blob: 5571b07f0f7f355015e4ae6c710cbcf2595f77b1 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
Draw n samples along a new outer dimension.
This ValueType draws `n` samples from StochasticTensors run within its
context, increasing the rank by one along a new outer dimension.
Example:
```python
mu = tf.zeros((2,3))
sigma = tf.ones((2, 3))
with sg.value_type(sg.SampleValue(n=4)):
dt = sg.DistributionTensor(
distributions.Normal, mu=mu, sigma=sigma)
# draws 4 samples each with shape (2, 3) and concatenates
assertEqual(dt.value().get_shape(), (4, 2, 3))
```
- - -
#### `tf.contrib.bayesflow.stochastic_graph.SampleValue.__init__(n=1, stop_gradient=False)` {#SampleValue.__init__}
Sample `n` times and concatenate along a new outer dimension.
##### Args:
* <b>`n`</b>: A python integer or int32 tensor. The number of samples to take.
* <b>`stop_gradient`</b>: If `True`, StochasticTensors' values are wrapped in
`stop_gradient`, to avoid backpropagation through.
- - -
#### `tf.contrib.bayesflow.stochastic_graph.SampleValue.n` {#SampleValue.n}
- - -
#### `tf.contrib.bayesflow.stochastic_graph.SampleValue.popped_above(unused_value_type)` {#SampleValue.popped_above}
- - -
#### `tf.contrib.bayesflow.stochastic_graph.SampleValue.pushed_above(unused_value_type)` {#SampleValue.pushed_above}
- - -
#### `tf.contrib.bayesflow.stochastic_graph.SampleValue.stop_gradient` {#SampleValue.stop_gradient}
|
