netket.models.ARNNConv2D¶
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class
netket.models.
ARNNConv2D
(hilbert, layers, features, kernel_size, kernel_dilation=(1, 1), activation=<function selu>, use_bias=True, dtype=<class 'jax._src.numpy.lax_numpy.float64'>, precision=None, kernel_init=<function variance_scaling.<locals>.init>, bias_init=<function zeros>, machine_pow=2, parent=<flax.linen.module._Sentinel object>, name=None)[source]¶ Bases:
netket.models.autoreg.AbstractARNN
Autoregressive neural network with 2D convolution layers.
- Attributes
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kernel_dilation
: Tuple[int, int] = (1, 1)¶ a sequence of 2 integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel (default: 1).
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precision
: Any = None¶ numerical precision of the computation, see jax.lax.Precision for details.
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- Methods
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activation
()¶ 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.
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bias_init
(shape, dtype=<class 'jax._src.numpy.lax_numpy.float64'>)¶
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conditionals
(inputs)[source]¶ Computes the conditional probabilities for each site to take each value.
- Parameters
inputs (
Union
[ndarray
,DeviceArray
,Tracer
]) – configurations with dimensions (batch, Hilbert.size).- Return type
Union
[ndarray
,DeviceArray
,Tracer
]- Returns
The probabilities with dimensions (batch, Hilbert.size, Hilbert.local_size).
Examples
>>> import pytest; pytest.skip("skip automated test of this docstring") >>> >>> p = model.apply(variables, σ, method=model.conditionals) >>> print(p[2, 3, :]) [0.3 0.7] # For the 3rd spin of the 2nd sample in the batch, # it takes probability 0.3 to be spin down (local state index 0), # and probability 0.7 to be spin up (local state index 1).
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kernel_init
(shape, dtype=<class 'jax._src.numpy.lax_numpy.float64'>)¶
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