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chore: bump deps (#60)
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21 files changed

+193
-287
lines changed

21 files changed

+193
-287
lines changed

‎Cargo.lock

+23-6
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‎Cargo.toml

+9
Original file line numberDiff line numberDiff line change
@@ -2,3 +2,12 @@
22
package.version = "0.4.0"
33
members = ["crates/*"]
44
resolver = "2"
5+
6+
[workspace.dependencies]
7+
cudarc = "0.9.14"
8+
ndarray = "0.16.1"
9+
ndarray-rand = "0.15.0"
10+
safetensors = "0.4.5"
11+
serde = { version = "1.0", features = ["derive"] }
12+
serde_json = "1.0"
13+
thiserror = "1.0.49"

‎bench/netsaur_cpu.ts

+29-67
Original file line numberDiff line numberDiff line change
@@ -11,72 +11,34 @@ import {
1111

1212
await setupBackend(CPU);
1313

14-
Deno.bench(
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{ name: "xor 10000 epochs", permissions: "inherit" },
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async () => {
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const net = new Sequential({
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size: [4, 2],
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silent: true,
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layers: [
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DenseLayer({ size: [3] }),
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SigmoidLayer(),
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DenseLayer({ size: [1] }),
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SigmoidLayer(),
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],
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cost: Cost.MSE,
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});
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net.train(
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[
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{
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inputs: tensor2D([
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[0, 0],
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[1, 0],
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[0, 1],
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[1, 1],
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]),
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outputs: tensor2D([[0], [1], [1], [0]]),
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},
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],
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10000,
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);
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console.log((await net.predict(tensor1D([0, 0]))).data);
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console.log((await net.predict(tensor1D([1, 0]))).data);
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console.log((await net.predict(tensor1D([0, 1]))).data);
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console.log((await net.predict(tensor1D([1, 1]))).data);
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},
14+
const net = new Sequential({
15+
size: [4, 2],
16+
silent: true,
17+
layers: [
18+
DenseLayer({ size: [3] }),
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SigmoidLayer(),
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DenseLayer({ size: [1] }),
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SigmoidLayer(),
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],
23+
cost: Cost.MSE,
24+
});
25+
26+
net.train(
27+
[
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{
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inputs: tensor2D([
30+
[0, 0],
31+
[1, 0],
32+
[0, 1],
33+
[1, 1],
34+
]),
35+
outputs: tensor2D([[0], [1], [1], [0]]),
36+
},
37+
],
38+
10000,
4939
);
5040

51-
// const net = new NeuralNetwork({
52-
// size: [4, 2],
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// silent: true,
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// layers: [
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// DenseLayer({ size: [3], activation: Activation.Sigmoid }),
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// DenseLayer({ size: [1], activation: Activation.Sigmoid }),
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// ],
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// cost: Cost.MSE,
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// });
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// const time = performance.now();
62-
63-
// net.train(
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// [
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// {
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// inputs: tensor2D([
67-
// [0, 0],
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// [1, 0],
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// [0, 1],
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// [1, 1],
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// ]),
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// outputs: tensor2D([[0], [1], [1], [0]]),
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// },
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// ],
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// 10000,
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// )
77-
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// console.log(`training time: ${performance.now() - time}ms`);
79-
// console.log((await net.predict(tensor2D([[0, 0]]))).data);
80-
// console.log((await net.predict(tensor2D([[1, 0]]))).data);
81-
// console.log((await net.predict(tensor2D([[0, 1]]))).data);
82-
// console.log((await net.predict(tensor2D([[1, 1]]))).data);
41+
console.log((await net.predict(tensor1D([0, 0]))).data);
42+
console.log((await net.predict(tensor1D([1, 0]))).data);
43+
console.log((await net.predict(tensor1D([0, 1]))).data);
44+
console.log((await net.predict(tensor1D([1, 1]))).data);

‎bench/netsaur_wasm.ts

+29-67
Original file line numberDiff line numberDiff line change
@@ -11,72 +11,34 @@ import {
1111

1212
await setupBackend(WASM);
1313

14-
Deno.bench(
15-
{ name: "xor 10000 epochs", permissions: "inherit" },
16-
async () => {
17-
const net = new Sequential({
18-
size: [4, 2],
19-
silent: true,
20-
layers: [
21-
DenseLayer({ size: [3] }),
22-
SigmoidLayer(),
23-
DenseLayer({ size: [1] }),
24-
SigmoidLayer(),
25-
],
26-
cost: Cost.MSE,
27-
});
28-
29-
net.train(
30-
[
31-
{
32-
inputs: tensor2D([
33-
[0, 0],
34-
[1, 0],
35-
[0, 1],
36-
[1, 1],
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]),
38-
outputs: tensor2D([[0], [1], [1], [0]]),
39-
},
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],
41-
10000,
42-
);
43-
44-
console.log((await net.predict(tensor1D([0, 0]))).data);
45-
console.log((await net.predict(tensor1D([1, 0]))).data);
46-
console.log((await net.predict(tensor1D([0, 1]))).data);
47-
console.log((await net.predict(tensor1D([1, 1]))).data);
48-
},
14+
const net = new Sequential({
15+
size: [4, 2],
16+
silent: true,
17+
layers: [
18+
DenseLayer({ size: [3] }),
19+
SigmoidLayer(),
20+
DenseLayer({ size: [1] }),
21+
SigmoidLayer(),
22+
],
23+
cost: Cost.MSE,
24+
});
25+
26+
net.train(
27+
[
28+
{
29+
inputs: tensor2D([
30+
[0, 0],
31+
[1, 0],
32+
[0, 1],
33+
[1, 1],
34+
]),
35+
outputs: tensor2D([[0], [1], [1], [0]]),
36+
},
37+
],
38+
10000,
4939
);
5040

51-
// const net = new NeuralNetwork({
52-
// size: [4, 2],
53-
// silent: true,
54-
// layers: [
55-
// DenseLayer({ size: [3], activation: Activation.Sigmoid }),
56-
// DenseLayer({ size: [1], activation: Activation.Sigmoid }),
57-
// ],
58-
// cost: Cost.MSE,
59-
// });
60-
61-
// const time = performance.now();
62-
63-
// net.train(
64-
// [
65-
// {
66-
// inputs: tensor2D([
67-
// [0, 0],
68-
// [1, 0],
69-
// [0, 1],
70-
// [1, 1],
71-
// ]),
72-
// outputs: tensor2D([[0], [1], [1], [0]]),
73-
// },
74-
// ],
75-
// 10000,
76-
// )
77-
78-
// console.log(`training time: ${performance.now() - time}ms`);
79-
// console.log((await net.predict(tensor2D([[0, 0]]))).data);
80-
// console.log((await net.predict(tensor2D([[1, 0]]))).data);
81-
// console.log((await net.predict(tensor2D([[0, 1]]))).data);
82-
// console.log((await net.predict(tensor2D([[1, 1]]))).data);
41+
console.log((await net.predict(tensor1D([0, 0]))).data);
42+
console.log((await net.predict(tensor1D([1, 0]))).data);
43+
console.log((await net.predict(tensor1D([0, 1]))).data);
44+
console.log((await net.predict(tensor1D([1, 1]))).data);

‎bench/node/brain_cpu.js

-27
This file was deleted.

‎bench/node/brain_gpu.js

-26
This file was deleted.

‎bench/node/package.json

-18
This file was deleted.

‎bench/node/tfjs_cpu.js

-23
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‎crates/core-gpu/Cargo.toml

+7-8
Original file line numberDiff line numberDiff line change
@@ -6,12 +6,11 @@ version = { workspace = true }
66
[lib]
77
crate-type = ["cdylib"]
88

9-
109
[dependencies]
11-
ndarray = "0.15.6"
12-
ndarray-rand = "0.14.0"
13-
serde = {version = "1.0", features = ["derive"]}
14-
serde_json = "1.0"
15-
safetensors = "0.4.0"
16-
cudarc = "0.9.14"
17-
thiserror = "1.0.49"
10+
cudarc = { workspace = true }
11+
ndarray = { workspace = true }
12+
ndarray-rand = { workspace = true }
13+
safetensors = { workspace = true }
14+
serde = { workspace = true }
15+
serde_json = { workspace = true }
16+
thiserror = { workspace = true }

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