I am trying to optimize the hyperparameters of a LSTM with Bayesian Optimization. But I received the error message TypeError: only integer scalar arrays can be converted to a scalar index when I run the code. A solution I found is to convert the training data and validation data into arrays, but in my code they are already arrays not lists. Or convert them into tuples but I cannot see how I would do this
X_train shape: (946, 60, 1)
y_train shape: (946,)
X_val shape: (192, 60, 1)
y_val shape: (192,)
def build(hp):
activation = hp.Choice('activation',
[
'relu',
'tanh',
'linear',
'selu',
'elu'
])
num_rnn_layers = hp.Int(
'num_rnn_layers',
min_value=0,
max_value=12,
default=3)
recurrent_dropout = hp.Float(
'recurrent_dropout',
min_value=0.0,
max_value=0.99,
default=0.2)
num_units = hp.Int(
'num_units',
min_value=0,
max_value=64,
default=32)
model = Sequential()
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
model.add(Dense(1))
model.compile(loss='mse', metrics=['mse'], optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])))
model.compile(
optimizer=keras.optimizers.Adam(
hp.Float(
'learning_rate',
min_value=1e-10,
max_value=1e-2,
sampling='LOG',
default=1e-6
),
),
loss=tf.losses.MeanSquaredError(),
metrics=[tf.metrics.MeanAbsoluteError()]
)
return model
bayesian_opt_tuner = BayesianOptimization(
build,
objective='mse',
max_trials=3,
executions_per_trial=1,
directory=os.path.normpath('C:/keras_tuning'),
project_name='kerastuner_bayesian_poc',
overwrite=True)
n_epochs=100
bayesian_opt_tuner.search(X_train, y_train,epochs=n_epochs,
validation_data=(X_val, y_val),
validation_split=0.2,verbose=1)
bayes_opt_model_best_model = bayesian_opt_tuner.get_best_models(num_models=1)
model = bayes_opt_model_best_model[0]
Error log:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 105, in build
model = self.hypermodel.build(hp)
File "<ipython-input-80-00452994e0d6>", line 33, in build
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py", line 204, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 309, in Input
input_layer = InputLayer(**input_layer_config)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 1247, in placeholder
shape=shape, dtype=dtype, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py", line 51, in __init__
self._shape = tensor_shape.TensorShape(shape)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in __init__
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in <listcomp>
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 201, in __init__
self._value = int(value.__index__())
TypeError: only integer scalar arrays can be converted to a scalar index
[Warning] Invalid model 0/5
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 105, in build
model = self.hypermodel.build(hp)
File "<ipython-input-80-00452994e0d6>", line 33, in build
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py", line 204, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 309, in Input
input_layer = InputLayer(**input_layer_config)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 1247, in placeholder
shape=shape, dtype=dtype, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py", line 51, in __init__
self._shape = tensor_shape.TensorShape(shape)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in __init__
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in <listcomp>
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 201, in __init__
self._value = int(value.__index__())
TypeError: only integer scalar arrays can be converted to a scalar index
[Warning] Invalid model 1/5
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 105, in build
model = self.hypermodel.build(hp)
File "<ipython-input-80-00452994e0d6>", line 33, in build
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py", line 204, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 309, in Input
input_layer = InputLayer(**input_layer_config)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 1247, in placeholder
shape=shape, dtype=dtype, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py", line 51, in __init__
self._shape = tensor_shape.TensorShape(shape)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in __init__
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in <listcomp>
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 201, in __init__
self._value = int(value.__index__())
TypeError: only integer scalar arrays can be converted to a scalar index
[Warning] Invalid model 2/5
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 105, in build
model = self.hypermodel.build(hp)
File "<ipython-input-80-00452994e0d6>", line 33, in build
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py", line 204, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 309, in Input
input_layer = InputLayer(**input_layer_config)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 1247, in placeholder
shape=shape, dtype=dtype, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py", line 51, in __init__
self._shape = tensor_shape.TensorShape(shape)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in __init__
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in <listcomp>
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 201, in __init__
self._value = int(value.__index__())
TypeError: only integer scalar arrays can be converted to a scalar index
[Warning] Invalid model 3/5
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 105, in build
model = self.hypermodel.build(hp)
File "<ipython-input-80-00452994e0d6>", line 33, in build
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py", line 204, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 309, in Input
input_layer = InputLayer(**input_layer_config)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 1247, in placeholder
shape=shape, dtype=dtype, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py", line 51, in __init__
self._shape = tensor_shape.TensorShape(shape)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in __init__
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in <listcomp>
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 201, in __init__
self._value = int(value.__index__())
TypeError: only integer scalar arrays can be converted to a scalar index
[Warning] Invalid model 4/5
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py", line 105, in build
model = self.hypermodel.build(hp)
File "<ipython-input-80-00452994e0d6>", line 33, in build
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py", line 517, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py", line 204, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 309, in Input
input_layer = InputLayer(**input_layer_config)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py", line 1247, in placeholder
shape=shape, dtype=dtype, name=name)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py", line 51, in __init__
self._shape = tensor_shape.TensorShape(shape)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in __init__
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 758, in <listcomp>
self._dims = [Dimension(d) for d in dims]
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 201, in __init__
self._value = int(value.__index__())
TypeError: only integer scalar arrays can be converted to a scalar index
[Warning] Invalid model 5/5
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py in build(self, hp)
104 with maybe_distribute(self.distribution_strategy):
--> 105 model = self.hypermodel.build(hp)
106 except:
17 frames
TypeError: only integer scalar arrays can be converted to a scalar index
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/kerastuner/engine/hypermodel.py in build(self, hp)
113 if i == self._max_fail_streak:
114 raise RuntimeError(
--> 115 'Too many failed attempts to build model.')
116 continue
117
RuntimeError: Too many failed attempts to build model.
Your code should look like:
def build(hp):
activation = hp.Choice('activation',
[
'relu',
'tanh',
'linear',
'selu',
'elu'
])
num_rnn_layers = hp.Int(
'num_rnn_layers',
min_value=0,
max_value=12,
default=3)
recurrent_dropout = hp.Float(
'recurrent_dropout',
min_value=0.0,
max_value=0.99,
default=0.2)
num_units = hp.Int(
'num_units',
min_value=0,
max_value=64,
default=32)
model = Sequential()
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train.shape[1], 1)))
model.add(Dense(1))
model.compile(loss='mse', metrics=['mse'], optimizer=keras.optimizers.Adam(
hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])))
model.compile(
optimizer=keras.optimizers.Adam(
hp.Float(
'learning_rate',
min_value=1e-10,
max_value=1e-2,
sampling='LOG',
default=1e-6
),
),
loss=tf.losses.MeanSquaredError(),
metrics=[tf.metrics.MeanAbsoluteError()]
)
return model
bayesian_opt_tuner = BayesianOptimization(
build,
objective='mse',
max_trials=3,
executions_per_trial=1,
directory=os.path.normpath('C:/keras_tuning'),
project_name='kerastuner_bayesian_poc',
overwrite=True)
n_epochs=100
bayesian_opt_tuner.search(X_train, y_train,epochs=n_epochs,
validation_data=(X_val, y_val),
validation_split=0.2,verbose=1)
bayes_opt_model_best_model = bayesian_opt_tuner.get_best_models(num_models=1)
model = bayes_opt_model_best_model[0]
This line is causing the problem I think:
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train[1], 1)))
Change it to this:
model.add(LSTM(units=num_units, activation=activation, recurrent_dropout = recurrent_dropout,input_shape=(X_train.shape[1], 1)))