我正在尝试在https://www.tensorflow.org/api_docs/python/tf/contrib/distributions/bijectors/MaskedAutoregressiveFlow运行MaskedAutoregressiveFlow的示例。它是文档的简单副本，但我收到以下错误。我已经尝试了event_shape=[dims, 1]，但这似乎没有帮助（不同的错误）。我不知道该怎么做。
有没有人见过这个？
import tensorflow as tf
import tensorflow.contrib.distributions as tfd
from tensorflow.contrib.distributions import bijectors as tfb
dims = 5
# A common choice for a normalizing flow is to use a Gaussian for the base
# distribution. (However, any continuous distribution would work.) E.g.,
maf = tfd.TransformedDistribution(
distribution=tfd.Normal(loc=0., scale=1.),
bijector=tfb.MaskedAutoregressiveFlow(
shift_and_log_scale_fn=tfb.masked_autoregressive_default_template(
hidden_layers=[512, 512])),
event_shape=[dims])
x = maf.sample() # Expensive; uses `tf.while_loop`, no Bijector caching.
maf.log_prob(x) # Almost free; uses Bijector caching.
maf.log_prob(0.) # Cheap; no `tf.while_loop` despite no Bijector caching.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-3b2fcb2af309> in <module>()
11
12
---> 13 x = maf.sample() # Expensive; uses `tf.while_loop`, no Bijector caching.
14 maf.log_prob(x) # Almost free; uses Bijector caching.
15 maf.log_prob(0.) # Cheap; no `tf.while_loop` despite no Bijector caching.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/distributions/distribution.py in sample(self, sample_shape, seed, name)
687 samples: a `Tensor` with prepended dimensions `sample_shape`.
688 """
--> 689 return self._call_sample_n(sample_shape, seed, name)
690
691 def _log_prob(self, value):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/distributions/transformed_distribution.py in _call_sample_n(self, sample_shape, seed, name, **kwargs)
411 # work, it is imperative that this is the last modification to the
412 # returned result.
--> 413 y = self.bijector.forward(x, **kwargs)
414 y = self._set_sample_static_shape(y, sample_shape)
415
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/distributions/bijector_impl.py in forward(self, x, name)
618 NotImplementedError: if `_forward` is not implemented.
619 """
--> 620 return self._call_forward(x, name)
621
622 def _inverse(self, y):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/distributions/bijector_impl.py in _call_forward(self, x, name, **kwargs)
599 if mapping.y is not None:
600 return mapping.y
--> 601 mapping = mapping.merge(y=self._forward(x, **kwargs))
602 self._cache(mapping)
603 return mapping.y
/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py in _forward(self, x)
245 y0 = array_ops.zeros_like(x, name="y0")
246 # call the template once to ensure creation
--> 247 _ = self._shift_and_log_scale_fn(y0)
248 def _loop_body(index, y0):
249 """While-loop body for autoregression calculation."""
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/template.py in __call__(self, *args, **kwargs)
358 custom_getter=self._custom_getter) as vs:
359 self._variable_scope = vs
--> 360 result = self._call_func(args, kwargs)
361 return result
362
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/template.py in _call_func(self, args, kwargs)
300 trainable_at_start = len(
301 ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES))
--> 302 result = self._func(*args, **kwargs)
303
304 if self._variables_created:
/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py in _fn(x)
478 activation=activation,
479 *args,
--> 480 **kwargs)
481 x = masked_dense(
482 inputs=x,
/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py in masked_dense(inputs, units, num_blocks, exclusive, kernel_initializer, reuse, name, *args, **kwargs)
386 *args,
387 **kwargs)
--> 388 return layer.apply(inputs)
389
390
/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py in apply(self, inputs, *args, **kwargs)
807 Output tensor(s).
808 """
--> 809 return self.__call__(inputs, *args, **kwargs)
810
811 def _add_inbound_node(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py in __call__(self, inputs, *args, **kwargs)
671
672 # Check input assumptions set before layer building, e.g. input rank.
--> 673 self._assert_input_compatibility(inputs)
674 if input_list and self._dtype is None:
675 try:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/layers/base.py in _assert_input_compatibility(self, inputs)
1195 ', found ndim=' + str(ndim) +
1196 '. Full shape received: ' +
-> 1197 str(x.get_shape().as_list()))
1198 # Check dtype.
1199 if spec.dtype is not None:
ValueError: Input 0 of layer dense_1 is incompatible with the layer: : expected min_ndim=2, found ndim=1. Full shape received: [5]
originally defined at:
File "<ipython-input-2-3b2fcb2af309>", line 9, in <module>
hidden_layers=[512, 512])),
File "/usr/local/lib/python3.6/dist-packages/tensorflow/contrib/distributions/python/ops/bijectors/masked_autoregressive.py", line 499, in masked_autoregressive_default_template
"masked_autoregressive_default_template", _fn)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/template.py", line 152, in make_template
**kwargs)