diff --git a/neural_tangents/_src/stax/branching.py b/neural_tangents/_src/stax/branching.py index cc8e1cd..f944378 100644 --- a/neural_tangents/_src/stax/branching.py +++ b/neural_tangents/_src/stax/branching.py @@ -226,7 +226,7 @@ def kernel_fn(ks: Kernels, **kwargs) -> Kernel: raise NotImplementedError( '`FanInConcat` layer along the non-channel axis is only implemented' 'for the case if all input layers guaranteed to be mean-zero ' - 'Gaussian, i.e. having all `is_gaussian set to `True`.') + 'Gaussian, i.e. having all `is_gaussian` set to `True`.') else: # TODO(romann): allow nonlinearity after channelwise concatenation. # TODO(romann): support concatenating different channelwise masks. diff --git a/neural_tangents/_src/stax/elementwise.py b/neural_tangents/_src/stax/elementwise.py index 9f14941..37949bb 100644 --- a/neural_tangents/_src/stax/elementwise.py +++ b/neural_tangents/_src/stax/elementwise.py @@ -912,10 +912,12 @@ def nngp_fn_diag(nngp: np.ndarray) -> np.ndarray: @layer @supports_masking(remask_kernel=False) def Polynomial(coef: Sequence[float]) -> InternalLayer: - """Polynomials, i.e. `coef[0] + coef[1] * x + ... + coef[n] * x**n`. + """Polynomials, i.e. `coef[0] + coef[1] * x + … + coef[n] * x**n`. Args: - coef: a sequence of coefficients. Follows `numpy.polynomial.Polynomial` API. + coef: + a sequence of coefficients. Follows + :class:`numpy.polynomial.polynomial.Polynomial` API. Returns: `(init_fn, apply_fn, kernel_fn)`. diff --git a/neural_tangents/_src/stax/linear.py b/neural_tangents/_src/stax/linear.py index 1aca8e5..5d918f6 100644 --- a/neural_tangents/_src/stax/linear.py +++ b/neural_tangents/_src/stax/linear.py @@ -3162,7 +3162,7 @@ def _conv_kernel_full_spatial_unshared( Used when `kernel.diagonal_spatial == False`. Has the same outputs on the spatial diagonal as `_conv_kernel_full_spatial_shared`, but `0` in all off-spatial-diagonal entries. The diagonal entries are computed via calling - ``_conv_kernel_diagonal_spatial`. + `_conv_kernel_diagonal_spatial`. Args: lhs: