v0.10.0

What's Changed

Breaking Changes

• Binary ops (add, sub, div, mul, maximum, minimum) take ownership of rhs by @coreylowman in #268
• backwards only allows 0d tensors now by @coreylowman in #206
• Clone now keeps same id, removing Tensor::duplicate by @coreylowman in #249
• Multi axis reductions
  • See docs
  • #189, #190, #194
  • Reduction functions now can reduce across any axis/axes: mean, sum, max, min, stddev, var, softmax, log_softmax, and logsumexp
  • Remove -1 from valid axes, add trait HasLastAxis to use in generic functions instead
  • Adding normalize function that normalizes across any axis
  • Removing single axis reduction functions fn *_axis(): mean_axis, sum_axis, max_axis, min_axis, normalize_axis, std_axis, var_axis
  • Rename HasAxis to HasAxes
  • Add trait BroadcastTo
    • Remove trait Broadcast1, trait Broadcast2, trait Broadcast3, trait Broadcast4
  • Add trait Reduce/trait ReduceTo
    • Remove trait Reduce1
• Batched select & select consistency
  • See docs
  • Renaming SelectTo, using SelectTo for batched select by @coreylowman in #217
  • Add Batched Select for devices and tensor_ops by @coreylowman in #182
• Reduce things in prelude by @coreylowman in #209
• Renaming FlattenImage to Flatten2D by @coreylowman in #243

New features

• Arc in Tensors instead of Rc by @caelunshun in #236
• powi() and powf() functions by @coreylowman in #167
• no_std support
  • See feature flags docs
  • Remove num-traits, no default features on depends by @coreylowman in #200
  • Adding intel-mkl feature and removing the 4 mkl-- features by @coreylowman in #239
  • Adding module that has docs for feature flags by @coreylowman in #240
  • Adding "numpy" feature to make numpy & npz optional by @coreylowman in #241
  • Adding #![no_std] support via no_std_compat by @coreylowman in #244
  • Adding default-features = false to dependencies by @coreylowman in #257
• Adding Axis permutations via trait PermuteTo.
  • See docs
  • #208, #169, #193
• Adding trait ModuleMut
  • See docs
  • #225
  • Removing Module super traits by @coreylowman in #223
  • Rework Dropout/DropoutOneIn to use ModuleMut by @coreylowman in #226
• Adding decoupled/l2 weight decay in optimizers:
  • See docs
  • add HasArrayData to GradientProvider by @cBournhonesque in #261
  • Add weight decay to SGD by @cBournhonesque in #258
  • Adding weight_decay to Adam by @coreylowman in #275
  • Adding weight decay to RMSprop by @coreylowman in #276
• Adding nn::Transformer #175, #173, #180
  • See docs
• Adding nn::MinPool2D, nn::MaxPool2D, nn::AvgPool2D by @coreylowman in #214
  • See docs
• Adding nn::MinPoolGlobal, nn::MaxPoolGlobal, nn::AvgPoolGlobal by @coreylowman in #216
  • See docs
• Adding nn::BatchNorm2D by @coreylowman in #228
  • See docs

Misc changes

• Add tensor() function as a convenient way to make tensors from arrays by @coreylowman in #161
  • See docs
• Remove allocation in dropout implementation by @coreylowman in #164
• Removing Tensor::OwnedTape by @coreylowman in #197
• Revamping examples/ by @coreylowman in #205
• Conv cleanup
  • Moving conv into device and cleaning up a bit by @coreylowman in #212
  • Minifying conv impls by @coreylowman in #213
  • Changing conv2d and conv2d_batched to methods of tensors by @coreylowman in #221
  • Replacing conv2d implementation with matmuls by @coreylowman in #237
• Fix typos by @cBournhonesque in #235
• Combining multiple where clauses with const generics into a single one by @coreylowman in #264
• Checking for null ptr in AllocateZeros by @coreylowman in #271
• Reducing allocations in map_df_uses_fx by @coreylowman in #272
• Adding with_empty_tape and with_diff_tape by @coreylowman in #274

New Contributors

• @cBournhonesque made their first contribution in #235
• @caelunshun made their first contribution in #236

Full Changelog: v0.9.0...v0.10.0