hypertorch
HyperTorch
¶
Hyperparameter Tuning for Torch.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
seed |
int
|
seed for random number generator. See Numpy Random Sampling |
126
|
log_level |
int
|
log level for logger. Default is 50. |
50
|
Attributes:
Name | Type | Description |
---|---|---|
seed |
int
|
seed for random number generator. |
rng |
Generator
|
random number generator. |
fun_control |
dict
|
dictionary containing control parameters for the function. |
log_level |
int
|
log level for logger. |
Source code in spotpython/fun/hypertorch.py
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|
check_X_shape(X)
¶
Check the shape of the input array X.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
ndarray
|
input array. |
required |
Raises:
Type | Description |
---|---|
Exception
|
if the second dimension of X does not match the length of var_name in fun_control. |
Examples:
>>> from spotpython.fun.hypertorch import HyperTorch
>>> import numpy as np
>>> hyper_torch = HyperTorch(seed=126, log_level=50)
>>> hyper_torch.fun_control["var_name"] = ["x1", "x2"]
>>> hyper_torch.check_X_shape(np.array([[1, 2], [3, 4]]))
>>> hyper_torch.check_X_shape(np.array([1, 2]))
Traceback (most recent call last):
...
Exception
Source code in spotpython/fun/hypertorch.py
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|
fun_torch(X, fun_control=None)
¶
Function to be optimized.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X |
ndarray
|
input array. |
required |
fun_control |
dict
|
dictionary containing control parameters for the function. |
None
|
Returns: np.ndarray: output array.
Examples:
>>> from spotpython.fun.hypertorch import HyperTorch
>>> import numpy as np
>>> hyper_torch = HyperTorch(seed=126, log_level=50)
>>> hyper_torch.fun_control["var_name"] = ["x1", "x2"]
>>> hyper_torch.fun_torch(np.array([[1, 2], [3, 4]]))
Source code in spotpython/fun/hypertorch.py
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