Megvii Engine Team
4 years ago
1 changed files with 0 additions and 32 deletions
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imperative/python/megengine/functional/nn.py
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| @@ -63,7 +63,6 @@ __all__ = [ | |||||
| "prelu", | "prelu", | ||||
| "softmax", | "softmax", | ||||
| "softplus", | "softplus", | ||||
| "svd", | |||||
| "sync_batch_norm", | "sync_batch_norm", | ||||
| "conv1d", | "conv1d", | ||||
| "sigmoid", | "sigmoid", | ||||
| @@ -1222,37 +1221,6 @@ def dot(inp1: Tensor, inp2: Tensor) -> Tensor: | |||||
| return result | return result | ||||
| def svd(inp: Tensor, full_matrices=False, compute_uv=True) -> Tensor: | |||||
| """ | |||||
| Computes the singular value decompositions of input matrix. | |||||
| :param inp: input matrix, must has shape `[..., M, N]`. | |||||
| :return: output matrices, `(U, sigma, V)`. | |||||
| Examples: | |||||
| .. testcode:: | |||||
| import numpy as np | |||||
| from megengine import tensor | |||||
| import megengine.functional as F | |||||
| x = tensor(np.arange(0, 6, dtype=np.float32).reshape(2,3)) | |||||
| _, y, _ = F.svd(x) | |||||
| print(y.numpy().round(decimals=3)) | |||||
| Outputs: | |||||
| .. testoutput:: | |||||
| [7.348 1. ] | |||||
| """ | |||||
| op = builtin.SVD(full_matrices=full_matrices, compute_uv=compute_uv) | |||||
| U, sigma, V = apply(op, inp) | |||||
| return U, sigma, V | |||||
| def dropout(inp: Tensor, drop_prob: float, training: bool = True) -> Tensor: | def dropout(inp: Tensor, drop_prob: float, training: bool = True) -> Tensor: | ||||
| """ | """ | ||||
| Returns a new tensor where each of the elements are randomly set to zero | Returns a new tensor where each of the elements are randomly set to zero | ||||