Tabular_learner error - Found dtype Char but expected Float

Getting the following error when I try using the nn tabular learner:

RuntimeError: Found dtype Char but expected Float

It’s happening both when I try lr_find or fit_one_cycle. Have a minimal reproduction of the error below :smile:

import pandas as pd
from fastai.tabular.all import *

data = {
    'user_id': {0: 32, 1: 100, 2: 122, 3: 156, 4: 152, 5: 166, 6: 155, 7: 308, 8: 330, 9: 523},
    'other_user_id': {0: 60, 1: 60, 2: 60, 3: 60, 4: 60, 5: 60, 6: 60, 7: 60, 8: 60, 9: 60},
    'type': {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 1, 6: 0, 7: 0, 8: 0, 9: 0}
}

df = pd.DataFrame(data)

dep_var = "type"
cont,cat = cont_cat_split(df, max_card=7000, dep_var=dep_var)
splits = [[0, 1, 3, 4, 5, 6, 7], [2, 8, 9]]

procs = [Categorify, FillMissing, Normalize]
to = TabularPandas(df, procs, cat, cont, splits=splits, y_names=dep_var)
dls = to.dataloaders(5)

learn = tabular_learner(dls, y_range=(-0.2,1.2), layers=[500,250],
                        n_out=1, loss_func=F.mse_loss)

learn.lr_find() # or `learn.fit_one_cycle(5, 1e-2)`

And the error it throws:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-3-1d9d22177d5b> in <module>
     21                         n_out=1, loss_func=F.mse_loss)
     22 
---> 23 learn.lr_find() # or `learn.fit_one_cycle(5, 1e-2)`

/usr/local/lib/python3.8/site-packages/fastai/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
    226     n_epoch = num_it//len(self.dls.train) + 1
    227     cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
--> 228     with self.no_logging(): self.fit(n_epoch, cbs=cb)
    229     if show_plot: self.recorder.plot_lr_find()
    230     if suggestions:

/usr/local/lib/python3.8/site-packages/fastcore/utils.py in _f(*args, **kwargs)
    452         init_args.update(log)
    453         setattr(inst, 'init_args', init_args)
--> 454         return inst if to_return else f(*args, **kwargs)
    455     return _f
    456 

/usr/local/lib/python3.8/site-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
    202             self.opt.set_hypers(lr=self.lr if lr is None else lr)
    203             self.n_epoch,self.loss = n_epoch,tensor(0.)
--> 204             self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
    205 
    206     def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _do_fit(self)
    192         for epoch in range(self.n_epoch):
    193             self.epoch=epoch
--> 194             self._with_events(self._do_epoch, 'epoch', CancelEpochException)
    195 
    196     @log_args(but='cbs')

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _do_epoch(self)
    186 
    187     def _do_epoch(self):
--> 188         self._do_epoch_train()
    189         self._do_epoch_validate()
    190 

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _do_epoch_train(self)
    178     def _do_epoch_train(self):
    179         self.dl = self.dls.train
--> 180         self._with_events(self.all_batches, 'train', CancelTrainException)
    181 
    182     def _do_epoch_validate(self, ds_idx=1, dl=None):

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.8/site-packages/fastai/learner.py in all_batches(self)
    159     def all_batches(self):
    160         self.n_iter = len(self.dl)
--> 161         for o in enumerate(self.dl): self.one_batch(*o)
    162 
    163     def _do_one_batch(self):

/usr/local/lib/python3.8/site-packages/fastai/learner.py in one_batch(self, i, b)
    174         self.iter = i
    175         self._split(b)
--> 176         self._with_events(self._do_one_batch, 'batch', CancelBatchException)
    177 
    178     def _do_epoch_train(self):

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _do_one_batch(self)
    167         if not self.training: return
    168         self('before_backward')
--> 169         self._backward();                                self('after_backward')
    170         self._step();                                    self('after_step')
    171         self.opt.zero_grad()

/usr/local/lib/python3.8/site-packages/fastai/learner.py in _backward(self)
    150 
    151     def _step(self): self.opt.step()
--> 152     def _backward(self): self.loss.backward()
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):

/usr/local/lib/python3.8/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
    183                 products. Defaults to ``False``.
    184         """
--> 185         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    186 
    187     def register_hook(self, hook):

/usr/local/lib/python3.8/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
    123         retain_graph = create_graph
    124 
--> 125     Variable._execution_engine.run_backward(
    126         tensors, grad_tensors, retain_graph, create_graph,
    127         allow_unreachable=True)  # allow_unreachable flag

RuntimeError: Found dtype Char but expected Float
Exception raised from compute_types at ../aten/src/ATen/native/TensorIterator.cpp:183 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >) + 169 (0x12ce2f7f9 in libc10.dylib)
frame #1: at::TensorIterator::compute_types(at::TensorIteratorConfig const&) + 3842 (0x11f772372 in libtorch_cpu.dylib)
frame #2: at::TensorIterator::build(at::TensorIteratorConfig&) + 618 (0x11f77b57a in libtorch_cpu.dylib)
frame #3: at::TensorIterator::TensorIterator(at::TensorIteratorConfig&) + 223 (0x11f77b25f in libtorch_cpu.dylib)
frame #4: at::native::mse_loss_backward_out(at::Tensor&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 410 (0x11f5c6fda in libtorch_cpu.dylib)
frame #5: at::CPUType::mse_loss_backward_out_grad_input(at::Tensor&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 9 (0x11f9e5049 in libtorch_cpu.dylib)
frame #6: at::mse_loss_backward_out(at::Tensor&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 157 (0x11fa984ad in libtorch_cpu.dylib)
frame #7: at::native::mse_loss_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 118 (0x11f5c6d56 in libtorch_cpu.dylib)
frame #8: at::CPUType::mse_loss_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 14 (0x11f9e505e in libtorch_cpu.dylib)
frame #9: c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long), at::Tensor, c10::guts::typelist::typelist<at::Tensor const&, at::Tensor const&, at::Tensor const&, long long> >, at::Tensor (at::Tensor const&, at::Tensor const&, at::Tensor const&, long long)>::call(c10::OperatorKernel*, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 27 (0x11f3968db in libtorch_cpu.dylib)
frame #10: at::Tensor c10::Dispatcher::call<at::Tensor, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long>(c10::TypedOperatorHandle<at::Tensor (at::Tensor const&, at::Tensor const&, at::Tensor const&, long long)> const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) const + 287 (0x11fac719f in libtorch_cpu.dylib)
frame #11: at::mse_loss_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 157 (0x11fa9857d in libtorch_cpu.dylib)
frame #12: torch::autograd::VariableType::mse_loss_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 900 (0x121a6bba4 in libtorch_cpu.dylib)
frame #13: c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoRuntimeFunctor_<at::Tensor (*)(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long), at::Tensor, c10::guts::typelist::typelist<at::Tensor const&, at::Tensor const&, at::Tensor const&, long long> >, at::Tensor (at::Tensor const&, at::Tensor const&, at::Tensor const&, long long)>::call(c10::OperatorKernel*, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 27 (0x11f3968db in libtorch_cpu.dylib)
frame #14: at::Tensor c10::Dispatcher::call<at::Tensor, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long>(c10::TypedOperatorHandle<at::Tensor (at::Tensor const&, at::Tensor const&, at::Tensor const&, long long)> const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) const + 287 (0x11fac719f in libtorch_cpu.dylib)
frame #15: at::mse_loss_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, long long) + 157 (0x11fa9857d in libtorch_cpu.dylib)
frame #16: torch::autograd::generated::MseLossBackward::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> >&&) + 298 (0x1218f6f7a in libtorch_cpu.dylib)
frame #17: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> >&&) + 742 (0x1220507e6 in libtorch_cpu.dylib)
frame #18: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&) + 1846 (0x1220458a6 in libtorch_cpu.dylib)
frame #19: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 764 (0x12204481c in libtorch_cpu.dylib)
frame #20: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr<torch::autograd::GraphTask> const&, std::__1::shared_ptr<torch::autograd::Node>) + 1023 (0x12204e87f in libtorch_cpu.dylib)
frame #21: torch::autograd::python::PythonEngine::execute_with_graph_task(std::__1::shared_ptr<torch::autograd::GraphTask> const&, std::__1::shared_ptr<torch::autograd::Node>) + 53 (0x11e4b7345 in libtorch_python.dylib)
frame #22: torch::autograd::Engine::execute(std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge> > const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, bool, bool, std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge> > const&) + 662 (0x12204cee6 in libtorch_cpu.dylib)
frame #23: torch::autograd::python::PythonEngine::execute(std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge> > const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor> > const&, bool, bool, std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge> > const&) + 82 (0x11e4b7142 in libtorch_python.dylib)
frame #24: THPEngine_run_backward(THPEngine*, _object*, _object*) + 2174 (0x11e4b7cee in libtorch_python.dylib)
frame #25: cfunction_call_varargs + 171 (0x10c30e0f6 in Python)
frame #26: _PyObject_MakeTpCall + 274 (0x10c30dbe5 in Python)
frame #27: call_function + 804 (0x10c3ae572 in Python)
frame #28: _PyEval_EvalFrameDefault + 30479 (0x10c3ab11f in Python)
frame #29: _PyEval_EvalCodeWithName + 1958 (0x10c3aeed7 in Python)
frame #30: _PyFunction_Vectorcall + 228 (0x10c30e590 in Python)
frame #31: call_function + 346 (0x10c3ae3a8 in Python)
frame #32: _PyEval_EvalFrameDefault + 30081 (0x10c3aaf91 in Python)
frame #33: _PyEval_EvalCodeWithName + 1958 (0x10c3aeed7 in Python)
frame #34: _PyFunction_Vectorcall + 228 (0x10c30e590 in Python)
frame #35: call_function + 346 (0x10c3ae3a8 in Python)
frame #36: _PyEval_EvalFrameDefault + 30053 (0x10c3aaf75 in Python)
frame #37: function_code_fastcall + 106 (0x10c30e42a in Python)
frame #38: call_function + 346 (0x10c3ae3a8 in Python)
frame #39: _PyEval_EvalFrameDefault + 30053 (0x10c3aaf75 in Python)
frame #40: function_code_fastcall + 106 (0x10c30e42a in Python)
frame #41: method_vectorcall + 135 (0x10c3104db in Python)
frame #42: call_function + 346 (0x10c3ae3a8 in Python)
frame #43: _PyEval_EvalFrameDefault + 30273 (0x10c3ab051 in Python)
frame #44: _PyEval_EvalCodeWithName + 1958 (0x10c3aeed7 in Python)
frame #45: _PyFunction_Vectorcall + 228 (0x10c30e590 in Python)
frame #46: call_function + 346 (0x10c3ae3a8 in Python)
frame #47: _PyEval_EvalFrameDefault + 30053 (0x10c3aaf75 in Python)
frame #48: function_code_fastcall + 106 (0x10c30e42a in Python)
frame #49: method_vectorcall + 372 (0x10c3105c8 in Python)
frame #50: PyVectorcall_Call + 108 (0x10c30de71 in Python)
frame #51: _PyEval_EvalFrameDefault + 31000 (0x10c3ab328 in Python)
frame #52: function_code_fastcall + 106 (0x10c30e42a in Python)
frame #53: method_vectorcall + 135 (0x10c3104db in Python)
frame #54: call_function + 346 (0x10c3ae3a8 in Python)
frame #55: _PyEval_EvalFrameDefault + 30273 (0x10c3ab051 in Python)
frame #56: _PyEval_EvalCodeWithName + 1958 (0x10c3aeed7 in Python)
frame #57: _PyFunction_Vectorcall + 228 (0x10c30e590 in Python)
frame #58: call_function + 346 (0x10c3ae3a8 in Python)
frame #59: _PyEval_EvalFrameDefault + 30053 (0x10c3aaf75 in Python)
frame #60: function_code_fastcall + 106 (0x10c30e42a in Python)
frame #61: call_function + 346 (0x10c3ae3a8 in Python)
frame #62: _PyEval_EvalFrameDefault + 30053 (0x10c3aaf75 in Python)
frame #63: function_code_fastcall + 106 (0x10c30e42a in Python)

What does dls.show_batch() return?

1 Like

Hey @muellerzr! This is what it outputs:

image

Had the same problem and error and I change dtype of my targets (y) to float32 and it resolved! maybe this can solve yours as well

5 Likes

As @moein mentioned try this:

df[‘type’] = df[‘type’].astype(np.float32)

Worked for me as well.

2 Likes

@moein What if my target variable is a category?

1 Like

There are plenty of choices for this case. I don’t know if you are using TabularPandas; because if you provide it with Categorify proc, it will do that automatically for you. But if you are implementing something from scratch, you can numericalize you categories by first making a vocab, or simply use pandas get_dummies to make them one-hot encoded

1 Like

df_nn = df_nn.astype({dep_var: np.float16})
worked for me, at least.

1 Like

this also worked for me. Thanks!

I have a dataset with categorical targets, and am getting a ‘type’ error even though I passed TabularPandas the Categorify proc. Any ideas why it’s not converting my column of boolean values to floats?