How to specify toolbox for function call

classify Classify data with the network [labels, scores] = classify(net, X) will classify the data X using the network net. labels will be an N-by-1 categorical vector where N is the number of observations, and scores will be an N-by-K matrix where K is the number of output classes. If the output of the network is a sequence, labels and scores will be N-by-1 cell arrays. Each element of labels will be a 1-by-S categorical array and each element of scores will be a K-by-S numeric array, where S is the sequence length of the output. The format of X will depend on the input layer for the network. The format of X will depend on the input layer for the network. For a network with an image input layer, X must be: - A single image. - A 4-D array of images, where the first three dimensions index the height, width and channels of an image, and the fourth dimension indexes the individual images. - A 5-D array of images, where the first four dimensions index the height, width, depth and channels of an image, and the fifth dimension indexes the individual images. - A datastore, where the output of the datastore read function is one image, a cell array of images, or a table whose first column contains images. - A table, where the first column contains either image paths or images. For a network with a sequence input layer, X must be: - A numeric array representing a single sequence. - A cell array of sequences. - A datastore, where the output of the datastore read function is a cell array or a table containing sequences in the first column. Each sequence must be either: - A vector sequence, specified by a C-by-S matrix, where C is the number of features and S is the number of time steps. - A 2-D image sequence, specified by an H-by-W-by-C-by-S array, where H-by-W-by-C is the 2-D image size and S is the number of time steps. - A 3-D image sequence, specified by an H-by-W-by-D-by-C-by-S array, where H-by-W-by-D-by-C is the 3-D image size and S is the number of time steps. For a network with a feature input layer, X must be: - A single observation specified as a row vector. - A 2-D array of observations, where the first dimension indexes the observations and the second dimension indexes the features. - A datastore, where the output of the datastore read function is a cell array or a table containing feature vectors in the first column. - A table, where each column represents a feature of the dataset. For a network with a point cloud input layer, X must be: - A single observation of point cloud data specified as a 2-D or 3-D numeric array. - A 3-D or 4-D numeric array of point clouds, where the second to last dimension indexes the channels and the last dimension indexes the individual observations. - A datastore, where the output of the datastore read function is a cell array containing point cloud data as a numeric array. [labels, scores] = classify(net, cds) classifies data using a multiple input network with the combined datastore cds. The combined datastore must return a 1-by-N cell array, where N is the number of network inputs. [labels, scores] = classify(net, X1, ..., XN) classifies data using a multi input network with N inputs, where the input data is X1, ..., XN. [labels, scores] = classify(__, 'PARAM1', VAL1, ...) specifies optional name-value pairs described below: 'MiniBatchSize' - The size of the mini-batches for computing predictions. Larger mini-batch sizes lead to faster predictions, at the cost of more memory. The default is 128. 'ExecutionEnvironment' - The execution environment for the network. This determines what hardware resources will be used to run the network. - 'auto' - Use a GPU if it is available, otherwise use the CPU. - 'gpu' - Use the GPU. To use a GPU, you must have Parallel Computing Toolbox(TM) and a supported GPU device. If a suitable GPU is not available, classify returns an error message. - 'cpu' - Use the CPU. - 'multi-gpu' - Use multiple GPUs on one machine, using a local parallel pool. If no pool is open, one is opened with one worker per supported GPU device. - 'parallel' - Use a compute cluster. If no pool is open, one is opened using the default cluster profile. If the pool has access to GPUs then they will be used and excess workers will be idle. If the pool has no GPUs then classification will take place on all cluster CPUs. The default is 'auto'. 'Acceleration' - Optimizations that can improve performance at the expense of some overhead on the first call and possible additional memory usage. - 'auto' - Automatically select optimizations suitable for the input network and environment. - 'mex' - (GPU only) Generate and execute a MEX function. Subsequent calls with the same network and options use the MEX function. This option is not supported when 'ExecutionEnvironment' value is either 'parallel' or 'multi-gpu'. - 'none' - Disable all acceleration. The default is 'auto'. 'SequenceLength' - Strategy to determine the length of the sequences used per mini-batch. Options are: - 'longest' to pad all sequences in a batch to the length of the longest sequence. - 'shortest' to truncate all sequences in a batch to the length of the shortest sequence. - Positive integer - For each mini-batch, pad the sequences to the nearest multiple of the specified length that is greater than the longest sequence length in the mini-batch, and then split the sequences into smaller sequences of the specified length. If splitting occurs, then the software creates extra mini-batches. The default is 'longest'. 'SequencePaddingValue' - Scalar value used to pad sequences where necessary. The default is 0. 'SequencePaddingDirection' - Direction of padding or truncation, specified as: - 'right' - Pad or truncate sequences on the right. - 'left' - Pad or truncate sequences on the left. The default is 'right'. See also DAGNetwork/predict, DAGNetwork/activations. Documentation for DAGNetwork/classify doc DAGNetwork/classify