Error running the Tutorial notebook - possibly a version issue?

[yaniyuval]

Hi, When trying to run the tutorial code, I get an error in the line:

results = pde.core.integrate.integrate_times( model=pde.core.models.FiniteDifferenceModel(equation,grid), state=initial_state, times=times, axis=0)

The error that I get is (I will also send it to the AutoGraph team as they requested) :

W0717 14:24:34.494019 140735880733568 ag_logging.py:145] Entity <function integrate_steps..advance_until_saved_step at 0xb4026f378> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: converting <function integrate_steps..advance_until_saved_step at 0xb4026f378>: ValueError: inconsistent nodes: None (NoneType) and None (NoneType) WARNING: Entity <function integrate_steps..advance_until_saved_step at 0xb4026f378> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: converting <function integrate_steps..advance_until_saved_step at 0xb4026f378>: ValueError: inconsistent nodes: None (NoneType) and None (NoneType)

TypeError Traceback (most recent call last)

in 1 time_step = equation.get_time_step(grid) 2 times = time_step*np.arange(400) ----> 3 results = pde.core.integrate.integrate_times(model=pde.core.models.FiniteDifferenceModel(equation,grid), state=initial_state,times=times, axis=0) ~/Dropbox/MIT/projects/burgers_theta_updated/pde_superresolution_2d/datadrivenpdes/core/integrate.py in integrate_times(model, state, times, initial_time, axis, xla_compile) 133 'time step {}: {}'.format(times, dt, approx_steps)) 134 --> 135 return integrate_steps(model, state, steps, initial_time, axis, xla_compile) ~/Dropbox/MIT/projects/burgers_theta_updated/pde_superresolution_2d/datadrivenpdes/core/integrate.py in integrate_steps(***failed resolving arguments***) 88 starts = tf.concat([[0], steps[:-1]], axis=0) 89 integrated = tf.scan(advance_until_saved_step, [starts, steps], ---> 90 initializer=evolving_state) 91 92 integrated_constants = nest.map_structure( ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py in scan(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, infer_shape, reverse, name) 503 back_prop=back_prop, 504 swap_memory=swap_memory, --> 505 maximum_iterations=n) 506 507 results_flat = [r.stack() for r in r_a] ~/anaconda3/envs/pde_TF/lib/python3.7/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, return_same_structure) 3461 3462 while cond(*loop_vars): -> 3463 loop_vars = body(*loop_vars) 3464 if try_to_pack and not isinstance(loop_vars, (list, _basetuple)): 3465 packed = True ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/ops/control_flow_ops.py in (i, lv) 3454 cond = lambda i, lv: ( # pylint: disable=g-long-lambda 3455 math_ops.logical_and(i < maximum_iterations, orig_cond(*lv))) -> 3456 body = lambda i, lv: (i + 1, orig_body(*lv)) 3457 3458 if executing_eagerly: ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/ops/functional_ops.py in compute(i, a_flat, tas) 480 packed_elems = input_pack([elem_ta.read(i) for elem_ta in elems_ta]) 481 packed_a = output_pack(a_flat) --> 482 a_out = fn(packed_a, packed_elems) 483 nest.assert_same_structure(elems if initializer is None else initializer, 484 a_out) ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds) 412 # This is the first call of __call__, so we have to initialize. 413 initializer_map = {} --> 414 self._initialize(args, kwds, add_initializers_to=initializer_map) 415 if self._created_variables: 416 try: ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to) 355 self._concrete_stateful_fn = ( 356 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access --> 357 *args, **kwds)) 358 359 def invalid_creator_scope(*unused_args, **unused_kwds): ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 1347 if self.input_signature: 1348 args, kwargs = None, None -> 1349 graph_function, _, _ = self._maybe_define_function(args, kwargs) 1350 return graph_function 1351 ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs) 1650 graph_function = self._function_cache.primary.get(cache_key, None) 1651 if graph_function is None: -> 1652 graph_function = self._create_graph_function(args, kwargs) 1653 self._function_cache.primary[cache_key] = graph_function 1654 return graph_function, args, kwargs ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 1543 arg_names=arg_names, 1544 override_flat_arg_shapes=override_flat_arg_shapes, -> 1545 capture_by_value=self._capture_by_value), 1546 self._function_attributes) 1547 ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes) 713 converted_func) 714 --> 715 func_outputs = python_func(*func_args, **func_kwargs) 716 717 # invariant: `func_outputs` contains only Tensors, CompositeTensors, ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds) 305 # __wrapped__ allows AutoGraph to swap in a converted function. We give 306 # the function a weak reference to itself to avoid a reference cycle. --> 307 return weak_wrapped_fn().__wrapped__(*args, **kwds) 308 weak_wrapped_fn = weakref.ref(wrapped_fn) 309 ~/anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 703 except Exception as e: # pylint:disable=broad-except 704 if hasattr(e, "ag_error_metadata"): --> 705 raise e.ag_error_metadata.to_exception(type(e)) 706 else: 707 raise TypeError: in converted code: relative to /Users/yani: Dropbox/MIT/projects/burgers_theta_updated/pde_superresolution_2d/datadrivenpdes/core/integrate.py:80 advance_until_saved_step while i < stop: anaconda3/envs/pde_TF/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:690 __bool__ raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. " TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor. I use Python 3.7.3, and the packages versions that I use are: absl-py==0.7.1 apache-beam==2.13.0 appnope==0.1.0 astor==0.8.0 attrs==19.1.0 avro-python3==1.9.0 backcall==0.1.0 bleach==3.1.0 certifi==2019.6.16 chardet==3.0.4 crcmod==1.7 cycler==0.10.0 decorator==4.4.0 defusedxml==0.6.0 dill==0.2.9 docopt==0.6.2 entrypoints==0.3 fastavro==0.21.24 future==0.17.1 gast==0.2.2 google-pasta==0.1.7 grpcio==1.22.0 h5py==2.9.0 hdfs==2.5.8 httplib2==0.12.0 idna==2.8 ipykernel==5.1.1 ipython==7.6.1 ipython-genutils==0.2.0 ipywidgets==7.5.0 jedi==0.13.3 Jinja2==2.10.1 jsonschema==3.0.1 jupyter==1.0.0 jupyter-client==5.3.1 jupyter-console==6.0.0 jupyter-core==4.5.0 Keras-Applications==1.0.8 Keras-Preprocessing==1.1.0 kiwisolver==1.1.0 Markdown==3.1.1 MarkupSafe==1.1.1 matplotlib==3.1.0 mistune==0.8.4 mkl-fft==1.0.12 mkl-random==1.0.2 mock==2.0.0 nbconvert==5.5.0 nbformat==4.4.0 notebook==5.7.8 numpy==1.16.4 oauth2client==3.0.0 pandas==0.24.2 pandocfilters==1.4.2 parso==0.5.0 pbr==5.4.0 pexpect==4.7.0 pickleshare==0.7.5 prometheus-client==0.7.1 prompt-toolkit==2.0.9 protobuf==3.9.0 ptyprocess==0.6.0 pyarrow==0.13.0 pyasn1==0.4.5 pyasn1-modules==0.2.5 pydot==1.2.4 Pygments==2.4.2 pyparsing==2.4.0 pyrsistent==0.14.11 python-dateutil==2.8.0 pytz==2019.1 PyYAML==3.13 pyzmq==18.0.0 qtconsole==4.5.1 requests==2.22.0 rsa==4.0 scipy==1.2.1 Send2Trash==1.5.0 six==1.12.0 tensorboard==1.14.0 tensorflow==1.14.0 tensorflow-estimator==1.14.0 termcolor==1.1.0 terminado==0.8.2 testpath==0.4.2 tornado==6.0.3 traitlets==4.3.2 urllib3==1.25.3 wcwidth==0.1.7 webencodings==0.5.1 Werkzeug==0.15.4 widgetsnbextension==3.5.0 wrapt==1.11.2 xarray==0.12.3 xgboost==0.90

[shoyer]

I just synced our internal code to this repository again. I think this specific error may be fixed by https://github.com/google-research/data-driven-pdes/commit/e182dde76e7d4f26e7f7381589acc31be28bc6ad. Can you give this a try again?

(I also really should get a continuous integration setup going on GitHub...)

[yaniyuval]

Hi Stephan, Thanks for the fast response. It is working now. Me and Nick understand better from your tutorial how to define and solve an equation. What seems to us complicated (or at least would require from us to dive into your code, which we want to avoid), is where could we use some ML to predict different things. Namely, if we want to train some algorithm, to predict some of the terms in the equation (e.g., predict a forcing based on ML, and not based on analytic formulation), where should we do it in the code.

Thanks, Yani and Nick

On Wed, Jul 17, 2019 at 2:45 PM Stephan Hoyer notifications@github.com wrote:

I just synced our internal code to this repository again. I think this specific error may be fixed by e182dde https://github.com/google-research/data-driven-pdes/commit/e182dde76e7d4f26e7f7381589acc31be28bc6ad. Can you give this a try again?

(I also really should get a continuous integration setup going on GitHub...)

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/google-research/data-driven-pdes/issues/7?email_source=notifications&email_token=AFPRKJJJQXEELUAPPHNBP2LP75SELA5CNFSM4IETJ3EKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD2GG6LQ#issuecomment-512519982, or mute the thread https://github.com/notifications/unsubscribe-auth/AFPRKJPI325PRBRUZFNKXGDP75SELANCNFSM4IETJ3EA .

[yohai]

I think (@shoyer correct me if I'm wrong) what you need to do is write a Model (see here). As I wrote in the tutorial, a Model is a structure that takes the current state and calculates the time derivative. This is done by the method Model.time_derivative, which calculates all the inputs needed for the Equation.time_derivative function.

The model that you write calculate the inputs in any way you want, Random Forests or CNNs or whatever.

[yohai]

Eventually I'll add an example for this to the tutorial, but it will take a few weeks

[shoyer]

I think (@shoyer correct me if I'm wrong) what you need to do is write a Model (see here

Generally you only need to reuse an existing Model, not write one from scratch. PseudoLinearModel is the main model we used in our PNAS paper.

[yohai]

Thanks. but they need to learn a reaction term, possibly not in a convolutional setting. They might have to write a new one.

On Tue, Jul 23, 2019 at 11:53 AM Stephan Hoyer notifications@github.com wrote:

I think (@shoyer https://github.com/shoyer correct me if I'm wrong) what you need to do is write a Model (see here https://github.com/google-research/data-driven-pdes/blob/master/datadrivenpdes/core/models.py

Generally you only need to reuse an existing Model, not write one from scratch. PseudoLinearModel is the main model we used in our PNAS paper.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/google-research/data-driven-pdes/issues/7?email_source=notifications&email_token=ABPA5PMHIR3RCS7BUPCJYETQA4SPJA5CNFSM4IETJ3EKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOD2TSQXA#issuecomment-514271324, or mute the thread https://github.com/notifications/unsubscribe-auth/ABPA5PMT2MJKJTG7B3BQ5RDQA4SPJANCNFSM4IETJ3EA .

-- Yohai Bar Sinai Post Doctoral Fellow John A. Paulson School of Engineering and Applied Sciences Harvard University