hw = np.zeros_like(w)
hb = np.zeros_like(b)
files = glob.glob(os.path.join(FLAGS.folder, '*.mat'))
files.sort()
count = 0
for i in range(mpi.RANK, len(files), mpi.SIZE):
file = files[i]
print '%d / %d: %s' % (i, len(files), file)
fid = h5py.File(file, 'r')
features = np.array(fid['features'])
count += features.shape[0]
pred = np.dot(features, w)
pred += b
prob = mathutil.softmax(pred)
dpdp = prob * (1 - prob)
# the gradient for b is simple
hb += np.sum(dpdp, 0)
features **= 2
hw += mathutil.dot(features.T, dpdp)
fid.close()
del features
count = mpi.COMM.allreduce(count)
# we no longer need w and b, so we use them as mpi buffer
mpi.COMM.Allreduce(hw, w)
mpi.COMM.Allreduce(hb, b)
if mpi.is_root():
hw /= count
hb /= count
# add regularization term
if mpi.is_root():
confmats = np.zeros_like(confmat_locals)
else:
confmats = None
for i in range(mpi.RANK, len(files), mpi.SIZE):
# for the i-th file the gt label is i
file = files[i]
predfile = preds[i]
fid = h5py.File(file, 'r')
features = np.array(fid['features'])
fid.close()
fid = h5py.File(predfile, 'r')
pred = np.array(fid['pred'])
fid.close()
# compute new prediction
diff = mathutil.softmax(pred)
diff[:,i] -= 1
features **= 2
weighted_direction = mathutil.dot(features, Hwinv) + Hbinv
dpred = diff * weighted_direction
if use_validation:
iter = 0
accu = sum((val_pred == i) & (val_label == i)) / float(sum(val_label == i))
s_low = 0
s_high = scales[0]
while iter < 10:
s = (s_low + s_high) / 2
newprob = pred + dpred * s
mathutil.softmax(newprob, out=newprob)
newpred = newprob.argmax(1)
my_accu = sum(newpred==i) / float(pred.shape[0])
if mpi.is_root():
confmats = np.zeros_like(confmat_locals)
else:
confmats = None
for i in range(mpi.RANK, len(files), mpi.SIZE):
# for the i-th file the gt label is i
file = files[i]
predfile = preds[i]
fid = h5py.File(file, 'r')
features = np.array(fid['features'])
fid.close()
fid = h5py.File(predfile, 'r')
pred = np.array(fid['pred'])
fid.close()
# compute new prediction
diff = mathutil.softmax(pred)
diff[:, i] -= 1
features **= 2
weighted_direction = mathutil.dot(features, Hwinv) + Hbinv
dpred = diff * weighted_direction
if use_validation:
iter = 0
accu = sum((val_pred == i) & (val_label == i)) / float(
sum(val_label == i))
s_low = 0
s_high = scales[0]
while iter < 10:
s = (s_low + s_high) / 2
newprob = pred + dpred * s
mathutil.softmax(newprob, out=newprob)
newpred = newprob.argmax(1)