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| Original file line number | Diff line number | Diff line change |
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| __pycache__ | ||
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| neural_network/.venv | ||
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| Scarb.lock | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,33 +1,63 @@ | ||
| # tensor.max | ||
|
|
||
| ```rust | ||
| fn max(self: @Tensor<T>) -> T; | ||
| fn max(tensors: Span<Tensor<T>>) -> Tensor<T>; | ||
| ``` | ||
|
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| Returns the maximum value in the tensor. | ||
| Returns the element-wise maximum values from a list of input tensors | ||
| The input tensors must have either: | ||
| * Exactly the same shape | ||
| * The same number of dimensions and the length of each dimension is either a common length or 1. | ||
|
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| ## Args | ||
|
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| * `self`(`@Tensor<T>`) - The input tensor. | ||
| * `tensors`(` Span<Tensor<T>>,`) - Array of the input tensors | ||
|
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| ## Returns | ||
| ## Returns | ||
|
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| The maximum `T` value in the tensor. | ||
| A new `Tensor<T>` containing the element-wise maximum values | ||
|
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| Examples | ||
| ## Panics | ||
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| * Panics if tensor array is empty | ||
| * Panics if the shapes are not equal or broadcastable | ||
|
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| ## Examples | ||
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| Case 1: Process tensors with same shape | ||
|
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| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
|
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| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
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| fn max_example() -> u32 { | ||
| let tensor = TensorTrait::new( | ||
| shape: array![2, 2, 2].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7].span(), | ||
| ); | ||
| fn max_example() -> Tensor<u32> { | ||
| let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),); | ||
| let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 3, 1, 2].span(),); | ||
| let result = TensorTrait::max(tensors: array![tensor1, tensor2].span()); | ||
| return result; | ||
| } | ||
| >>> [0, 3, 2, 3] | ||
|
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| result.shape | ||
| >>> (2, 2) | ||
| ``` | ||
|
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| Case 2: Process tensors with different shapes | ||
|
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| // We can call `max` function as follows. | ||
| return tensor.max(); | ||
| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
|
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| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
|
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| fn max_example() -> Tensor<u32> { | ||
| let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),); | ||
| let tensor2 = TensorTrait::new(shape: array![1, 2].span(), data: array![1, 4].span(),); | ||
| let result = TensorTrait::max(tensors: array![tensor1, tensor2].span()); | ||
| return result; | ||
| } | ||
| >>> 7 | ||
| >>> [1, 4, 2, 4] | ||
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| result.shape | ||
| >>> (2, 2) | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,33 @@ | ||
| # tensor.max_in_tensor | ||
|
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| ```rust | ||
| fn max_in_tensor(self: @Tensor<T>) -> T; | ||
| ``` | ||
|
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| Returns the maximum value in the tensor. | ||
|
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| ## Args | ||
|
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| * `self`(`@Tensor<T>`) - The input tensor. | ||
|
|
||
| ## Returns | ||
|
|
||
| The maximum `T` value in the tensor. | ||
|
|
||
| Examples | ||
|
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||
| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
|
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| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
|
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| fn max_in_tensor_example() -> u32 { | ||
| let tensor = TensorTrait::new( | ||
| shape: array![2, 2, 2].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7].span(), | ||
| ); | ||
|
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| // We can call `max_in_tensor` function as follows. | ||
| return tensor.max_in_tensor(); | ||
| } | ||
| >>> 7 | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,34 +1,63 @@ | ||
| # tensor.min | ||
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| ```rust | ||
| fn min(self: @Tensor<T>) -> T; | ||
| fn min(tensors: Span<Tensor<T>>) -> Tensor<T>; | ||
| ``` | ||
|
|
||
| Returns the minimum value in the tensor. | ||
| Returns the element-wise minumum values from a list of input tensors | ||
| The input tensors must have either: | ||
| * Exactly the same shape | ||
| * The same number of dimensions and the length of each dimension is either a common length or 1. | ||
|
|
||
| ## Args | ||
|
|
||
| * `self`(`@Tensor<T>`) - The input tensor. | ||
| * `tensors`(` Span<Tensor<T>>,`) - Array of the input tensors | ||
|
|
||
| ## Returns | ||
| ## Returns | ||
|
|
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| The minimum `T` value in the tensor. | ||
| A new `Tensor<T>` containing the element-wise minimum values | ||
|
|
||
| ## Panics | ||
|
|
||
| * Panics if tensor array is empty | ||
| * Panics if the shapes are not equal or broadcastable | ||
|
|
||
| ## Examples | ||
|
|
||
| Case 1: Process tensors with same shape | ||
|
|
||
| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
|
|
||
| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
|
|
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| fn min_example() -> u32 { | ||
| let tensor = TensorTrait::new( | ||
| shape: array![2, 2, 2].span(), | ||
| data: array![0, 1, 2, 3, 4, 5, 6, 7].span(), | ||
| ); | ||
| fn min_example() -> Tensor<u32> { | ||
| let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),); | ||
| let tensor2 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 3, 1, 2].span(),); | ||
| let result = TensorTrait::min(tensors: array![tensor1, tensor2].span()); | ||
| return result; | ||
| } | ||
| >>> [0, 1, 1, 2] | ||
|
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| result.shape | ||
| >>> (2, 2) | ||
| ``` | ||
|
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| Case 2: Process tensors with different shapes | ||
|
|
||
| // We can call `min` function as follows. | ||
| return tensor.min(); | ||
| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
|
|
||
| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
|
|
||
| fn min_example() -> Tensor<u32> { | ||
| let tensor1 = TensorTrait::new(shape: array![2, 2].span(), data: array![0, 1, 2, 3].span(),); | ||
| let tensor2 = TensorTrait::new(shape: array![1, 2].span(), data: array![1, 4].span(),); | ||
| let result = TensorTrait::min(tensors: array![tensor1, tensor2].span()); | ||
| return result; | ||
| } | ||
| >>> 0 | ||
| >>> [0, 1, 1, 4] | ||
|
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| result.shape | ||
| >>> (2, 2) | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| # tensor.min_in_tensor | ||
|
|
||
| ```rust | ||
| fn min_in_tensor(self: @Tensor<T>) -> T; | ||
| ``` | ||
|
|
||
| Returns the minimum value in the tensor. | ||
|
|
||
| ## Args | ||
|
|
||
| * `self`(`@Tensor<T>`) - The input tensor. | ||
|
|
||
| ## Returns | ||
|
|
||
| The minimum `T` value in the tensor. | ||
|
|
||
| ## Examples | ||
|
|
||
| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
|
|
||
| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
|
|
||
| fn min_in_tensor_example() -> u32 { | ||
| let tensor = TensorTrait::new( | ||
| shape: array![2, 2, 2].span(), | ||
| data: array![0, 1, 2, 3, 4, 5, 6, 7].span(), | ||
| ); | ||
|
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| // We can call `min_in_tensor` function as follows. | ||
| return tensor.min_in_tensor(); | ||
| } | ||
| >>> 0 | ||
| ``` |
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