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Posted to commits@systemml.apache.org by du...@apache.org on 2017/09/20 20:31:29 UTC
systemml git commit: [SYSTEMML-1921] Bug in backward of recurrent
layers when only returning final output
Repository: systemml
Updated Branches:
refs/heads/master a134997e6 -> 3acf786d4
[SYSTEMML-1921] Bug in backward of recurrent layers when only returning final output
This fixes a typo in the `backward` functions of the recurrent layers
that causes an error when `given_sequences` is false. Specifically,
when only the final time-step's output was desired from the forward
functions, `out` and `dout` will be of shape `(N, M)`, and within
the `backward` functions, `dout` will be padded with zeros for the
earlier time-steps to enlarge the shape to `(N, T*M)`. While the goal
of the logic and the associated comments were correct, there was a typo
in which the shape was instead enlarged to `(T*D)`.
This also updates the associated gradient checks to also run the same
test with `return_sequences = FALSE`.
Project: http://git-wip-us.apache.org/repos/asf/systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/3acf786d
Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/3acf786d
Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/3acf786d
Branch: refs/heads/master
Commit: 3acf786d4ccee15882950738e5259440de7b9a51
Parents: a134997
Author: Mike Dusenberry <mw...@us.ibm.com>
Authored: Wed Sep 20 13:29:52 2017 -0700
Committer: Mike Dusenberry <mw...@us.ibm.com>
Committed: Wed Sep 20 13:29:52 2017 -0700
----------------------------------------------------------------------
scripts/nn/layers/lstm.dml | 2 +-
scripts/nn/layers/rnn.dml | 2 +-
scripts/nn/test/grad_check.dml | 396 +++++++++++++++++++-----------------
3 files changed, 212 insertions(+), 188 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/systemml/blob/3acf786d/scripts/nn/layers/lstm.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/layers/lstm.dml b/scripts/nn/layers/lstm.dml
index a75add4..7c21f2b 100644
--- a/scripts/nn/layers/lstm.dml
+++ b/scripts/nn/layers/lstm.dml
@@ -169,7 +169,7 @@ backward = function(matrix[double] dout, matrix[double] dc,
dct = dc
if (!given_sequences) {
# only given dout for output at final timestep, so prepend empty douts for all other timesteps
- dout = cbind(matrix(0, rows=N, cols=(T-1)*D), dout) # shape (N, T*M)
+ dout = cbind(matrix(0, rows=N, cols=(T-1)*M), dout) # shape (N, T*M)
}
t = T
http://git-wip-us.apache.org/repos/asf/systemml/blob/3acf786d/scripts/nn/layers/rnn.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/layers/rnn.dml b/scripts/nn/layers/rnn.dml
index 3c6faae..a1e0bb4 100644
--- a/scripts/nn/layers/rnn.dml
+++ b/scripts/nn/layers/rnn.dml
@@ -119,7 +119,7 @@ backward = function(matrix[double] dout, matrix[double] X, matrix[double] W, mat
dout0 = matrix(0, rows=N, cols=M)
if (!given_sequences) {
# only given dout for output at final timestep, so prepend empty douts for all other timesteps
- dout = cbind(matrix(0, rows=N, cols=(T-1)*D), dout) # shape (N, T*M)
+ dout = cbind(matrix(0, rows=N, cols=(T-1)*M), dout) # shape (N, T*M)
}
t = T
http://git-wip-us.apache.org/repos/asf/systemml/blob/3acf786d/scripts/nn/test/grad_check.dml
----------------------------------------------------------------------
diff --git a/scripts/nn/test/grad_check.dml b/scripts/nn/test/grad_check.dml
index c969a98..41a8bc6 100644
--- a/scripts/nn/test/grad_check.dml
+++ b/scripts/nn/test/grad_check.dml
@@ -1211,138 +1211,150 @@ lstm = function() {
# Generate data
N = 3 # num examples
- D = 10 # num features
- T = 15 # num timesteps (sequence length)
- M = 5 # num neurons
- return_seq = TRUE
+ D = 5 # num features
+ T = 15 # num timesteps (sequence length)
+ M = 10 # num neurons
X = rand(rows=N, cols=T*D)
- y = rand(rows=N, cols=T*M)
yc = rand(rows=N, cols=M)
out0 = rand(rows=N, cols=M)
c0 = rand(rows=N, cols=M)
[W, b, dummy, dummy2] = lstm::init(N, D, M)
- # Compute analytical gradients of loss wrt parameters
- [out, c, cache_out, cache_c, cache_ifog] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- dout = l2_loss::backward(out, y)
- dc = l2_loss::backward(c, yc)
- [dX, dW, db, dout0, dc0] = lstm::backward(dout, dc, X, W, b, T, D, return_seq, out0, c0,
- cache_out, cache_c, cache_ifog)
+ # test with (1) outputs from all timesteps, and (2) output from the final timestep
+ for (i in 1:2) {
+ if (i == 1) {
+ return_seq = TRUE
+ y = rand(rows=N, cols=T*M)
+ }
+ else {
+ return_seq = FALSE
+ y = rand(rows=N, cols=M)
+ }
- # Grad check
- h = 1e-5
- print(" - Grad checking X.")
- for (i in 1:nrow(X)) {
- for (j in 1:ncol(X)) {
- # Compute numerical derivative
- old = as.scalar(X[i,j])
- X[i,j] = old - h
- [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outmh = l2_loss::forward(outmh, y)
- loss_cmh = l2_loss::forward(cmh, yc)
- lossmh = loss_outmh + loss_cmh
- X[i,j] = old + h
- [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outph = l2_loss::forward(outph, y)
- loss_cph = l2_loss::forward(cph, yc)
- lossph = loss_outph + loss_cph
- X[i,j] = old # reset
- dX_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking with return_seq = " + return_seq)
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dX[i,j]), dX_num, lossph, lossmh)
+ # Compute analytical gradients of loss wrt parameters
+ [out, c, cache_out, cache_c, cache_ifog] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ dout = l2_loss::backward(out, y)
+ dc = l2_loss::backward(c, yc)
+ [dX, dW, db, dout0, dc0] = lstm::backward(dout, dc, X, W, b, T, D, return_seq, out0, c0,
+ cache_out, cache_c, cache_ifog)
+
+ # Grad check
+ h = 1e-5
+ print(" - Grad checking X.")
+ for (i in 1:nrow(X)) {
+ for (j in 1:ncol(X)) {
+ # Compute numerical derivative
+ old = as.scalar(X[i,j])
+ X[i,j] = old - h
+ [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outmh = l2_loss::forward(outmh, y)
+ loss_cmh = l2_loss::forward(cmh, yc)
+ lossmh = loss_outmh + loss_cmh
+ X[i,j] = old + h
+ [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outph = l2_loss::forward(outph, y)
+ loss_cph = l2_loss::forward(cph, yc)
+ lossph = loss_outph + loss_cph
+ X[i,j] = old # reset
+ dX_num = (lossph-lossmh) / (2*h) # numerical derivative
+
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dX[i,j]), dX_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking W.")
- for (i in 1:nrow(W)) {
- for (j in 1:ncol(W)) {
- # Compute numerical derivative
- old = as.scalar(W[i,j])
- W[i,j] = old - h
- [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outmh = l2_loss::forward(outmh, y)
- loss_cmh = l2_loss::forward(cmh, yc)
- lossmh = loss_outmh + loss_cmh
- W[i,j] = old + h
- [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outph = l2_loss::forward(outph, y)
- loss_cph = l2_loss::forward(cph, yc)
- lossph = loss_outph + loss_cph
- W[i,j] = old # reset
- dW_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking W.")
+ for (i in 1:nrow(W)) {
+ for (j in 1:ncol(W)) {
+ # Compute numerical derivative
+ old = as.scalar(W[i,j])
+ W[i,j] = old - h
+ [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outmh = l2_loss::forward(outmh, y)
+ loss_cmh = l2_loss::forward(cmh, yc)
+ lossmh = loss_outmh + loss_cmh
+ W[i,j] = old + h
+ [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outph = l2_loss::forward(outph, y)
+ loss_cph = l2_loss::forward(cph, yc)
+ lossph = loss_outph + loss_cph
+ W[i,j] = old # reset
+ dW_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dW[i,j]), dW_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dW[i,j]), dW_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking b.")
- for (i in 1:nrow(b)) {
- for (j in 1:ncol(b)) {
- # Compute numerical derivative
- old = as.scalar(b[i,j])
- b[i,j] = old - h
- [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outmh = l2_loss::forward(outmh, y)
- loss_cmh = l2_loss::forward(cmh, yc)
- lossmh = loss_outmh + loss_cmh
- b[i,j] = old + h
- [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outph = l2_loss::forward(outph, y)
- loss_cph = l2_loss::forward(cph, yc)
- lossph = loss_outph + loss_cph
- b[i,j] = old # reset
- db_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking b.")
+ for (i in 1:nrow(b)) {
+ for (j in 1:ncol(b)) {
+ # Compute numerical derivative
+ old = as.scalar(b[i,j])
+ b[i,j] = old - h
+ [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outmh = l2_loss::forward(outmh, y)
+ loss_cmh = l2_loss::forward(cmh, yc)
+ lossmh = loss_outmh + loss_cmh
+ b[i,j] = old + h
+ [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outph = l2_loss::forward(outph, y)
+ loss_cph = l2_loss::forward(cph, yc)
+ lossph = loss_outph + loss_cph
+ b[i,j] = old # reset
+ db_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(db[i,j]), db_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(db[i,j]), db_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking out0.")
- for (i in 1:nrow(out0)) {
- for (j in 1:ncol(out0)) {
- # Compute numerical derivative
- old = as.scalar(out0[i,j])
- out0[i,j] = old - h
- [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outmh = l2_loss::forward(outmh, y)
- loss_cmh = l2_loss::forward(cmh, yc)
- lossmh = loss_outmh + loss_cmh
- out0[i,j] = old + h
- [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outph = l2_loss::forward(outph, y)
- loss_cph = l2_loss::forward(cph, yc)
- lossph = loss_outph + loss_cph
- out0[i,j] = old # reset
- dout0_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking out0.")
+ for (i in 1:nrow(out0)) {
+ for (j in 1:ncol(out0)) {
+ # Compute numerical derivative
+ old = as.scalar(out0[i,j])
+ out0[i,j] = old - h
+ [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outmh = l2_loss::forward(outmh, y)
+ loss_cmh = l2_loss::forward(cmh, yc)
+ lossmh = loss_outmh + loss_cmh
+ out0[i,j] = old + h
+ [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outph = l2_loss::forward(outph, y)
+ loss_cph = l2_loss::forward(cph, yc)
+ lossph = loss_outph + loss_cph
+ out0[i,j] = old # reset
+ dout0_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dout0[i,j]), dout0_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dout0[i,j]), dout0_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking c0.")
- for (i in 1:nrow(c0)) {
- for (j in 1:ncol(c0)) {
- # Compute numerical derivative
- old = as.scalar(c0[i,j])
- c0[i,j] = old - h
- [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outmh = l2_loss::forward(outmh, y)
- loss_cmh = l2_loss::forward(cmh, yc)
- lossmh = loss_outmh + loss_cmh
- c0[i,j] = old + h
- [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
- loss_outph = l2_loss::forward(outph, y)
- loss_cph = l2_loss::forward(cph, yc)
- lossph = loss_outph + loss_cph
- c0[i,j] = old # reset
- dc0_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking c0.")
+ for (i in 1:nrow(c0)) {
+ for (j in 1:ncol(c0)) {
+ # Compute numerical derivative
+ old = as.scalar(c0[i,j])
+ c0[i,j] = old - h
+ [outmh, cmh, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outmh = l2_loss::forward(outmh, y)
+ loss_cmh = l2_loss::forward(cmh, yc)
+ lossmh = loss_outmh + loss_cmh
+ c0[i,j] = old + h
+ [outph, cph, cache, cache, cache] = lstm::forward(X, W, b, T, D, return_seq, out0, c0)
+ loss_outph = l2_loss::forward(outph, y)
+ loss_cph = l2_loss::forward(cph, yc)
+ lossph = loss_outph + loss_cph
+ c0[i,j] = old # reset
+ dc0_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dc0[i,j]), dc0_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dc0[i,j]), dc0_num, lossph, lossmh)
+ }
}
}
}
@@ -1554,95 +1566,107 @@ rnn = function() {
# Generate data
N = 3 # num examples
- D = 10 # num features
- T = 15 # num timesteps (sequence length)
- M = 5 # num neurons
- return_seq = TRUE
+ D = 5 # num features
+ T = 15 # num timesteps (sequence length)
+ M = 10 # num neurons
X = rand(rows=N, cols=T*D)
- y = rand(rows=N, cols=T*M)
out0 = rand(rows=N, cols=M)
[W, b, dummy] = rnn::init(N, D, M)
- # Compute analytical gradients of loss wrt parameters
- [out, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- dout = l2_loss::backward(out, y)
- [dX, dW, db, dout0] = rnn::backward(dout, X, W, b, T, D, return_seq, out0, cache_out)
+ # test with (1) outputs from all timesteps, and (2) output from the final timestep
+ for (i in 1:2) {
+ if (i == 1) {
+ return_seq = TRUE
+ y = rand(rows=N, cols=T*M)
+ }
+ else {
+ return_seq = FALSE
+ y = rand(rows=N, cols=M)
+ }
- # Grad check
- h = 1e-5
- print(" - Grad checking X.")
- for (i in 1:nrow(X)) {
- for (j in 1:ncol(X)) {
- # Compute numerical derivative
- old = as.scalar(X[i,j])
- X[i,j] = old - h
- [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossmh = l2_loss::forward(outmh, y)
- X[i,j] = old + h
- [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossph = l2_loss::forward(outph, y)
- X[i,j] = old # reset
- dX_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking with return_seq = " + return_seq)
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dX[i,j]), dX_num, lossph, lossmh)
+ # Compute analytical gradients of loss wrt parameters
+ [out, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ dout = l2_loss::backward(out, y)
+ [dX, dW, db, dout0] = rnn::backward(dout, X, W, b, T, D, return_seq, out0, cache_out)
+
+ # Grad check
+ h = 1e-5
+ print(" - Grad checking X.")
+ for (i in 1:nrow(X)) {
+ for (j in 1:ncol(X)) {
+ # Compute numerical derivative
+ old = as.scalar(X[i,j])
+ X[i,j] = old - h
+ [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossmh = l2_loss::forward(outmh, y)
+ X[i,j] = old + h
+ [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossph = l2_loss::forward(outph, y)
+ X[i,j] = old # reset
+ dX_num = (lossph-lossmh) / (2*h) # numerical derivative
+
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dX[i,j]), dX_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking W.")
- for (i in 1:nrow(W)) {
- for (j in 1:ncol(W)) {
- # Compute numerical derivative
- old = as.scalar(W[i,j])
- W[i,j] = old - h
- [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossmh = l2_loss::forward(outmh, y)
- W[i,j] = old + h
- [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossph = l2_loss::forward(outph, y)
- W[i,j] = old # reset
- dW_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking W.")
+ for (i in 1:nrow(W)) {
+ for (j in 1:ncol(W)) {
+ # Compute numerical derivative
+ old = as.scalar(W[i,j])
+ W[i,j] = old - h
+ [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossmh = l2_loss::forward(outmh, y)
+ W[i,j] = old + h
+ [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossph = l2_loss::forward(outph, y)
+ W[i,j] = old # reset
+ dW_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dW[i,j]), dW_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dW[i,j]), dW_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking b.")
- for (i in 1:nrow(b)) {
- for (j in 1:ncol(b)) {
- # Compute numerical derivative
- old = as.scalar(b[i,j])
- b[i,j] = old - h
- [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossmh = l2_loss::forward(outmh, y)
- b[i,j] = old + h
- [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossph = l2_loss::forward(outph, y)
- b[i,j] = old # reset
- db_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking b.")
+ for (i in 1:nrow(b)) {
+ for (j in 1:ncol(b)) {
+ # Compute numerical derivative
+ old = as.scalar(b[i,j])
+ b[i,j] = old - h
+ [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossmh = l2_loss::forward(outmh, y)
+ b[i,j] = old + h
+ [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossph = l2_loss::forward(outph, y)
+ b[i,j] = old # reset
+ db_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(db[i,j]), db_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(db[i,j]), db_num, lossph, lossmh)
+ }
}
- }
- print(" - Grad checking out0.")
- for (i in 1:nrow(out0)) {
- for (j in 1:ncol(out0)) {
- # Compute numerical derivative
- old = as.scalar(out0[i,j])
- out0[i,j] = old - h
- [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossmh = l2_loss::forward(outmh, y)
- out0[i,j] = old + h
- [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
- lossph = l2_loss::forward(outph, y)
- out0[i,j] = old # reset
- dout0_num = (lossph-lossmh) / (2*h) # numerical derivative
+ print(" - Grad checking out0.")
+ for (i in 1:nrow(out0)) {
+ for (j in 1:ncol(out0)) {
+ # Compute numerical derivative
+ old = as.scalar(out0[i,j])
+ out0[i,j] = old - h
+ [outmh, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossmh = l2_loss::forward(outmh, y)
+ out0[i,j] = old + h
+ [outph, cache_out] = rnn::forward(X, W, b, T, D, return_seq, out0)
+ lossph = l2_loss::forward(outph, y)
+ out0[i,j] = old # reset
+ dout0_num = (lossph-lossmh) / (2*h) # numerical derivative
- # Check error
- rel_error = test_util::check_rel_grad_error(as.scalar(dout0[i,j]), dout0_num, lossph, lossmh)
+ # Check error
+ rel_error = test_util::check_rel_grad_error(as.scalar(dout0[i,j]), dout0_num, lossph, lossmh)
+ }
}
}
}