Tutorial request: simple metropolis sampling about probability HOT 9 CLOSED

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TypeError                                 Traceback (most recent call last)
<ipython-input-29-faad2d5672e2> in <module>()
      8         new_state_fn=tfp.mcmc.random_walk_uniform_fn(scale=2.),seed=42),
      9     num_burnin_steps=500,
---> 10     parallel_iterations=1
     11 )  # For determinism.
     12 

~/anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/mcmc/sample.py in sample_chain(num_results, current_state, previous_kernel_results, kernel, num_burnin_steps, num_steps_between_results, parallel_iterations, name)
    247                          dtype=tf.int32),  # num_steps
    248         initializer=[current_state, previous_kernel_results],
--> 249         parallel_iterations=parallel_iterations)

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/functional_ops.py in scan(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, infer_shape, name)
    618         parallel_iterations=parallel_iterations,
    619         back_prop=back_prop, swap_memory=swap_memory,
--> 620         maximum_iterations=n)
    621 
    622     results_flat = [r.stack() for r in r_a]

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations)
   3206     if loop_context.outer_context is None:
   3207       ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
-> 3208     result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
   3209     if maximum_iterations is not None:
   3210       return result[1]

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
   2944       with ops.get_default_graph()._lock:  # pylint: disable=protected-access
   2945         original_body_result, exit_vars = self._BuildLoop(
-> 2946             pred, body, original_loop_vars, loop_vars, shape_invariants)
   2947     finally:
   2948       self.Exit()

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
   2881         flat_sequence=vars_for_body_with_tensor_arrays)
   2882     pre_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION)  # pylint: disable=protected-access
-> 2883     body_result = body(*packed_vars_for_body)
   2884     post_summaries = ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION)  # pylint: disable=protected-access
   2885     if not nest.is_sequence(body_result):

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in <lambda>(i, lv)
   3182         cond = lambda i, lv: (  # pylint: disable=g-long-lambda
   3183             math_ops.logical_and(i < maximum_iterations, orig_cond(*lv)))
-> 3184         body = lambda i, lv: (i + 1, orig_body(*lv))
   3185 
   3186     if context.executing_eagerly():

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/functional_ops.py in compute(i, a_flat, tas)
    607       packed_elems = input_pack([elem_ta.read(i) for elem_ta in elems_ta])
    608       packed_a = output_pack(a_flat)
--> 609       a_out = fn(packed_a, packed_elems)
    610       nest.assert_same_structure(
    611           elems if initializer is None else initializer, a_out)

~/anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/mcmc/sample.py in _scan_body(args_list, num_steps)
    235               previous_kernel_results,
    236           ],
--> 237           parallel_iterations=parallel_iterations)[1:]  # Lop off `it_`.
    238 
    239     if previous_kernel_results is None:

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in while_loop(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations)
   3206     if loop_context.outer_context is None:
   3207       ops.add_to_collection(ops.GraphKeys.WHILE_CONTEXT, loop_context)
-> 3208     result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants)
   3209     if maximum_iterations is not None:
   3210       return result[1]

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildLoop(self, pred, body, loop_vars, shape_invariants)
   2944       with ops.get_default_graph()._lock:  # pylint: disable=protected-access
   2945         original_body_result, exit_vars = self._BuildLoop(
-> 2946             pred, body, original_loop_vars, loop_vars, shape_invariants)
   2947     finally:
   2948       self.Exit()

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in _BuildLoop(self, pred, body, original_loop_vars, loop_vars, shape_invariants)
   2903     # during this comparison, because inputs are typically lists and
   2904     # outputs of the body are typically tuples.
-> 2905     nest.assert_same_structure(list(packed_vars_for_body), list(body_result))
   2906 
   2907     # Store body_result to keep track of TensorArrays returned by body

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/nest.py in assert_same_structure(nest1, nest2, check_types)
    181       their substructures. Only possible if `check_types` is `True`.
    182   """
--> 183   _pywrap_tensorflow.AssertSameStructure(nest1, nest2, check_types)
    184 
    185 

TypeError: The two structures don't have the same nested structure.

First structure: type=list str=[<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity:0' shape=() dtype=int32>, <tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_1:0' shape=() dtype=float32>, MetropolisHastingsKernelResults(accepted_results=UncalibratedRandomWalkResults(log_acceptance_correction=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_2:0' shape=() dtype=float32>, target_log_prob=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_3:0' shape=() dtype=float32>), is_accepted=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_4:0' shape=() dtype=bool>, log_accept_ratio=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_5:0' shape=() dtype=float32>, proposed_state=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_6:0' shape=() dtype=float32>, proposed_results=UncalibratedRandomWalkResults(log_acceptance_correction=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_7:0' shape=() dtype=float32>, target_log_prob=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_8:0' shape=() dtype=float32>))]

Second structure: type=list str=[<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/add:0' shape=() dtype=int32>, <tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/choose_next_state/Select:0' shape=() dtype=float32>, MetropolisHastingsKernelResults(accepted_results=[<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/choose_inner_results/Select:0' shape=() dtype=float32>, <tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/choose_inner_results/Select_1:0' shape=() dtype=float32>], is_accepted=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/Less:0' shape=() dtype=bool>, log_accept_ratio=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/compute_log_accept_ratio/Sum:0' shape=() dtype=float32>, proposed_state=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/rwm_one_step/random_walk_uniform_fn/random_uniform:0' shape=() dtype=float32>, proposed_results=UncalibratedRandomWalkResults(log_acceptance_correction=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/rwm_one_step/zeros:0' shape=() dtype=float32>, target_log_prob=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/rwm_one_step/Normal/log_prob/sub:0' shape=() dtype=float32>))]

More specifically: The two namedtuples don't have the same sequence type. First structure type=UncalibratedRandomWalkResults str=UncalibratedRandomWalkResults(log_acceptance_correction=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_2:0' shape=() dtype=float32>, target_log_prob=<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/Identity_3:0' shape=() dtype=float32>) has type UncalibratedRandomWalkResults, while second structure type=list str=[<tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/choose_inner_results/Select:0' shape=() dtype=float32>, <tf.Tensor 'mcmc_sample_chain_11/scan/while/while/mh_one_step/choose_inner_results/Select_1:0' shape=() dtype=float32>] has type list