InvalidArgumentError when building sequential model using Keras - machine-learning

Question

I want to develop a sequential model using optimal hyperparameters derived from Keras Tuner. My code raised "Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:]" error.
I tried adding +1 or +10 to the entire X_train but still get the same error.
Build the sequential model:
def model_builder(hp):
model = Sequential()
# Tune the number of units in the first Dense layer
# Choose an optimal value between 32-512
hp_units1 = hp.Int('units1', min_value=32, max_value=512, step=32)
hp_units2 = hp.Int('units2', min_value=32, max_value=512, step=32)
hp_units3 = hp.Int('units3', min_value=32, max_value=512, step=32)
model.add(Dense(units=hp_units1, activation='relu'))
model.add(tf.keras.layers.Dense(units=hp_units2, activation='relu'))
model.add(tf.keras.layers.Dense(units=hp_units3, activation='relu'))
model.add(Dense(1, kernel_initializer='normal', activation='linear')) # output layer
# Tune the learning rate for the optimizer
# Choose an optimal value from 0.01, 0.001, or 0.0001
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
# Performance visualization callback
performance_viz_cbk = PerformanceVisualizationCallback(
model=model,
test_data=X_test,
image_dir='c:\perorfmance_charts')
# Model summary
model.compile(optimizer=Adam(learning_rate=hp_learning_rate),
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy', tf.keras.metrics.AUC(), MulticlassTruePositives()])
return model
Hyperparameter tuning
tuner = kt.Hyperband(model_builder,
objective='val_accuracy',
max_epochs=10,
factor=3,
directory='my_dir',
project_name='intro_to_kt')
tuner.search(X_train, y_train, epochs=10, validation_split=0.2)
Traceback:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/tmp/ipykernel_17/1976310635.py in <module>
----> 1 tuner.search(X_train, y_train, epochs=10, validation_split=0.2)
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
177
178 self.on_trial_begin(trial)
--> 179 results = self.run_trial(trial, *fit_args, **fit_kwargs)
180 # `results` is None indicates user updated oracle in `run_trial()`.
181 if results is None:
/opt/conda/lib/python3.7/site-packages/keras_tuner/tuners/hyperband.py in run_trial(self, trial, *fit_args, **fit_kwargs)
382 fit_kwargs["epochs"] = hp.values["tuner/epochs"]
383 fit_kwargs["initial_epoch"] = hp.values["tuner/initial_epoch"]
--> 384 return super(Hyperband, self).run_trial(trial, *fit_args, **fit_kwargs)
385
386 def _build_model(self, hp):
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
292 callbacks.append(model_checkpoint)
293 copied_kwargs["callbacks"] = callbacks
--> 294 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
295
296 histories.append(obj_value)
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py in _build_and_fit_model(self, trial, *args, **kwargs)
220 hp = trial.hyperparameters
221 model = self._try_build(hp)
--> 222 results = self.hypermodel.fit(hp, model, *args, **kwargs)
223 return tuner_utils.convert_to_metrics_dict(
224 results, self.oracle.objective, "HyperModel.fit()"
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/hypermodel.py in fit(self, hp, model, *args, **kwargs)
135 If return a float, it should be the `objective` value.
136 """
--> 137 return model.fit(*args, **kwargs)
138
139
/opt/conda/lib/python3.7/site-packages/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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
948 # Lifting succeeded, so variables are initialized and we can run the
949 # stateless function.
--> 950 return self._stateless_fn(*args, **kwds)
951 else:
952 _, _, _, filtered_flat_args = \
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
-> 3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
3041
3042 #property
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1962 # No tape is watching; skip to running the function.
1963 return self._build_call_outputs(self._inference_function.call(
-> 1964 ctx, args, cancellation_manager=cancellation_manager))
1965 forward_backward = self._select_forward_and_backward_functions(
1966 args,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
594 inputs=args,
595 attrs=attrs,
--> 596 ctx=ctx)
597 else:
598 outputs = execute.execute_with_cancellation(
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential/dense_3/BiasAdd:0) = ] [[-1.17921221][-1.64039445][-1.71617472]...] [y (Cast_8/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/else/_1/assert_greater_equal/Assert/AssertGuard/Assert}}]]
(1) Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential/dense_3/BiasAdd:0) = ] [[-1.17921221][-1.64039445][-1.71617472]...] [y (Cast_8/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/else/_1/assert_greater_equal/Assert/AssertGuard/Assert}}]]
[[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_41]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_7509]
Function call stack:
train_function -> train_function
Sample data:
X_train
array([[7.97469882, 3.5471509 , 5.67233186, ..., 5.40341065, 3.16356072,
4.62657322],
[4.5390077 , 5.19247562, 5.21946796, ..., 4.12139197, 5.62828788,
4.31792717],
[5.83576307, 6.42946189, 4.85759085, ..., 5.26563348, 5.48022682,
5.29933021],
[4.58240518, 5.02457724, 6.1146384 , ..., 5.47847468, 6.62666587,
5.77328054],
[4.51602713, 5.17875946, 4.57102455, ..., 4.15622882, 5.48888824,
4.88704 ]])
y_train
array([[1],
[3],
[1],
[0],
[3]])