: 0.162153653
step: 15410 | loss: 0.160251856
step: 15420 | loss: 0.158350059
step: 15430 | loss: 0.156448262
step: 15440 | loss: 0.154546464
step: 15450 | loss: 0.152644667
step: 15460 | loss: 0.150742870
step: 15470 | loss: 0.148841073
step: 15480 | loss: 0.146939276
step: 15490 | loss: 0.145037479
step: 15500 | loss: 0.143135682
step: 15510 | loss: 0.141233885
step: 15520 | loss: 0.139332088
step: 15530 | loss: 0.137430291
step: 15540 | loss: 0.135528494
step: 15550 | loss: 0.133626697
step: 15560 | loss: 0.131724900
step: 15570 | loss: 0.129823103
step: 15580 | loss: 0.127921306
step: 15590 | loss: 0.126019509
step: 15600 | loss: 0.124117712
step: 15610 | loss: 0.122215915
step: 15620 | loss: 0.120314118
step: 15630 | loss: 0.118412321
step: 15640 | loss: 0.116510525
step: 15650 | loss: 0.114608728
step: 15660 | loss: 0.112706931
step: 15670 | loss: 0.110805134
step: 15680 | loss: 0.108903337
step: 15690 | loss: 0.107001541
step: 15700 | loss: 0.105099744
step: 15710 | loss: 0.103197947
step: 15720 | loss: 0.101296150
step: 15730 | loss: 0.099394354
step: 15740 | loss: 0.097492557
step: 15750 | loss: 0.095590760
step: 15760 | loss: 0.093688963
step: 15770 | loss: 0.091787167
step: 15780 | loss: 0.089885370
step: 15790 | loss: 0.087983573
step: 15800 | loss: 0.086081777
step: 15810 | loss: 0.084179980
step: 15820 | loss: 0.082278183
step: 15830 | loss: 0.080376387
step: 15840 | loss: 0.078474590
step: 15850 | loss: 0.076572794
step: 15860 | loss: 0.074670997
step: 15870 | loss: 0.072769200
step: 15880 | loss: 0.070867404
step: 15890 | loss: 0.068965607
step: 15900 | loss: 0.067063811
step: 15910 | loss: 0.065162014
step: 15920 | loss: 0.063260218
step: 15930 | loss: 0.061358421
step: 15940 | loss: 0.059456625
step: 15950 | loss: 0.057554828
step: 15960 | loss: 0.055653032
step: 15970 | loss: 0.053751235
step: 15980 | loss: 0.051849439
step: 15990 | loss: 0.049947642
step: 16000 | loss: 0.048045846
step: 16010 | loss: 0.046144050
step: 16020 | loss: 0.044242253
step: 16030 | loss: 0.042340457
step: 16040 | loss: 0.040438660
step: 16050 | loss: 0.038536864
step: 16060 | loss: 0.036635067
step: 16070 | loss: 0.034733271
step: 16080 | loss: 0.032831475
step: 16090 | loss: 0.030929678
step: 16100 | loss: 0.029027882
step: 16110 | loss: 0.027126086
step: 16120 | loss: 0.025224289
step: 16130 | loss: 0.023322493
step: 16140 | loss: 0.021420697
step: 16150 | loss: 0.019518900
step: 16160 | loss: 0.017617104
step: 16170 | loss: 0.015715308
step: 16180 | loss: 0.013813511
step: 16190 | loss: 0.011911715
step: 16200 | loss: 0.010009919
step: 16210 | loss: 0.008108123
step: 16220 | loss: 0.006206326
step: 16230 | loss: 0.004304530
step: 16240 | loss: 0.002402734
- final loss: 0.000881
-> compiled owl-opt.0.0.1
-> removed owl-opt.0.0.1
-> installed owl-opt.0.0.1
Done.
# To update the current shell environment, run: eval $(opam env)
2026-03-21 21:06.44 ---> saved as "ecf46a3212e2d9d1c039a5b883870ba6f441f3435a01e0cf88174ef5a7aa3060"
Job succeeded
2026-03-21 21:06.52: Job succeeded