https://github.com/SciScala/NDScala

N-dimensional / multi-dimensional arrays (tensors) in Scala 3. Think NumPy ndarray / PyTorch Tensor but type-safe over shapes, array/axis labels & numeric data types
https://github.com/SciScala/NDScala

array-programming dotty machine-learning matrix multi-dimensional-array n-dimensional-array named-tensor ndarray neural-networks numerical-computing numpy scala scala-3 scala3 shape-safety tensor

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N-dimensional / multi-dimensional arrays (tensors) in Scala 3. Think NumPy ndarray / PyTorch Tensor but type-safe over shapes, array/axis labels & numeric data types

• Host: GitHub
• URL: https://github.com/SciScala/NDScala
• Owner: SciScala
• License: agpl-3.0
• Created: 2020-06-14T13:57:45.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-12-22T18:02:46.000Z (almost 2 years ago)
• Last Synced: 2024-08-04T00:06:29.292Z (4 months ago)
• Topics: array-programming, dotty, machine-learning, matrix, multi-dimensional-array, n-dimensional-array, named-tensor, ndarray, neural-networks, numerical-computing, numpy, scala, scala-3, scala3, shape-safety, tensor
• Language: Scala
• Homepage:
• Size: 182 KB
• Stars: 48
• Watchers: 7
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-scala - NDScala - dimensional arrays in Scala 3. Think NumPy ndarray, but type-safe over shapes, array/axis labels & numeric data types |   (Table of Contents / Science and Data Analysis)

README

        
Training a (shape-safe) neural network in 10 lines:

In NDScala:

```scala

//After some setup

//Declaring types and their corresponding values

type Mat10kX10k = 10000 #: 10000 #:SNil

type AxisLabels = "AxisLabel" ##: "AxisLabel" ##: TSNil

val mat10kX10k = shapeOf[Mat10kX10k]

val axisLabels = tensorShapeDenotationOf[AxisLabels]

val ones = Tensor(Array.fill(100000000)(1.0f),"TensorLabel",axisLabels, mat10kX10k)

def train(x: Tensor[Float, ("TensorLabel", AxisLabels, Mat10kX10k)],

          y: Tensor[Float, ("TensorLabel", AxisLabels, Mat10kX10k)],

          w0: Tensor[Float, ("TensorLabel", AxisLabels, Mat10kX10k)],

          w1: Tensor[Float, ("TensorLabel", AxisLabels, Mat10kX10k)],

          iter: Int): Tuple2[Tensor[Float, ("TensorLabel", AxisLabels, Mat10kX10k)],

                             Tensor[Float, ("TensorLabel", AxisLabels, Mat10kX10k)]] =

    if iter == 0 then (w0, w1)

    else

        val l1 =  (x.matmul(w0)).sigmoid()

        val l2 = (l1.matmul(w1)).sigmoid()

        val error = y - l2

        val l2Delta = (error) * (l2 * (ones - l2))

        val l1Delta =  (l2Delta.matmul(w1.transpose))

        val w1New = w1 + (((l1.transpose).matmul(l2Delta)))

        val w0New = w0 + (((x.transpose).matmul(l1Delta)))

        train(x,y,w0New,w1New,iter-1)

```

And for reference, in NumPy, in 10 lines:

```python

def train(X,Y,iter): 

    syn0 = 2*np.random.random((10000,10000)).astype('float32') - 1

    syn1 = 2*np.random.random((10000,1000)).astype('float32') - 1

    for j in range(iter): 

        l1 = 1/(1+np.exp(-(np.dot(X,syn0))))  

        l2 = 1/(1+np.exp(-(np.dot(l1,syn1)))) 

        error = y - l2

        l2_delta = (error)*(l2*(1-l2))

        l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))

        syn1 += l1.T.dot(l2_delta)

        syn0 += X.T.dot(l1_delta) 

```

The run time of the NDScala version is ~80% of that of NumPy w/MKL

The PyTorch equivalent is slightly faster, at ~85% of the NDScala version run time.

This can be accounted for by the copy overhead of passing data between the JVM and native memory.