log_softmax

paddle.nn.functional. log_softmax ( x, axis=- 1, dtype=None, name=None ) [source]

This operator implements the log_softmax layer. The calculation process is as follows:

\[\begin{split}\begin{aligned} log\_softmax[i, j] &= log(softmax(x)) \\ &= log(\frac{\exp(X[i, j])}{\sum_j(\exp(X[i, j])}) \end{aligned}\end{split}\]
Parameters

  • x (Tensor) – The input Tensor with data type float32, float64.

  • axis (int, optional) – The axis along which to perform log_softmax calculations. It should be in range [-D, D), where D is the dimensions of x . If axis < 0, it works the same way as \(axis + D\) . Default is -1.

  • dtype (str|np.dtype|core.VarDesc.VarType, optional) – The desired data type of the output tensor. If dtype is specified, x is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Supported dtype: float32, float64. If dtype is None, the output Tensor has the same dtype as x. Default is None.

  • name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

A Tensor with the same shape and data type (use dtype if it is specified) as x.

Examples

import paddle
import paddle.nn.functional as F

x = [[[-2.0, 3.0, -4.0, 5.0],
      [3.0, -4.0, 5.0, -6.0],
      [-7.0, -8.0, 8.0, 9.0]],
     [[1.0, -2.0, -3.0, 4.0],
      [-5.0, 6.0, 7.0, -8.0],
      [6.0, 7.0, 8.0, 9.0]]]
x = paddle.to_tensor(x)
out1 = F.log_softmax(x)
out2 = F.log_softmax(x, dtype='float64')
# out1's data type is float32; out2's data type is float64
# out1 and out2's value is as follows:
# [[[ -7.1278396   -2.1278396   -9.127839    -0.12783948]
#   [ -2.1270514   -9.127051    -0.12705144 -11.127051  ]
#   [-16.313261   -17.313261    -1.3132617   -0.31326184]]
#  [[ -3.0518122   -6.051812    -7.051812    -0.051812  ]
#   [-12.313267    -1.3132664   -0.3132665  -15.313267  ]
#   [ -3.4401896   -2.4401896   -1.4401896   -0.44018966]]]