mirrors/icefall
1
0
Fork 0
mirror of https://github.com/k2-fsa/icefall.git synced 2025-09-19 05:54:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix issue with max_eig formula; restore cov_min[1]=0.0025.

Browse Source
This commit is contained in:
Daniel Povey 2022-07-31 18:29:44 +08:00
parent 3590c2fc42
commit e2cc09a8c6
1 changed files with 8 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
View File

@ -163,7 +163,7 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
lr=3e-02,
betas=(0.9, 0.98),
size_lr_scale=0.1,
cov_min=(0.025, 0.0, 0.02, 0.0001),
cov_min=(0.025, 0.0025, 0.02, 0.0001),
cov_max=(10.0, 80.0, 5.0, 400.0),
cov_pow=(1.0, 1.0, 1.0, 1.0),
param_rms_smooth0=0.4,
@ -830,8 +830,8 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
# which case X is extremely tiny).
# eig_ceil is the maximum possible eigenvalue that X could possibly
# have at this time.
eig_ceil = X.shape[-1]
# have at this time, equal to num_blocks * block_size.
eig_ceil = X.shape[1] * X.shape[3]
# the next statement wslightly adjusts the target to be the same as
# what the baseline function, eig -> 1./(1./eig + 1./max_eig) would
@ -886,10 +886,11 @@ param_rms_smooth1: Smoothing proportion for parameter matrix, if assumed rank of
X /= mean_eig
if min_eig != 0.0:
# should be inverting as block-diag..
X += min_eig * M.inverse()
X = X * (1.0-min_eig) + min_eig * M.inverse()
eig_ceil = X.shape[-1]
# eig_ceil is the maximum possible eigenvalue that X could possibly
# have at this time, equal to num_blocks * block_size.
eig_ceil = X.shape[1] * X.shape[3]
# the next statement wslightly adjusts the target to be the same as
# what the baseline function, eig -> 1./(1./eig + 1./max_eig) would
@ -1859,7 +1860,7 @@ def _test_eve_cain():
fix_random_seed(42)
Linear = torch.nn.Linear if iter == 0 else ScaledLinear
hidden_dim = 200
hidden_dim = 300
m = torch.nn.Sequential(Linear(E, hidden_dim),
torch.nn.PReLU(),
Linear(hidden_dim, hidden_dim),