Minibatch size must evenly divide validation split

[arvoelke]

Steps to reproduce:

• Open docs/examples/spiking-mnist.ipynb
• Change sim.fit(...) to sim.fit(..., validation_split=0.55)
• Set do_training = True and then run notebook

Train on 26999 samples, validate on 33001 samples
Epoch 1/200
26800/26999 [============================>.] - ETA: 0s - loss: 0.2636 - probe_loss: 0.2636
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-10-c2eaadcf9727> in <module>
      6         loss={out_p: tf.losses.SparseCategoricalCrossentropy(from_logits=True)}
      7     )
----> 8     sim.fit({inp: train_images}, {out_p: train_labels}, epochs=200, validation_split=0.55)
      9 
     10     # save the parameters to file

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/nengo/utils/magic.py in __call__(self, *args, **kwargs)
    179                 return self.wrapper(wrapped, instance, args, kwargs)
    180             else:
--> 181                 return self.wrapper(self.__wrapped__, self.instance, args, kwargs)
    182         else:
    183             instance = getattr(self.__wrapped__, "__self__", None)

~/git/nengo-dl/nengo_dl/simulator.py in require_open(wrapped, instance, args, kwargs)
     64         )
     65 
---> 66     return wrapped(*args, **kwargs)
     67 
     68 

~/git/nengo-dl/nengo_dl/simulator.py in fit(self, x, y, n_steps, stateful, **kwargs)
    847 
    848         return self._call_keras(
--> 849             "fit", x=x, y=y, n_steps=n_steps, stateful=stateful, **kwargs
    850         )
    851 

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/nengo/utils/magic.py in __call__(self, *args, **kwargs)
    179                 return self.wrapper(wrapped, instance, args, kwargs)
    180             else:
--> 181                 return self.wrapper(self.__wrapped__, self.instance, args, kwargs)
    182         else:
    183             instance = getattr(self.__wrapped__, "__self__", None)

~/git/nengo-dl/nengo_dl/simulator.py in with_self(wrapped, instance, args, kwargs)
     48         instance.tensor_graph.device
     49     ):
---> 50         output = wrapped(*args, **kwargs)
     51     tf.keras.backend.set_floatx(keras_dtype)
     52 

~/git/nengo-dl/nengo_dl/simulator.py in _call_keras(self, func_type, x, y, n_steps, stateful, **kwargs)
    995             func_args = dict(x=x, y=y, **kwargs)
    996 
--> 997         outputs = getattr(self.keras_model, func_type)(**func_args)
    998 
    999         # update n_steps/time

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    726         max_queue_size=max_queue_size,
    727         workers=workers,
--> 728         use_multiprocessing=use_multiprocessing)
    729 
    730   def evaluate(self,

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    672         validation_steps=validation_steps,
    673         validation_freq=validation_freq,
--> 674         steps_name='steps_per_epoch')
    675 
    676   def evaluate(self,

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)
    391 
    392         # Get outputs.
--> 393         batch_outs = f(ins_batch)
    394         if not isinstance(batch_outs, list):
    395           batch_outs = [batch_outs]

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py in __call__(self, inputs)
   3578 
   3579     fetched = self._callable_fn(*array_vals,
-> 3580                                 run_metadata=self.run_metadata)
   3581     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   3582     output_structure = nest.pack_sequence_as(

~/anaconda3/envs/nengo-dl/lib/python3.7/site-packages/tensorflow_core/python/client/session.py in __call__(self, *args, **kwargs)
   1470         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1471                                                self._handle, args,
-> 1472                                                run_metadata_ptr)
   1473         if run_metadata:
   1474           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument: Input to reshape is a tensor with 156016 values, but the requested shape has 156800
	 [[{{node TensorGraph/while/iteration_0/DotIncBuilder/Reshape_1}}]]
	 [[loss_2/mul/_187]]
  (1) Invalid argument: Input to reshape is a tensor with 156016 values, but the requested shape has 156800
	 [[{{node TensorGraph/while/iteration_0/DotIncBuilder/Reshape_1}}]]
0 successful operations.
0 derived errors ignored.

This error seems to appear when the size of the validation split (here, 33001) is not evenly divisble by the minibatch size (here, 200).

Versions:

• Python 3.7
• master nengo-dl
• latest conda install tensorflow-gpu

[kebitmatf]

still got same error too... I cannot find the solution