LogSoftmax - 1 vs 11#

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

Files changed (1) hide show
  1. LogSoftmax1 → LogSoftmax11 +6 -7
LogSoftmax1 → LogSoftmax11 RENAMED
@@ -1 +1 @@
1
1
  The operator computes the logsoftmax (log of softmax) values for each layer in the batch
2
+ of the given input. The input is a 2-D tensor (Tensor<float>) of size
3
+ (batch_size x input_feature_dimensions). The output tensor has the same shape
2
- of the given input.
4
+ and contains the logsoftmax values of the corresponding input.
3
- The input does not need to explicitly be a 2D vector; rather, it will be
5
+ Input does not need to explicitly be a 2D vector; rather, it will be
4
6
  coerced into one. For an arbitrary n-dimensional tensor
5
7
  input in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is
6
8
  the axis provided, then input will be coerced into a 2-dimensional tensor with
7
9
  dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default
8
10
  case where axis=1, this means the input tensor will be coerced into a 2D tensor
9
11
  of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size.
10
12
  In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D.
11
13
  Each of these dimensions must be matched correctly, or else the operator
14
+ will throw errors.
12
- will throw errors. The output tensor has the same shape
13
- and contains the logsoftmax values of the corresponding input.
14
15
  **Attributes**
15
16
  * **axis**:
16
17
  Describes the axis of the inputs when coerced to 2D; defaults to one
17
- because the 0th axis most likely describes the batch_size. Negative
18
+ because the 0th axis most likely describes the batch_size
18
- value means counting dimensions from the back. Accepted range is
19
- [-r, r-1] where r = rank(input).
20
19
  **Inputs**
21
20
  * **input** (heterogeneous) - **T**:
22
21
  The input tensor that's coerced into a 2D matrix of size (NxD) as
23
22
  described above.
24
23
  **Outputs**
25
24
  * **output** (heterogeneous) - **T**:
26
25
  The output values with the same shape as input tensor (the original
27
26
  size without coercion).
28
27
  **Type Constraints**
29
28
  * **T** in (
30
29
  tensor(double),
31
30
  tensor(float),
32
31
  tensor(float16)
33
32
  ):
34
33
  Constrain input and output types to float tensors.