如何为张量流中的矢量化参数设置双喷射器？

Question

我遵循本教程中关于GaussianProcessRegressionModel的第3个示例的逻辑。然而，我的设置中的一个不同之处在于，我的振幅和长度刻度是向量。但是，我很难为矢量化参数设置双喷射器。在

我尝试了官方示例教程（click here）中的一种方法，并搜索关键字“Batching Bijectors”）。在

他们用过

softplus = tfp.bijectors.Softplus(
  hinge_softness=[1., .5, .1])
print("Hinge softness shape:", softplus.hinge_softness.shape)

更改标量参数的Softplus的形状。但是控制台一直显示相同的错误消息。在

Mycompute_joint_log_prob_3只输出给定所有数据和参数的标量对数后验概率。我已经测试了这个功能是否有效。唯一的问题是在存在向量化内核超参数的情况下设置unconstrained_bijectors。在

^{2}$

它应该可以工作，模型将提取这些参数的样本。相反，我收到一堆错误消息说

Traceback (most recent call last):
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1659, in _create_c_op
    c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Requires start <= limit when delta > 0: 1/0 for 'mcmc_sample_chain/transformed_kernel_bootstrap_results/mh_bootstrap_results/hmc_kernel_bootstrap_results/maybe_call_fn_and_grads/value_and_gradients/softplus_10/forward_log_det_jacobian/range' (op: 'Range') with input shapes: [], [], [] and with computed input tensors: input[0] = <1>, input[1] = <0>, input[2] = <1>.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/runpy.py", line 183, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/runpy.py", line 109, in _get_module_details
    __import__(pkg_name)
  File "/MMAR_q/MMAR_q.py", line 237, in <module>
    bijector=unconstrained_bijectors))
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/sample.py", line 235, in sample_chain
    previous_kernel_results = kernel.bootstrap_results(current_state)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/transformed_kernel.py", line 344, in bootstrap_results
    transformed_init_state))
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/hmc.py", line 518, in bootstrap_results
    kernel_results = self._impl.bootstrap_results(init_state)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/metropolis_hastings.py", line 264, in bootstrap_results
    pkr = self.inner_kernel.bootstrap_results(init_state)
  File "/MAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/hmc.py", line 687, in bootstrap_results
    ] = mcmc_util.maybe_call_fn_and_grads(self.target_log_prob_fn, init_state)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/util.py", line 237, in maybe_call_fn_and_grads
    result, grads = _value_and_gradients(fn, fn_arg_list, result, grads)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/util.py", line 185, in _value_and_gradients
    result = fn(*fn_arg_list)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/transformed_kernel.py", line 204, in new_target_log_prob
    event_ndims=event_ndims)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/transformed_kernel.py", line 51, in fn
    for b, e, sp in zip(bijector, event_ndims, transformed_state_parts)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/mcmc/transformed_kernel.py", line 51, in <listcomp>
    for b, e, sp in zip(bijector, event_ndims, transformed_state_parts)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/bijectors/bijector.py", line 1205, in forward_log_det_jacobian
    return self._call_forward_log_det_jacobian(x, event_ndims, name)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/bijectors/bijector.py", line 1177, in _call_forward_log_det_jacobian
    kwargs=kwargs)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/bijectors/bijector.py", line 982, in _compute_inverse_log_det_jacobian_with_caching
    event_ndims)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/bijectors/bijector.py", line 1272, in _reduce_jacobian_det_over_event
    axis=self._get_event_reduce_dims(min_event_ndims, event_ndims))
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow_probability/python/bijectors/bijector.py", line 1284, in _get_event_reduce_dims
    return tf.range(-reduce_ndims, 0)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py", line 1199, in range
    return gen_math_ops._range(start, limit, delta, name=name)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 6746, in _range
    "Range", start=start, limit=limit, delta=delta, name=name)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1823, in __init__
    control_input_ops)
  File "/MMAR_q/venv/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1662, in _create_c_op
    raise ValueError(str(e))
ValueError: Requires start <= limit when delta > 0: 1/0 for 'mcmc_sample_chain/transformed_kernel_bootstrap_results/mh_bootstrap_results/hmc_kernel_bootstrap_results/maybe_call_fn_and_grads/value_and_gradients/softplus_10/forward_log_det_jacobian/range' (op: 'Range') with input shapes: [], [], [] and with computed input tensors: input[0] = <1>, input[1] = <0>, input[2] = <1>.

我不知道这些输入形状到底是什么意思。谢谢你的时间和解释。在

-----我是人工分隔线-----

和布赖恩商量之后，我知道我错在哪里了。错误消息可能意味着compute_joint_log_prob_3的结果不是标量而是其他形状。在

正如Brian昨天所说，Softplus()能够根据它所受的张量自动广播。如果我想改变它的柔软度，那么我可以修改hinge_softness=...。在

在我读了tutorial on tensorflow distribution shape之后，我也获得了更深的理解。在

再次感谢您的澄清。。。当我知道我错在哪里之后，这是多么美好的一天。。。在

Answer 1

如果您只想要相同的softplus，铰链柔度为1，双喷射器将广播，您只需写下：

vectorized_sp = tfb.Softplus(hinge_softness=np.float32(1)) 另请注意，默认值为1，因此更简单： vectorized_sp = tfb.Softplus()

另外，我建议查看SimpleStepSizeAdaptation内核（目前可能只在pip install tfp-nightly中）。在

我想你看到的实际异常可能是由于双喷射器参数形状与你的潜在状态形状发生了某种冲突。转换后的转换内核需要减少bijector指定的事件dim上的log-prob。每个潜在事件的event_ndims是使用从target_log_prob_fn返回的日志概率的秩作为目标批处理秩派生的，也就是说，后面的事件维数将被bijector减少。在

你能再说一点你想做什么吗？看起来你正试图在一堆GP内核hparam上运行一个MCMC链。很难提供很多帮助，不了解compute_joint_log_prob_3的内部结构。在