---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[28], line 1
----> 1 history = model.fit(train_dataset,
2 epochs=25,
3 validation_data = test_dataset,
4 validation_steps=1)
File ~/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:777, in Model.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)
774 self._check_call_args('fit')
776 func = self._select_training_loop(x)
--> 777 return func.fit(
778 self,
779 x=x,
780 y=y,
781 batch_size=batch_size,
782 epochs=epochs,
783 verbose=verbose,
784 callbacks=callbacks,
785 validation_split=validation_split,
786 validation_data=validation_data,
787 shuffle=shuffle,
788 class_weight=class_weight,
789 sample_weight=sample_weight,
790 initial_epoch=initial_epoch,
791 steps_per_epoch=steps_per_epoch,
792 validation_steps=validation_steps,
793 validation_freq=validation_freq,
794 max_queue_size=max_queue_size,
795 workers=workers,
796 use_multiprocessing=use_multiprocessing)
File ~/.local/lib/python3.10/site-packages/keras/engine/training_arrays_v1.py:616, in ArrayLikeTrainingLoop.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)
595 def fit(self,
596 model,
597 x=None,
(...)
611 validation_freq=1,
612 **kwargs):
613 batch_size = model._validate_or_infer_batch_size(batch_size,
614 steps_per_epoch, x)
--> 616 x, y, sample_weights = model._standardize_user_data(
617 x,
618 y,
619 sample_weight=sample_weight,
620 class_weight=class_weight,
621 batch_size=batch_size,
622 check_steps=True,
623 steps_name='steps_per_epoch',
624 steps=steps_per_epoch,
625 validation_split=validation_split,
626 shuffle=shuffle)
628 if validation_data:
629 val_x, val_y, val_sample_weights = model._prepare_validation_data(
630 validation_data, batch_size, validation_steps)
File ~/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:2318, in Model._standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
2316 is_compile_called = False
2317 if not self._is_compiled and self.optimizer:
-> 2318 self._compile_from_inputs(all_inputs, y_input, x, y)
2319 is_compile_called = True
2321 # In graph mode, if we had just set inputs and targets as symbolic tensors
2322 # by invoking build and compile on the model respectively, we do not have to
2323 # feed anything to the model. Model already has input and target data as
(...)
2327
2328 # self.run_eagerly is not free to compute, so we want to reuse the value.
File ~/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:2568, in Model._compile_from_inputs(self, all_inputs, target, orig_inputs, orig_target)
2565 else:
2566 target_tensors = None
-> 2568 self.compile(
2569 optimizer=self.optimizer,
2570 loss=self.loss,
2571 metrics=self._compile_metrics,
2572 weighted_metrics=self._compile_weighted_metrics,
2573 loss_weights=self.loss_weights,
2574 target_tensors=target_tensors,
2575 sample_weight_mode=self.sample_weight_mode,
2576 run_eagerly=self.run_eagerly,
2577 experimental_run_tf_function=self._experimental_run_tf_function)
File ~/.local/lib/python3.10/site-packages/tensorflow/python/training/tracking/base.py:629, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
627 self._self_setattr_tracking = False # pylint: disable=protected-access
628 try:
--> 629 result = method(self, *args, **kwargs)
630 finally:
631 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
File ~/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:443, in Model.compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
439 training_utils_v1.prepare_sample_weight_modes(
440 self._training_endpoints, sample_weight_mode)
442 # Creates the model loss and weighted metrics sub-graphs.
--> 443 self._compile_weights_loss_and_weighted_metrics()
445 # Functions for train, test and predict will
446 # be compiled lazily when required.
447 # This saves time when the user is not using all functions.
448 self.train_function = None
File ~/.local/lib/python3.10/site-packages/tensorflow/python/training/tracking/base.py:629, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
627 self._self_setattr_tracking = False # pylint: disable=protected-access
628 try:
--> 629 result = method(self, *args, **kwargs)
630 finally:
631 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
File ~/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:1537, in Model._compile_weights_loss_and_weighted_metrics(self, sample_weights)
1524 self._handle_metrics(
1525 self.outputs,
1526 targets=self._targets,
(...)
1529 masks=masks,
1530 return_weighted_metrics=True)
1532 # Compute total loss.
1533 # Used to keep track of the total loss value (stateless).
1534 # eg., total_loss = loss_weight_1 * output_1_loss_fn(...) +
1535 # loss_weight_2 * output_2_loss_fn(...) +
1536 # layer losses.
-> 1537 self.total_loss = self._prepare_total_loss(masks)
File ~/.local/lib/python3.10/site-packages/keras/engine/training_v1.py:1597, in Model._prepare_total_loss(self, masks)
1594 sample_weight *= mask
1596 if hasattr(loss_fn, 'reduction'):
-> 1597 per_sample_losses = loss_fn.call(y_true, y_pred)
1598 weighted_losses = losses_utils.compute_weighted_loss(
1599 per_sample_losses,
1600 sample_weight=sample_weight,
1601 reduction=losses_utils.ReductionV2.NONE)
1602 loss_reduction = loss_fn.reduction
File ~/.local/lib/python3.10/site-packages/keras/losses.py:245, in LossFunctionWrapper.call(self, y_true, y_pred)
242 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(y_pred, y_true)
244 ag_fn = tf.__internal__.autograph.tf_convert(self.fn, tf.__internal__.autograph.control_status_ctx())
--> 245 return ag_fn(y_true, y_pred, **self._fn_kwargs)
File ~/.local/lib/python3.10/site-packages/tensorflow/python/autograph/impl/api.py:692, in convert.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
690 except Exception as e: # pylint:disable=broad-except
691 if hasattr(e, 'ag_error_metadata'):
--> 692 raise e.ag_error_metadata.to_exception(e)
693 else:
694 raise
ValueError: in user code:
File "/tmp/ipykernel_49162/810674056.py", line 8, in Loss *
loss += confidenceLoss(y[:,:,:-4],tf.cast(gt[:,:,0],tf.int32))
File "/tmp/ipykernel_49162/2037607510.py", line 2, in confidenceLoss *
unweighted_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(label, y)
ValueError: Only call sparse_softmax_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...). Received unnamed argument: Tensor("loss/output_1_loss/Cast:0", shape=(None, None), dtype=int32) |
