netket.models.FastARNNConv1D#
- class netket.models.FastARNNConv1D[source]#
Bases:
FastARNNSequential
Fast autoregressive neural network with 1D convolution layers.
See
netket.models.FastARNNSequential
for a brief explanation of fast autoregressive sampling.- Attributes
-
-
precision:
Any
= None# numerical precision of the computation, see
jax.lax.Precision
for details.
-
features:
Union
[tuple
[int
,...
],int
]# output feature density in each layer. If a single number is given, all layers except the last one will have the same number of features.
-
kernel_init:
Callable
[[Any
,Sequence
[int
],Any
],Union
[ndarray
,Array
]]# initializer for the weights.
- hilbert: HomogeneousHilbert#
the Hilbert space. Only homogeneous unconstrained Hilbert spaces are supported.
-
precision:
- Methods
- __call__(inputs)#
Computes the log wave-functions for input configurations.
- activation()#
selu applied separately to the real andimaginary parts of it’s input.
The docstring to the original function follows.
Scaled exponential linear unit activation.
Computes the element-wise function:
\[\begin{split}\mathrm{selu}(x) = \lambda \begin{cases} x, & x > 0\\ \alpha e^x - \alpha, & x \le 0 \end{cases}\end{split}\]where \(\lambda = 1.0507009873554804934193349852946\) and \(\alpha = 1.6732632423543772848170429916717\).
For more information, see Self-Normalizing Neural Networks.
- Args:
x : input array
- Returns:
An array.
- See also:
elu()
- Return type: