def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict(total_count=parameter_properties.ParameterProperties( default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED), concentration1=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util. eps(dtype)))), concentration0=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util. eps(dtype)))))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( distribution=parameter_properties.BatchedComponentProperties(), shift=parameter_properties.ParameterProperties(), scale=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))), tailweight=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( loc=parameter_properties.ParameterProperties(), scale=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))), low=parameter_properties.ParameterProperties(), # TODO(b/169874884): Support decoupled parameterization. high=parameter_properties.ParameterProperties( default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED, ))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( temperature=parameter_properties.ParameterProperties( shape_fn=lambda sample_shape: sample_shape[:-1], default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))), logits=parameter_properties.ParameterProperties(event_ndims=1), probs=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=softmax_centered_bijector .SoftmaxCentered, is_preferred=False))
def _parameter_properties(cls, dtype, num_classes=None): return dict( loc=parameter_properties.ParameterProperties(event_ndims=1), atol=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED, is_preferred=False), rtol=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED, is_preferred=False))
def _parameter_properties(cls, dtype): from tensorflow_probability.python.bijectors import softplus # pylint:disable=g-import-not-at-top return dict( base_kernel=parameter_properties.BatchedComponentProperties(), fixed_inputs=parameter_properties.ParameterProperties( event_ndims=lambda self: self.base_kernel.feature_ndims + 1), fixed_inputs_mask=parameter_properties.ParameterProperties( event_ndims=1), diag_shift=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))), _precomputed_divisor_matrix_cholesky=( parameter_properties.ParameterProperties(event_ndims=2)))
def _parameter_properties(cls, dtype): from tensorflow_probability.python.bijectors import softplus # pylint:disable=g-import-not-at-top return dict( bias_variance=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))), exponent=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))), slope_variance=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))), shift=parameter_properties.ParameterProperties())
def _parameter_properties(cls, dtype, num_classes=None): return dict( rate1=parameter_properties. ParameterProperties(default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype))), is_preferred=False), rate2=parameter_properties. ParameterProperties(default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype))), is_preferred=False), log_rate1=parameter_properties.ParameterProperties(), log_rate2=parameter_properties.ParameterProperties(), )
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( loc=parameter_properties.ParameterProperties(event_ndims=1), scale_diag=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))), scale_identity_multiplier=parameter_properties. ParameterProperties(default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype))), is_preferred=False))
def _parameter_properties(cls, dtype, num_classes=None): return dict(concentration=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)) )), scale=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util. eps(dtype)))), upper_bound=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util. eps(dtype)))))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( logits=parameter_properties.ParameterProperties( event_ndims=1, shape_fn=lambda sample_shape: ps.concat( [sample_shape, [num_classes]], axis=0)), probs=parameter_properties.ParameterProperties( event_ndims=1, shape_fn=lambda sample_shape: ps.concat( [sample_shape, [num_classes]], axis=0), default_constraining_bijector_fn=softmax_centered_bijector. SoftmaxCentered, is_preferred=False))
def _parameter_properties(cls, dtype, num_classes=None): from tensorflow_probability.python.bijectors import ascending # pylint:disable=g-import-not-at-top from tensorflow_probability.python.bijectors import softplus # pylint:disable=g-import-not-at-top return dict( kernels=parameter_properties.BatchedComponentProperties( event_ndims=lambda self: [0 for _ in self.kernels]), locs=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=lambda: ascending.Ascending( )), # pylint:disable=unnecessary-lambda slopes=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))))
def _parameter_properties(cls, dtype): from tensorflow_probability.python.bijectors import softplus # pylint:disable=g-import-not-at-top return dict( amplitude=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))), length_scale=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))), inverse_length_scale=parameter_properties.ParameterProperties( default_constraining_bijector_fn=softplus.Softplus), scale_mixture_rate=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( loc=parameter_properties.ParameterProperties(event_ndims=1), covariance_matrix=parameter_properties.ParameterProperties( event_ndims=2, shape_fn=lambda sample_shape: ps.concat( [sample_shape, sample_shape[-1:]], axis=0), default_constraining_bijector_fn=( lambda: chain_bijector.Chain([ cholesky_outer_product_bijector.CholeskyOuterProduct(), fill_scale_tril_bijector.FillScaleTriL( diag_shift=dtype_util.eps(dtype)) ]))))
def _parameter_properties(cls, dtype, num_classes=None): return dict( df=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus( # pylint: disable=g-long-lambda low=dtype_util.as_numpy_dtype(dtype)(2.)))), index_points=parameter_properties.ParameterProperties( event_ndims=lambda self: self.kernel.feature_ndims + 1, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED), kernel=parameter_properties.BatchedComponentProperties(), observation_noise_variance=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype))), shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( concentration=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))), mixing_concentration=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))), mixing_rate=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype )))))
def _parameter_properties(cls, dtype, num_classes=None): return dict( loc=parameter_properties.ParameterProperties(event_ndims=1), precision_factor=parameter_properties.BatchedComponentProperties(), precision=parameter_properties.BatchedComponentProperties(), nonzeros=parameter_properties.BatchedComponentProperties( event_ndims=1))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict(df=parameter_properties.ParameterProperties( shape_fn=lambda sample_shape: sample_shape[:-2], default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED), scale=parameter_properties.BatchedComponentProperties())
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict(concentration=parameter_properties.ParameterProperties( shape_fn=lambda sample_shape: sample_shape[:-2], default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=tf.convert_to_tensor( 1. + dtype_util.eps(dtype), dtype=dtype)))))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( scores=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))))
def _parameter_properties(cls, dtype, num_classes=None): # pylint: disable=g-long-lambda return dict( power=parameter_properties.ParameterProperties( default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus( low=tf.convert_to_tensor( 1. + dtype_util.eps(dtype), dtype=dtype)))))
def _parameter_properties(cls, dtype): from tensorflow_probability.python.bijectors import softplus # pylint:disable=g-import-not-at-top return dict( kernel=parameter_properties.BatchedComponentProperties(), scale_diag=parameter_properties.ParameterProperties( event_ndims=lambda self: self.kernel.feature_ndims, default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))))
def _parameter_properties(cls, dtype, num_classes=None): return dict( loc=parameter_properties.ParameterProperties( event_ndims=1), scale_diag=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))), scale_perturb_factor=parameter_properties.ParameterProperties( event_ndims=2, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED, is_preferred=False), scale_perturb_diag=parameter_properties.ParameterProperties( event_ndims=1, is_preferred=False, default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))))
def _parameter_properties(cls, dtype): return { 'scale_diag': parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=( lambda: softplus.Softplus(low=dtype_util.eps(dtype)))) }
def _parameter_properties(cls, dtype): # pylint: disable=g-long-lambda return dict(scale_tril=parameter_properties.ParameterProperties( event_ndims=2, shape_fn=lambda sample_shape: ps.concat( [sample_shape, sample_shape[-1:]], axis=0), default_constraining_bijector_fn=fill_triangular_bijector. FillTriangular))
def _parameter_properties(cls, dtype, num_classes=None): from tensorflow_probability.python.bijectors import softplus as softplus_bijector # pylint:disable=g-import-not-at-top return dict( amplitudes=parameter_properties.ParameterProperties( event_ndims=1, default_constraining_bijector_fn=( softplus_bijector.Softplus(low=dtype_util.eps(dtype)))), kernel=parameter_properties.BatchedComponentProperties(event_ndims=1))
def _parameter_properties(cls, dtype, num_classes=None): return dict( total_count=parameter_properties.ParameterProperties( default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED, # The method `_sample_bates` currently constructs intermediate # samples with a shape that depends on `total_count`, so, although # `total_count` is not *inherently* a shape parameter, we annotate # it as one in the current implementation (making it the rare case # of a shape parameter that also has batch semantics). This could # be removed if a different sampling method (eg, rejection sampling) # were used. specifies_shape=True), low=parameter_properties.ParameterProperties(), # TODO(b/169874884): Support decoupled parameterization. high=parameter_properties.ParameterProperties( default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED))
def _parameter_properties(cls, dtype): return dict( bin_widths=parameter_properties.ParameterProperties( event_ndims=1, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED, default_constraining_bijector_fn=parameter_properties .BIJECTOR_NOT_IMPLEMENTED), bin_heights=parameter_properties.ParameterProperties( event_ndims=1, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED, default_constraining_bijector_fn=parameter_properties .BIJECTOR_NOT_IMPLEMENTED), knot_slopes=parameter_properties.ParameterProperties( event_ndims=1, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED, default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util.eps(dtype)))), range_min=parameter_properties.ParameterProperties( shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED,))
def _parameter_properties(cls, dtype): return dict(rightmost_transposed_ndims=parameter_properties. ParameterProperties( shape_fn=lambda sample_shape: [], default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED, is_preferred=False), perm=parameter_properties.ParameterProperties( event_ndims=1, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED, default_constraining_bijector_fn=parameter_properties. BIJECTOR_NOT_IMPLEMENTED))
def _parameter_properties(cls, dtype, num_classes=None): return dict(index_points=parameter_properties.ParameterProperties( event_ndims=lambda self: self.kernel.feature_ndims + 1, shape_fn=parameter_properties.SHAPE_FN_NOT_IMPLEMENTED, ), kernel=parameter_properties.BatchedComponentProperties(), observation_noise_variance=parameter_properties. ParameterProperties( event_ndims=0, shape_fn=lambda sample_shape: sample_shape[:-1], default_constraining_bijector_fn=( lambda: softplus_bijector.Softplus(low=dtype_util. eps(dtype)))))