I am getting compilation error in the coding line given below...Kindly tell me ....how to fix it.... I am using google collab for compilation

dg = Solver(config) dg.fit(batch_size=50, sampler=s, n_iters=300, bar='notebook')

OperatorNotAllowedInGraphError Traceback (most recent call last) in () ----> 1 dg = Solver(config) 2 dg.fit(batch_size=50, sampler=s, n_iters=300, bar='notebook')

15 frames /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _disallow_in_graph_mode(self, task) 521 raise errors.OperatorNotAllowedInGraphError( 522 "{} is not allowed in Graph execution. Use Eager execution or decorate" --> 523 " this function with @tf.function.".format(task)) 524 525 def _disallow_bool_casting(self):

OperatorNotAllowedInGraphError: iterating over tf.Tensor is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.

I'm using Spyder3 to run this code:

` import pydens as pde from pydens import D import numpy as np import tensorflow as tf

pde.add_tokens()

pde = {'n_dims': 2, 'form': lambda u, x, y: D(D(u, x), x) + D(D(u, y), y) - 5 * pde.sin(np.pi * (x + y)), 'boundary_condition': 1}

body = {'layout': 'fa fa fa f', #estrutura da rede neural (layers) 'units': [15, 25, 15, 1], #neurônios por camada 'activation': [tf.nn.tanh, tf.nn.tanh, tf.nn.tanh]} #ativação advinda do TFlow

config = {'body': body, 'pde': pde}

us = pde.NumpySampler('uniform', dim=2)

dg = pde.Solver(config) dg.fit(batch_size=100, sampler=us, n_iters=1500) ` ...and I'm getting this error:

`File "C:\Users\my_user\Anaconda3\lib\site-packages\pydens\letters.py", line 20, in from .batchflow.models.tf.layers import conv_block

ModuleNotFoundError: No module named 'pydens.batchflow'`

My tersorflow are actualized and I got PyDEns and Batchflow with pip on conda terminal.

Hello Everyone I want to change the interval of x. Instead of using the default one [0,1], I want to have [1, 1.5] so I can define my boundary conditions in these two points? How could I modify the code?

Dear Guys I am extremely interested in working with your product but I have a problem installing the packages. I installed using different ways but I faced "No module named 'pydens.batchflow'" and I do not have any idea to solve it. I will appreciate it if could do a favor of installing.

Thanks in advanced

Hello, I want to code according to the pydens and during compilation I am using an error at "dg solver line". Kindly fix it ASAP so that I can move further to code my problem.

I am using tensorflow 1.15 version ======================code================

%tensorflow_version 1.x import os import sys

Stop TF from showing unnecessary deprecation warnings

import warnings warnings.filterwarnings('ignore') from tensorflow import logging logging.set_verbosity(logging.ERROR) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import numpy as np import tensorflow as tf from tqdm import tqdm_notebook import matplotlib.pyplot as plt

sys.path.append('..') # this line is not needed if PyDEns is installed as package from pydens import Solver, NumpySampler, cart_prod, add_tokens from pydens import plot_loss, plot_pair_1d, plot_2d, plot_sections_2d, plot_sections_3d

add_tokens()

describing pde-problem in pde-dict

pde = { 'n_dims': 1, 'form': lambda u, t: D(u, t) - 2 * np.pi * cos(2 * np.pi * t), 'initial_condition': 1 }

put it together in model-config

config = { 'pde': pde, 'track': {'dt': lambda u, t: D(u, t)} # allows to later fetch this value from the model }

uniform sampling scheme

s = NumpySampler('uniform')

train the network on batches of 100 points

dg = Solver(config) dg.fit(batch_size=100, sampler=s, n_iters=2000, bar='notebook')

plot_loss(dg.loss, color='cornflowerblue')

plot real solution and network approximation

pts = np.linspace(0, 1, 200).reshape(-1, 1) sol = lambda t: np.sin(2 * np.pi * t) + 1 true = [sol(t[0]) for t in pts] approxs = dg.solve(pts)

plt.plot(pts, true, 'b--', linewidth=3, label='True solution') plt.plot(pts, approxs, 'r', label='Network approximation') plt.xlabel(r'$t$', fontdict={'fontsize': 14}) plt.legend() plt.show()

plot approximation of solution-derivative

der = lambda t: 2 * np.pi * np.cos(2 * np.pi * t) true_der = [der(t[0]) for t in pts] ders = dg.solve(pts, fetches='dt')

plt.plot(pts, true_der, 'b--', linewidth=3, label=r'True derivative') plt.plot(pts, ders, 'r', label=r'Network approximation of $\frac{d u}{d t}$') plt.xlabel(r'$t$', fontdict={'fontsize': 14}) plt.legend() plt.show()

plot_pair_1d(model=dg, solution=lambda t: np.sin(2np.pit)+1, confidence=0.15, alpha=0.2)

plot_pair_1d(model=dg, solution=lambda t: 2 * np.pi * np.cos(2 * np.pi * t), fetches='dt')

======================error is==================== OperatorNotAllowedInGraphError Traceback (most recent call last) in () 40 41 # train the network on batches of 100 points ---> 42 dg = Solver(config) 43 dg.fit(batch_size=100, sampler=s, n_iters=2000, bar='notebook') 44

15 frames /tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in _disallow_in_graph_mode(self, task) 521 raise errors.OperatorNotAllowedInGraphError( 522 "{} is not allowed in Graph execution. Use Eager execution or decorate" --> 523 " this function with @tf.function.".format(task)) 524 525 def _disallow_bool_casting(self):

OperatorNotAllowedInGraphError: iterating over tf.Tensor is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.

While running the solved example:

import os
import warnings
import sys

warnings.filterwarnings('ignore')
from tensorflow import logging
logging.set_verbosity(logging.ERROR)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

import numpy as np
import tensorflow as tf
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt

sys.path.append('..')
from pydens import Solver, NumpySampler, cart_prod
from pydens import plot_loss, plot_2d, plot_pair_1d, plot_sections_2d, plot_sections_3d

from pydens import add_tokens
add_tokens()

# describing pde-problem in pde-dict
pde = {'n_dims': 1,
       'form': lambda u, t: D(u, t) - 2 * np.pi * cos(2 * np.pi * t),
       'initial_condition': 1 # will be transformed into callable returning 1
      }

# put it together in model-config
config = {'pde': pde,
          'track': {'dt': lambda u, t: D(u, t)}}

# uniform sampling scheme
s = NumpySampler('uniform')

#
## train the network on batches of 100 points
dg = Solver(config)

I run into the following error: TypeError: 'SyntaxTreeNode' object is not iterable

The stacktrace is:

File "<ipython-input-21-1b5c74329633>", line 1, in <module>
    runfile('/home/nirvik/Documents/neuronal_model_python/temp.py', wdir='/home/nirvik/Documents/neuronal_model_python')

  File "/usr/lib/python3/dist-packages/spyder/utils/site/sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "/usr/lib/python3/dist-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "/home/nirvik/Documents/neuronal_model_python/temp.py", line 38, in <module>
    dg = Solver(config)

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/wrapper.py", line 21, in __init__
    self.model = model_class(config)

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/batchflow/batchflow/models/tf/base.py", line 262, in __init__
    super().__init__(*args, **kwargs)

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/batchflow/batchflow/models/base.py", line 38, in __init__
    self.build(*args, **kwargs)

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/batchflow/batchflow/models/tf/base.py", line 331, in build
    config = self.build_config()

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/model_tf.py", line 117, in build_config
    n_parameters = get_num_parameters(form[0])

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/syntax_tree.py", line 77, in get_num_parameters
    tree = form(*[SyntaxTreeNode('_' + str(i)) for i in range(n_args)])

  File "/home/nirvik/Documents/neuronal_model_python/temp.py", line 25, in <lambda>
    'form': lambda u, t: D(u, t) - 2 * np.pi * 2 * t^3,

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/tokens.py", line 63, in <lambda>
    if isinstance(args[0], SyntaxTreeNode) else method_(*args, **kwargs))

  File "/home/nirvik/.local/lib/python3.6/site-packages/pydens/letters.py", line 82, in D
    return np.stack([self.D(func, coordinate) for coordinate in coordinates],
```

I am new to using this package...please help.

pyDens not plotting after running solver.fit() on both Jupyter notebook and google colaboratory. Using the first example from Github solver.fit() seems to run fine but I was expecting a plot of the results to appear. What am i missing?

This pr adds torch-based model and solver and removes tensorflow completely. Leaves only one tutorial covering wide range of examples; updates pylint to working state.

Classes added by this pr:

• TorchModel
• Solver
• ConvBlockModel - model based on conv_block from batchflow.

NOTE: despite the diff looking complex, there are basically two files in the library: model_torch.py and the tutorial notebook.

Main addition

• Add saving and loading capabilities to a PyDEns-Solver class. Saving is done via Solver.save-method, while loading is performed by adding path-argument to a Solver-initialization procedure.

For example: """ body = {'layout': 'fa f', 'units': [10, 1], 'activation': [tf.nn.tanh,]} """ This means that there are one first full connected layer with 10 units and activation "tanh" ('fa') and a final full connected layer with no activation ('f').

But what about: """ body = {'layout': 'faR fa fa+ f', 'units': [10, 25, 10, 1], 'activation': [tf.nn.tanh, tf.nn.tanh, tf.nn.tanh]} """ What does 'R' mean? And the character '+'?

Thank you.

Main additions

• update plot utils: now all plot funcs for 3d-problems allow for three different plot-modes: imshow, contourf and 3d-view
• move file with plot utils into pydens-module for convenient usage in tutorials
• update tutorials using plot-funcs thus making them shorter

The code block below results in error:

solver = Solver(equation=pde, ndims=2,constraints=constraints,domain=domain,layout='fa fa f', units=(10,20,1), activation='Sigmoid')
solver.fit(batch_size=100, niters=5000, lr=0.01,optimizer='LBFGS')

Error:

  0%|                                                                                   | 0/5000 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
File <timed exec>:8, in <module>

File D:\anaconda3\envs\pydens_gpu\lib\site-packages\pydens\model_torch.py:461, in Solver.fit(self, niters, batch_size, sampler, loss_terms, optimizer, criterion, lr, **kwargs)
    459 # Optimizer step.
    460 loss.backward()
--> 461 self.optimizer.step()
    463 # Gather and store training stats.
    464 self.losses.append(loss.detach().cpu().numpy())

File D:\anaconda3\envs\pydens_gpu\lib\site-packages\torch\optim\optimizer.py:88, in Optimizer._hook_for_profile.<locals>.profile_hook_step.<locals>.wrapper(*args, **kwargs)
     86 profile_name = "Optimizer.step#{}.step".format(obj.__class__.__name__)
     87 with torch.autograd.profiler.record_function(profile_name):
---> 88     return func(*args, **kwargs)

File D:\anaconda3\envs\pydens_gpu\lib\site-packages\torch\autograd\grad_mode.py:27, in _DecoratorContextManager.__call__.<locals>.decorate_context(*args, **kwargs)
     24 @functools.wraps(func)
     25 def decorate_context(*args, **kwargs):
     26     with self.clone():
---> 27         return func(*args, **kwargs)

TypeError: step() missing 1 required positional argument: 'closure'

Any idea on how to use LBFGS optimizer ?

Sorry if this is obvious of a question, but is there a way to learn the PDE from data? I don't have the PDE as is shown throughout the examples https://github.com/analysiscenter/pydens/blob/master/tutorials/1.%20Solving%20PDEs.ipynb to define a function for the solver.

Hi, Can Pydens handle the PDE with more than 2 boundary conditions? In the parametric heat-equation example, would you please show me how to change the boundary condition to: case 1: u(x,y,t=0)=1000 u(x=0, y=0, t)=200 case 2: u(x,y,t=0)=1000 u(x=0, y=0, t)=200 u(xe,0)=u(-xe,0)=u(0,ye)=u(ye,0)=1000 (e represents edge, which indicates all boundaries have same value)

Dear all, I have a problem installing the packages. In Jypyter notebook , Python 3; I use pip install pydens and it has installed successfully. Next when writing from pydens import Solver, NumpySampler, add_tokens getting error "module 'tensorflow' has no attribute 'layers'" Plz fix it.