set_dl_classifier_param — Set the parameters of a deep-learning-based classifier.
set_dl_classifier_param is obsolete and is only provided for
reasons of backward compatibility. New applications should use the common
CNN-based operator set_dl_model_param instead.
set_dl_classifier_param( : : DLClassifierHandle, GenParamName, GenParamValue : )
set_dl_classifier_param sets the parameters and hyperparameters
GenParamName of the neural network DLClassifierHandle
to the values GenParamValue.
The pretrained classifiers are trained for their default image dimensions,
see read_dl_classifier.
The network architectures allow different image dimensions. But for networks with at least one fully connected layer such a change makes a retraining necessary. Networks without fully connected layers are directly applicable to different image sizes. However, images with a size differing from the size with which the classifier has been trained, are likely to show a reduced classification accuracy.
GenParamName can attain the following values:
train_dl_classifier_batch for further details. The parameter
'batch_size' is stored in the pretrained classifier. Per default,
the 'batch_size' is set such that a training of the pretrained
classifier with up to 100 classes can be easily performed on a
device with 8 gigabyte of memory.
For the pretrained classifiers, the default values are hence given as
follows:
| pretrained classifier | default value of 'batch_size' |
|---|---|
| 'pretrained_dl_classifier_compact.hdl' | 160 |
| 'pretrained_dl_classifier_enhanced.hdl' | 96 |
| 'pretrained_dl_classifier_resnet18.hdl' | 24 |
| 'pretrained_dl_classifier_resnet50.hdl' | 23 |
apply_dl_classifier for details on how to set this parameter
for greater efficiency.
Multiplier for
'batch_size' to enable training with larger numbers of images
in one step which would otherwise not be possible due to GPU memory
limitations. For detailed information see
train_dl_classifier_batch. This model parameter does not have
any impact during evaluation and inference. For the pretrained
classifiers, the default value of 'batch_size_multiplier' is
set to 1.
Tuple of labels corresponding to the classes of objects which are to be recognized. The order of the class names remains unchanged after the setting.
Identifier of the GPU where the training and
inference operators (train_dl_classifier_batch and
apply_dl_classifier) are executed. Per default, the first
available GPU is used. get_system with 'cuda_devices' can
be used to retrieve a list of available GPUs. Pass the index in this list
to 'gpu'.
Width of the images the network will
process. The default value is given by the network, see
read_dl_classifier.
Height of the images the network will
process. The default value is given by the network, see
read_dl_classifier.
Number of channels of the images the
network will process. Possible are one channel (gray value image),
or three channels (three-channel image).
The default value is given by the network, see
read_dl_classifier.
Changing to a single channel image modifies the network configuration.
This process removes the color information contained in certain layers and
is not invertible.
Tuple containing the image dimensions
'image_width', 'image_height', and number of channels
'image_num_channels'.
The default values are given by the network,
see read_dl_classifier.
Concerning the number of channels, the values one (gray value image),
or three (three-channel image) are possible.
Changing to a single channel image modifies the network configuration.
This process removes the color information contained in certain layers and
is not invertible.
Initial value
of the factor determining the gradient influence during training.
Please refer to train_dl_classifier_batch for further details.
The default value depends on the classifier.
When updating the arguments of the loss function,
the hyperparameter 'momentum' specifies to which extent
previous updating vectors will be added to the current updating vector.
Please refer to train_dl_classifier_batch for further details.
Per default, the 'momentum' is set to 0.9.
Defines the device on which the operators will be executed. Per default, the 'runtime' is set to 'gpu'.
The operator apply_dl_classifier will be executed on the CPU,
whereas the operator train_dl_classifier_batch is not
executable.
In case the GPU has been used before, CPU memory is initialized, and if necessary values stored on the GPU memory are moved to the CPU memory.
On Intel or AMD architectures the 'cpu' runtime uses OpenMP
for the parallelization of apply_dl_classifier, where per
default, all threads available to the OpenMP runtime are used. You may
use the set_system parameter 'thread_num' to specify
the number of threads.
On Arm architectures the 'cpu' runtime uses a global thread
pool. You may specify the number of threads with the
set_system parameter 'thread_num'.
For both architectures mentioned above, it is not possible
to specify a thread-specific number of threads (via the parameter
'tsp_thread_num' of the operator set_system).
The GPU memory is initialized and the corresponding handle created.
The operators apply_dl_classifier and
train_dl_classifier_batch will be executed on the GPU.
For the specific requirements please refer to the HALCON
“Installation Guide”.
If called with 'immediately', the GPU memory is initialized and
the corresponding handle created. Otherwise this is done on demand,
which may result in significantly larger execution times for the first
call of apply_dl_classifier or
train_dl_classifier_batch.
If 'gpu' or 'batch_size' is changed with
subsequent calls of set_dl_classifier_param, the GPU memory is
reinitialized.
Note, this parameter has no effect if running on CPUs, thus if 'runtime' is set to 'cpu'.
Regularization parameter used for regularization of the loss
function. Regularization is helpful in the presence of overfitting during
the classifier training. If the hyperparameter 'weight_prior' is
non-zero, the regularization term given below is added to the loss
function (see also train_dl_classifier_batch)
Here the index k runs over all weights of the network, except for the
biases which are not regularized. The regularization term
generally penalizes large weights, thus
pushing the weights towards zero, which effectively reduces the complexity
of the model.
Simply put: Regularization favors simpler models that are less likely to
learn noise in the data and generalize better.
In case the classifier overfits the data, it is strongly
recommended to try different values for the parameter
'weight_prior' to improve the generalization properties of the
neural network. Choosing its value is a trade-off between the models
ability to generalize, overfitting, and underfitting.
If is too small, the model might overfit, if its too
large the model might loose its ability to fit the data, because all
weights are effectively zero. For finding an ideal value for
, we recommend a cross-validation, i.e. to perform the
training for a range of values and choose the value that results in the
best validation error. For typical applications, we recommend testing the
values for 'weight_prior' on a logarithmic scale between
. If the training takes a very
long time, one might consider performing the hyperparameter optimization
on a reduced amount of data.
The default value depends on the classifier.
For an explanation of the concept of deep-learning-based classification see the introduction of chapter Deep Learning / Classification. The workflow involving this legacy operator is described in the chapter Legacy / DL Classification.
To successfully set 'gpu' parameters, cuDNN and cuBLAS are
required, i.e., to set the parameter GenParamName
'runtime' to 'gpu' or to set the GenParamName
'gpu'.
For further details, please refer to the “Installation Guide”,
paragraph “Requirements for Deep Learning and Deep-Learning-Based Methods”.
DLClassifierHandle (input_control) dl_classifier → (handle)
Handle of the deep-learning-based classifier.
GenParamName (input_control) attribute.name(-array) → (string)
Name of the generic parameter.
Default: 'classes'
List of values: 'batch_size', 'batch_size_multiplier', 'classes', 'gpu', 'image_dimensions', 'image_height', 'image_num_channels', 'image_width', 'learning_rate', 'momentum', 'runtime', 'runtime_init', 'weight_prior'
GenParamValue (input_control) attribute.value(-array) → (string / real / integer)
Value of the generic parameter.
Default: ['class_1','class_2','class_3']
Suggested values: 1, 2, 3, 50, 0.001, 'cpu', 'gpu', 'immediately'
If the parameters are valid, the operator
set_dl_classifier_param returns the value 2 (
H_MSG_TRUE)
. If
necessary, an exception is raised.
get_dl_classifier_param,
apply_dl_classifier,
train_dl_classifier_batch
Deep Learning Inference