如何在TensorFlow中为矢量化参数设置bijectors?

问题描述 投票:1回答:1

我遵循GaussianProcessRegressionModel上本教程第3个例子的逻辑。但是,我的设置中的一个差异是我的幅度和length_scale是向量。但是,我很难为矢量化参数设置bijectors。

我尝试了官方示例教程中的一种方法(click here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)

更改标量参数的Softplus形状。但控制台仍然显示相同的错误消息。

给定所有数据和参数,我的compute_joint_log_prob_3只输出标量对数后验概率。我已经测试过该功能效果很好。唯一的问题是在存在矢量化内核超参数的情况下设置unconstrained_bijectors

# 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))

它应该工作,模型将绘制此参数的样本。相反,我得到了一堆错误信息

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>.

我不知道那些输入形状到底意味着什么。感谢您的时间和解释。

-------我是人工分离线------

在与Brian讨论后,我知道我错在哪里。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。

正如布莱恩昨天所说,Softplus()能够根据它所提供的张量自动播放。如果我想改变它的柔软度,那么我可以修改hinge_softness=...

在读完tutorial on tensorflow distribution shape之后,我也获得了更深刻的理解。

再次感谢您的澄清......在我知道自己错在哪里之后,这是多么美好的一天......

python tensorflow tensorflow-probability
1个回答
0
投票

如果你只想要相同的softplus,铰链柔软度为1,那么bijector会播放,你可以写:

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)。

我认为你看到的实际异常可能是由于bijector参数形状与你的潜状态形状有某种冲突。转换后的转换内核需要在bijector指定的事件调整上减少log_prob。使用从event_ndims返回的log_prob的等级作为目标批次等级来导出每个潜在的target_log_prob_fn,即,尾部事件维度将由缩放器减少。

你能说一下你想做什么吗?看起来你正试图在一堆GP内核hparams上运行一个MCMC链。提供很多帮助很难,而不是看到compute_joint_log_prob_3的内部。

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