set_dl_classifier_param — Set the parameters of the deep-learning-based classifier.
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:
Number of images (and corresponding labels) in a batch and thus the number of images that are processed simultaneously in a single iteration of the training. Please refer to train_dl_classifier_batch for further details. This parameter 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'|
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_hight', 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. Per default, the 'learning_rate' is set to 0.001.
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 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.
The 'cpu' runtime uses OpenMP for the parallelization of apply_dl_classifier. Per default, all threads available to the OpenMP runtime are used. Use the thread specific set_system parameter 'tsp_thread_num' to specify the number of threads to use.
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)
For an explanation of the concept of deep-learning-based classification see the introduction of chapter Deep Learning / Classification.
To successfully set 'gpu' parameters, cuDNN and cuBLAS are required, i.e. to set the parameter GenParamName 'runtime' to VarRef('gpu') or to set the GenParamName 'gpu' For further details, please refer to the Installation Guide, paragraph Requirements for Deep Learning.
Handle of the deep-learning-based classifier.
Name of the generic parameter.
Default value: 'classes'
List of values: 'batch_size', 'classes', 'gpu', 'image_dimensions', 'image_height', 'image_num_channels', 'image_width', 'learning_rate', 'momentum', 'runtime', 'runtime_init', 'weight_prior'
Value of the generic parameter.
Default value: ['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