TestsTested | ✓ |

LangLanguage | SwiftSwift |

License | MIT |

ReleasedLast Release | Nov 2016 |

SwiftSwift Version | 3.0 |

SPMSupports SPM | ✓ |

Maintained by Yuta Koshizawa, Takehiro Araki.

- By
- Yuta Koshizawa and Takehiro Araki

*TensorSwift* is a lightweight library to calculate tensors, which has similar APIs to *TensorFlow*’s. *TensorSwift* is useful to simulate calculating tensors in Swift **using models trained by TensorFlow**.

```
let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6])
let b = Tensor(shape: [2, 3], elements: [7, 8, 9, 10, 11, 12])
let sum = a + b // Tensor(shape: [2, 3], elements: [8, 10, 12, 14, 16, 18])
let mul = a * b // Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72])
let c = Tensor(shape: [3, 1], elements: [7, 8, 9])
let matmul = a.matmul(c) // Tensor(shape: [2, 1], elements: [50, 122])
let zeros = Tensor(shape: [2, 3, 4])
let ones = Tensor(shape: [2, 3, 4], element: 1)
```

The following code shows how to simulate Deep MNIST for Experts, a tutorial of *TensorFlow*, by *TensorSwift*.

```
public struct Classifier {
public let W_conv1: Tensor
public let b_conv1: Tensor
public let W_conv2: Tensor
public let b_conv2: Tensor
public let W_fc1: Tensor
public let b_fc1: Tensor
public let W_fc2: Tensor
public let b_fc2: Tensor
public func classify(x_image: Tensor) -> Int {
let h_conv1 = (x_image.conv2d(filter: W_conv1, strides: [1, 1, 1]) + b_conv1).relu()
let h_pool1 = h_conv1.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1])
let h_conv2 = (h_pool1.conv2d(filter: W_conv2, strides: [1, 1, 1]) + b_conv2).relu()
let h_pool2 = h_conv2.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1])
let h_pool2_flat = h_pool2.reshaped([1, 7 * 7 * 64])
let h_fc1 = (h_pool2_flat.matmul(W_fc1) + b_fc1).relu()
let y_conv = (h_fc1.matmul(W_fc2) + b_fc2).softmax()
return y_conv.elements.enumerated().max { $0.1 < $1.1 }!.0
}
}
```

`.Package(url: "[email protected]:qoncept/TensorSwift.git", majorVersion: 0, minor: 2),`