I am working on training a neural network using keras and keras_tuner for hyper parameter tuning and ran into an error that isn't similar to other questions asked in the website before.
Here's the code that is relevant according to my knowledge:
x = data.drop('label', axis=1).values
X = normalize(x)
y = pd.get_dummies(data['label']).values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=40)
print("Length of train set: ", X_train.shape, "y:", y_train.shape)
print("Length of test set: ", X_test.shape, "y:", y_test.shape)
which resulted like this:
Length of train set: (7922, 26) y: (7922, 6)
Length of test set: (3396, 26) y: (3396, 6)
def build_model(hp):
model = Sequential()
step = 100
for i in range(hp.Int('num_layers', 3, 4)):
if i == 1:
model.add(
Dense(
units=hp.Int('units_'+str(i), min_value=step*2, max_value=step*5, default=step*2, step=step),
input_dim=26,
activation=hp.Choice('dense_activation_'+str(i), values=['relu', 'tanh', 'sigmoid'])
)
)
else:
model.add(
Dense(units=hp.Int('units_' + str(i), min_value=step*2, max_value=step*5, default=step*2, step=step),
activation=hp.Choice('dense_activation_'+str(i), values=['relu', 'tanh', 'sigmoid'])
)
)
model.add(
Dropout(
hp.Float('dropout_' + str(i), min_value=0.0, max_value=0.1, default=0.1, step=0.02)
)
)
if step > 20:
step = step/2
else:
step = 10
model.add(Dense(6, activation='softmax'))
# optimizer = optimizers.Adam(learning_rate=0.001)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
tuner = kt.Hyperband(
build_model,
max_epochs=100,
objective='accuracy',
seed=42,
executions_per_trial=2
)
tuner.search(X_train, y_train, epochs=500, validation_data=(X_test, y_test))
best_model = tuner.get_best_models()[0]
and Finally after all that i got an error which is as shown:
Traceback (most recent call last):
File "PycharmProjects\FYP_Project\Keras training\Keras Training.py", line 75, in <module>
best_model.evaluate(X_test, y_test)[1] * 100)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\training.py", line 1489, in evaluate
tmp_logs = self.test_function(iterator)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\def_function.py", line 763, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\function.py", line 3050, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\function.py", line 3444, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\function.py", line 3279, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\framework\func_graph.py", line 999, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\def_function.py", line 672, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "AppData\Roaming\Python\Python39\site-packages\tensorflow\python\framework\func_graph.py", line 986, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\training.py:1323 test_function *
return step_function(self, iterator)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\training.py:1314 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\distribute\distribute_lib.py:1285 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\distribute\distribute_lib.py:2833 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\distribute\distribute_lib.py:3608 _call_for_each_replica
return fn(*args, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\training.py:1307 run_step **
outputs = model.test_step(data)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\training.py:1266 test_step
y_pred = self(x, training=False)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\base_layer.py:1030 __call__
outputs = call_fn(inputs, *args, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\sequential.py:394 call
outputs = layer(inputs, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\base_layer.py:1023 __call__
self._maybe_build(inputs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\base_layer.py:2625 _maybe_build
self.build(input_shapes) # pylint:disable=not-callable
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\layers\core.py:1191 build
self.kernel = self.add_weight(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\base_layer.py:639 add_weight
variable = self._add_variable_with_custom_getter(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\training\tracking\base.py:810 _add_variable_with_custom_getter
new_variable = getter(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\keras\engine\base_layer_utils.py:127 make_variable
return tf_variables.VariableV1(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:260 __call__
return cls._variable_v1_call(*args, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:206 _variable_v1_call
return previous_getter(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\distribute\distribute_lib.py:3523 creator
return next_creator(**kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
C:\Users\Axell\AppData\Roaming\Python\Python39\site-packages\tensorflow\python\distribute\distribute_lib.py:3523 creator
return next_creator(**kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\distribute\distribute_lib.py:3523 creator
return next_creator(**kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\def_function.py:750 variable_capturing_scope
v = UnliftedInitializerVariable(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\ops\variables.py:264 __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\eager\def_function.py:293 __init__
initial_value = initial_value()
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\training\tracking\base.py:86 __call__
return CheckpointInitialValue(
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\training\tracking\base.py:122 __init__
self.wrapped_value.set_shape(shape)
AppData\Roaming\Python\Python39\site-packages\tensorflow\python\framework\ops.py:1238 set_shape
raise ValueError(
ValueError: Tensor's shape (26, 400) is not compatible with supplied shape [26, 200]
I'm not even sure where the 400 came from, and i realized that i have a zero on one of my layer, but when i remove that, the error still stayed the same. the new changes i've made are:
def build_model(hp):
model = Sequential()
for i in range(hp.Int('num_layers', 4, 5)):
# if i == 1:
# model.add(Dense(units=hp.Int('units_' + str(i),
# min_value=20,
# max_value=500,
# step=20),
# input_dim=26,
# activation='relu'))
# else:
model.add(Dense(units=hp.Int('units_' + str(i),
min_value=20,
max_value=500,
step=20),
activation='relu'))
model.add(Dense(6, activation='softmax'))
# optimizer = optimizers.Adam(learning_rate=0.001)
model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])), metrics=['accuracy'])
return model
Still had the 400 from somewhere, can someone enlighten me of where it comes from?
I think there is bug with Keras tuner, I found the similar issue when getting the best model as below:
models = tuner.get_best_models(num_models=2)
best_model = models[0]
best_model.build(input_shape=(INPUT_FEATURE_SIZE, ))
This problem can be solved by using get_best_hyperparameters() method as below:
best_hp = tuner.get_best_hyperparameters()[0]
best_model = tuner.hypermodel.build(best_hp)
print("Best Model summary is as below: ")
best_model.summary()