An experimental deep learning library written in pure rust. Breakage expected on each release in the short term. See mnist.rs in examples or Rusty_SR for usage samples.

The key types are Node and Ops which are Rc-like references to components of a shared mutable Graph, which is extended gradually with new tensors and operations via construction functions. Facilities for reverse-mode automatic differentiation are included in operations, extending the graph as necessary. Typical graph construction and differentiation shown below:

// 1. Build a MLP neural net graph - 98% @ 10 epochs
let input = Node::new(&[-1, 28, 28, 1]).set_name("input");
let labels = Node::new(&[-1, 10]).set_name("labels");

let layer1 = elu(affine(&input, 256, msra(1.0))).set_name("layer1");
let layer2 = elu(affine(&layer1, 256, msra(1.0))).set_name("layer2");
let logits = linear(&layer2, 10, msra(1.0)).set_name("logits");

let training_loss = add(
  reduce_sum(softmax_cross_entropy(&logits, &labels, -1), &[], false).set_name("loss"),
  scale(l2(logits.graph().nodes_tagged(NodeTag::Parameter)), 1e-3).set_name("regularisation"),
)
.set_name("training_loss");
let accuracy = equal(argmax(&logits, -1), argmax(&labels, -1)).set_name("accuracy");

let parameters = accuracy.graph().nodes_tagged(NodeTag::Parameter);

let grads = Grad::of(training_loss).wrt(parameters).build()?;

Current work is focused on improving the high level graph construction API, and better supporting dynamic/define-by-run graphs.

Issues are a great place for discussion, problems, requests.

Patchy until the library API experimentation ends, particularly until the graph construction API finalised.

• Computation hypergraph
• NN
  • Dense Connection and Bias operations
  • N-dimensional Convolution
    • Arbitrary padding
    • Strides
    • Reflection padding
  • Categorical Cross Entropy
  • Binary Cross Entropy
• Boolean
  • Equal
  • Greater_Equal
  • Greater_Than
  • Less_Equal
  • Less_Than
  • Not
• Elementwise
  • Abs
  • Ceil
  • Cos
  • Div
  • Elu
  • Exp
  • Floor
  • Identity
  • Leaky_relu
  • Ln
  • Logistic
  • Max
  • Min
  • Mul
  • Negative
  • Offset
  • Reciprocal
  • Relu
  • Robust
  • Round
  • Scale
  • Sign
  • Sin
  • SoftPlus
  • SoftSign
  • Sqr
  • Sqrt
  • Srgb
  • Subtract
  • Tanh
• Grad
  • Stop_grad
• Manip
  • Concat
  • Slice
  • Permute_axes
  • Expand_dims
  • Remove_dims
• Math
  • Argmax
  • Broadcast
• Pooling
  • N-dimensional Avg_Pool
  • Max pool
  • N-dimensional spaxel shuffling for "Sub-pixel Convolution"
  • N-dimensional Linear-Interpolation
  • Global Pooling
• Reduce
  • Reduce_Prod
  • Reduce_Sum
• Regularisation
  • L1
  • L2
  • Hoyer_squared
  • Robust
• Shapes
  • Shape inference and constraint propagation
• Data Loading
  • Mnist
  • Cifar
  • Image Folders
  • Imagenet (ILSVRC)
• SGD
• RMSProp
• ADAM
• Basic numerical tests
• Limit Optimiser evaluation batch size to stay within memory limits
• Selectively disable calculation of forward values, node derivatives and parameter derivatives
• Builder patterns for operation contruction
• Split Graph struct into mutable GraphBuilder and immutable Sub-Graphs
  • Replace 'accidentally quadratic' graph algorithms
  • Replace up-front allocation with Sub-Graph optimised allocation/deallocation patterns based on liveness analysis of nodes
• Overhaul data ingestion, particularly buffering input processing/reads.
• Move tensor format to bluss' ndarray
• Improve naming inter/intra-library consistancy
• Operator overloading for simple ops
• Complete documentation
• Reduce ability to express illegal states in API
• Move from panics to error-chain
• Move from error-chain to thiserror
• Guard unsafe code rigourously
• Comprehensive tests

• Optionally typed tensors
• Arrayfire as an option for sgemm on APUs
• Graph optimisation passes and inplace operations
• Support for both dynamic and static graphs
  • RNNs

MIT