InvalidArgumentError Traceback (most recent call last) - graph

Question

I've created a simple CNN with two main labels. The imput_shape was changed from 300 to 224 and the batch_size is 128. What am I supposed to do?
After entering this code:
history = model.fit(
train_ds,
validation_data=val_ds,
epochs = 20,
verbose = 2)
I got following error message:
Epoch 1/20
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
Input In [63], in <cell line: 1>()
----> 1 history = model.fit(
2 train_ds,
3 validation_data=val_ds,
4 epochs = 20,
5 verbose = 2)
File ~\AppData\Roaming\Python\Python39\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~\AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\execute.py:52, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
50 try:
51 ctx.ensure_initialized()
---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
53 inputs, attrs, num_outputs)
54 except core._NotOkStatusException as e:
55 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' defined at (most recent call last):
File "C:\Anaconda3\lib\runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\Anaconda3\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
app.start()
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
self.io_loop.start()
File "C:\Anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
self.asyncio_loop.run_forever()
File "C:\Anaconda3\lib\asyncio\base_events.py", line 601, in run_forever
self._run_once()
File "C:\Anaconda3\lib\asyncio\base_events.py", line 1905, in _run_once
handle._run()
File "C:\Anaconda3\lib\asyncio\events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 471, in dispatch_queue
await self.process_one()
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 460, in process_one
await dispatch(*args)
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 367, in dispatch_shell
await result
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 662, in execute_request
reply_content = await reply_content
File "C:\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 360, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 532, in run_cell
return super().run_cell(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2863, in run_cell
result = self._run_cell(
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2909, in _run_cell
return runner(coro)
File "C:\Anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
coro.send(None)
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3106, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3309, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3369, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "C:\Users\local_sms92\Temp\ipykernel_43944\602300899.py", line 1, in <cell line: 1>
history = model.fit(
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1249, in train_function
return step_function(self, iterator)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1222, in run_step
outputs = model.train_step(data)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1024, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\training.py", line 1082, in compute_loss
return self.compiled_loss(
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\engine\compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\losses.py", line 152, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\losses.py", line 284, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\losses.py", line 2098, in sparse_categorical_crossentropy
return backend.sparse_categorical_crossentropy(
File "C:\Users\sms92\AppData\Roaming\Python\Python39\site-packages\keras\backend.py", line 5633, in sparse_categorical_crossentropy
res = tf.nn.sparse_softmax_cross_entropy_with_logits(
Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
Received a label value of 2 which is outside the valid range of [0, 2). Label values: 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 1 2 1 1 2 2 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 1 2 2 1 2 2 1 2 2 1 1 2 1 1 2 2 2 1
[[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_38939]
(I've already tried to modify the imput_shape to 224, as recommended on other platforms, but this didn't work)

Answer 1

I think you need to change your output label to 0 & 1, instead of 1 & 2, since the error message shows that:
Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'
Received a label value of 2 which is outside the valid range of [0, 2).