我在GaussianProcessRegressionModel上遵循本教程第三个示例的逻辑。但是，我的设置中的一个区别是我的幅值和长度尺度是向量。但是，我很难为矢量化参数设置双射器。
我尝试了官方示例教程中的一种方法（单击此处并搜索关键字“Batching Bijectors”）。
他们用
softplus = tfp.bijectors.Softplus(
hinge_softness=[1., .5, .1])
print("Hinge softness shape:", softplus.hinge_softness.shape)
更改 Softplus for scalar 参数的形状。但是控制台一直显示相同的错误消息。
我compute_joint_log_prob_3只是在给定所有数据和参数的情况下输出标量对数后验概率。我已经测试过该功能运行良好。唯一的问题是unconstrained_bijectors在存在矢量化内核超参数的情况下的设置。
# Create a list to save all variables to be iterated.
initial_chain_states = [
tf.ones([1, num_GPs], dtype=tf.float32, name="init_amp_1"),
tf.ones([1, num_GPs], dtype=tf.float32, name="init_scale_1"),
tf.ones([1, num_GPs], dtype=tf.float32, name="init_amp_0"),
tf.ones([1, num_GPs], dtype=tf.float32, name="init_scale_0"),
tf.ones([], dtype=tf.float32, name="init_sigma_sq_1"),
tf.ones([], dtype=tf.float32, name="init_sigma_sq_0")
]
vectorized_sp = tfb.Softplus(hinge_softness=np.ones([1, num_GPs], dtype=np.float32))
unconstrained_bijectors = [
vectorized_sp,
vectorized_sp,
vectorized_sp,
vectorized_sp,
tfp.bijectors.Softplus(),
tfp.bijectors.Softplus()
]
def un_normalized_log_posterior(amplitude_1, length_scale_1,
amplitude_0, length_scale_0,
noise_var_1, noise_var_0):
return compute_joint_log_prob_3(
para_index, delayed_signal, y_type,
amplitude_1, length_scale_1, amplitude_0, length_scale_0,
noise_var_1, noise_var_0
)
num_results = 200
[
amps_1,
scales_1,
amps_0,
scales_0,
sigma_sqs_1,
sigma_sqs_0
], kernel_results = tfp.mcmc.sample_chain(
num_results=num_results,
num_burnin_steps=250,
num_steps_between_results=3,
current_state=initial_chain_states,
kernel=tfp.mcmc.TransformedTransitionKernel(
inner_kernel=tfp.mcmc.HamiltonianMonteCarlo(
target_log_prob_fn=un_normalized_log_posterior,
step_size=np.float32(0.1),
num_leapfrog_steps=3,
step_size_update_fn=tfp.mcmc.make_simple_step_size_update_policy(
num_adaptation_steps=100)),
bijector=unconstrained_bijectors))
它应该可以工作，并且模型将抽取此参数的样本。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。感谢您的时间和解释。
--------我是人工分隔线------
在和布赖恩讨论之后，我知道我错在哪里了。错误消息可能意味着结果compute_joint_log_prob_3不是标量而是其他形状。
正如布赖恩昨天所说，Softplus()能够根据它所依赖的张量自动广播。如果我想改变它的柔软度，那么我可以修改hinge_softness=....
并且在阅读了关于张量流分布形状的教程后，我也有了更深入的了解。
再次感谢您的澄清......在我知道我错在哪里之后，这是多么美好的一天......