I received following error after submitting the assignment. However, in collab I didn’t get any error. Please help me understand it. My output layer has 3 units and I have used softmax activation function and sparse_categorical_crossentroy loss function.
All tests passed for parse_data_from_input!
All tests passed for train_val_generators!
Details of failed tests for create_model
Failed test case: your model could not be used for inference. Details shown in ‘got’ value below:.
Expected:
no exceptions,
but got:
Received a label value of 24 which is outside the valid range of [0, 3). Label values: 23 17 15 16 13 4 8 23 4 20 18 15 15 20 22 20 19 10 10 2 11 2 15 0 2 0 10 1 12 22 19 20 21 4 6 7 5 1 21 7 2 3 8 0 13 15 24 8 21 13 4 22 7 23 5 2 3 5 2 12 21 17 22 1 13 4 18 11 14 7 10 15 7 2 7 10 7 21 17 5 0 14 3 14 19 7 14 8 3 21 0 16 4 7 4 6 21 3 8 7
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits
(defined at /opt/conda/lib/python3.7/site-packages/keras/backend.py:5114)
]] [Op:__inference_test_function_501]
Errors may have originated from an input operation.
Input Source operations connected to node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits:
In[0] sparse_categorical_crossentropy/Reshape_1 (defined at /opt/conda/lib/python3.7/site-packages/keras/backend.py:5109)
In[1] sparse_categorical_crossentropy/Reshape (defined at /opt/conda/lib/python3.7/site-packages/keras/backend.py:3561)
Operation defined at: (most recent call last)
File “entry.py”, line 56, in
main()File “entry.py”, line 52, in main
grade_notebook(config)File “entry.py”, line 40, in grade_notebook
failed_partids = grade_func()File “/grader/grader.py”, line 302, in grade
cases, num_cases = g()File “/grader/grader.py”, line 282, in grade
return g_func(learner_model)File “/grader/grader.py”, line 247, in g_func
model.evaluate(train_generator)File “/opt/conda/lib/python3.7/site-packages/keras/utils/traceback_utils.py”, line 64, in error_handler
return fn(*args, **kwargs)File “/opt/conda/lib/python3.7/site-packages/keras/engine/training.py”, line 1537, in evaluate
tmp_logs = self.test_function(iterator)File “/opt/conda/lib/python3.7/site-packages/keras/engine/training.py”, line 1366, in test_function
return step_function(self, iterator)File “/opt/conda/lib/python3.7/site-packages/keras/engine/training.py”, line 1356, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))File “/opt/conda/lib/python3.7/site-packages/keras/engine/training.py”, line 1349, in run_step
outputs = model.test_step(data)File “/opt/conda/lib/python3.7/site-packages/keras/engine/training.py”, line 1306, in test_step
y, y_pred, sample_weight, regularization_losses=self.losses)File “/opt/conda/lib/python3.7/site-packages/keras/engine/compile_utils.py”, line 201, in call
loss_value = loss_obj(y_t, y_p, sample_weight=sw)File “/opt/conda/lib/python3.7/site-packages/keras/losses.py”, line 141, in call
losses = call_fn(y_true, y_pred)File “/opt/conda/lib/python3.7/site-packages/keras/losses.py”, line 245, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)File “/opt/conda/lib/python3.7/site-packages/keras/losses.py”, line 1738, in sparse_categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)File “/opt/conda/lib/python3.7/site-packages/keras/backend.py”, line 5114, in sparse_categorical_crossentropy
labels=target, logits=output)
.