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