[Activations
Melvin Amey
I have a Desktop & a Laptop, both had Pro 2022 installed and running fine. Today I signed out of 2022 on my Laptop, uninstalled it then installed 2023 as administrator. Now I cannot sign in to 2023 as apparently I have exceeded my allowed activations.
I am currently having this exceeded activations problem for myself now and I have tried the first option multiple time, I am currently trying the second option but my windows computer does not have the files
(I use google translate)
Howdy, I have a problem with activating the antivirus, because the problem appeared with the activation of the key. The problem is that I get an error called: Maximum number of activations exceeded.
Interestingly, the antivirus worked before the format, I formatted the computer and I can no longer activate it. And there are 200 days left until the end of the subscription. I spent money on it so I wish it would work. I wrote to support and am waiting for a reply, if I buy a key, it should always work until the subscription stops working. Because you have to format from time to time.
act = activations(___,Name=Value) returns network activations with additional options specified by one or more name-value pair arguments. For example, OutputAs="rows" specifies the activation output format as "rows". Use this syntax with any of the input arguments in previous syntaxes. Specify name-value arguments after all other input arguments.
Trained network, specified as a SeriesNetwork or a DAGNetwork object. You can get a trained network by importing a pretrained network (for example, by using the googlenet function) or by training your own network using trainNetwork.
ImageDatastore allows batch reading of JPG or PNG image files using prefetching. If you set the ReadFcn option to a custom function, then ImageDatastore does not prefetch and is usually significantly slower.
You can use other built-in datastores for making predictions by using the transform and combine functions. These functions can convert the data read from datastores to the format required by classify.
You can use other built-in datastores for making predictions by using the transform and combine functions. These functions can convert the data read from datastores to the table or cell array format required by activations. For example, you can transform and combine data read from in-memory arrays and CSV files using an ArrayDatastore and an TabularTextDatastore object, respectively.
For data that fits in memory and does not require additional processing like custom transformations, you can specify a single sequence as a numeric array or a data set of sequences as a cell array of numeric arrays.
For cell array input, the cell array must be an N-by-1 cell array of numeric arrays, where N is the number of observations. The size and shape of the numeric array representing a sequence depends on the type of sequence data.
You can use other built-in datastores for making predictions by using the transform and combine functions. These functions can convert the data read from datastores to the table or cell array format required by activations. For more information, see Datastores for Deep Learning.
For image input, if the OutputAs option is "channels", then the images in the input data can be larger than the input size of the image input layer of the network. For other output formats, the images in the input must have the same size as the input size of the image input layer of the network.
Because recurrent layers process sequence data one time step at a time, when the recurrent layer OutputMode property is "last", any padding in the final time steps can negatively influence the layer output. To pad or truncate sequence data on the left, set the SequencePaddingDirection option to "left".
For sequence-to-sequence neural networks (when the OutputMode property is "sequence" for each recurrent layer), any padding in the first time steps can negatively influence the predictions for the earlier time steps. To pad or truncate sequence data on the right, set the SequencePaddingDirection option to "right".
The "auto" and "mex" options can offer performance benefits at the expense of an increased initial run time. Subsequent calls with compatible parameters are faster. Use performance optimization when you plan to call the function multiple times using new input data.
The "mex" option generates and executes a MEX function based on the network and parameters used in the function call. You can have several MEX functions associated with a single network at one time. Clearing the network variable also clears any MEX functions associated with that network.
The "gpu", "multi-gpu", and "parallel" options require Parallel Computing Toolbox. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox). If you choose one of these options and Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.
Activations from the network layer, returned as a numeric array or a cell array of numeric arrays. The format of act depends on the type of input data, the type of layer output, and the specified OutputAs option.
For 2-D and 3-D image output, act is an n-by-m matrix, where n is the number of images and m is the number of output elements from the layer. In this case, act(i,:) contains the activations for the ith image.
For 2-D and 3-D image output, act is an m-by-n matrix, where m is the number of output elements from the chosen layer and n is the number of images. In this case, act(:,i) contains the activations for the ith image.
For 2-D image sequence output, act is an n-by-1 cell array of h-by-w-by-c-by-s matrices, where n is the number of sequences, h, w, and c are the height, width, and the number of channels of the images, respectively, and s is the sequence length.
For 3-D image sequence output, act is an n-by-1 cell array of h-by-w-by-c-by-d-by-s matrices, where n is the number of sequences, h, w, d, and c are the height, width, depth, and the number of channels of the images, respectively, and s is the sequence length.
For a single time step containing 2-D image data, act is a h-by-w-by-c-by-n array, where n is the number of sequences and h, w, and c are the height, width, and the number of channels of the images, respectively.
For a single time step containing 3-D image data, act is a h-by-w-by-c-by-d-by-n array, where n is the number of sequences and h, w, d, and c are the height, width, depth, and the number of channels of the images, respectively.
When you train a neural network using the trainnet or trainNetwork functions, or when you use prediction or validation functions with DAGNetwork and SeriesNetwork objects, the software performs these computations using single-precision, floating-point arithmetic. Functions for prediction and validation include predict, classify, and activations. The software uses single-precision arithmetic when you train neural networks using both CPUs and GPUs.
To provide the best performance, deep learning using a GPU in MATLAB is not guaranteed to be deterministic. Depending on your network architecture, under some conditions you might get different results when using a GPU to train two identical networks or make two predictions using the same network and data.
Only the OutputAs, MiniBatchSize, SequenceLength, SequencePaddingDirection, and SequencePaddingValue name-value pair arguments are supported for code generation. All name-value pairs must be compile-time constants.
Starting in R2024a, DAGNetwork and SeriesNetwork objects are not recommended, use dlnetwork objects instead. This recommendation means that the activations function is also not recommended. Use the predict function instead and specify the Outputs option.
Starting in R2022b, when you make predictions with sequence data using the predict, classify, predictAndUpdateState, classifyAndUpdateState, and activations functions and the SequenceLength option is an integer, the software pads sequences to the length of the longest sequence in each mini-batch and then splits the sequences into mini-batches with the specified sequence length. If SequenceLength does not evenly divide the sequence length of the mini-batch, then the last split mini-batch has a length shorter than SequenceLength. This behavior prevents time steps that contain only padding values from influencing predictions.
In previous releases, the software pads mini-batches of sequences to have a length matching the nearest multiple of SequenceLength that is greater than or equal to the mini-batch length and then splits the data. To reproduce this behavior, manually pad the input data such that the mini-batches have the length of the appropriate multiple of SequenceLength. For sequence-to-sequence workflows, you may also need to manually remove time steps of the output that correspond to padding values.
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Our aim was identify brain areas involved in the premonitory phase of migraine using functional neuroimaging. To this end, we performed positron emission tomography scans with H2(15)O to measure cerebral blood flow as a marker of neuronal activity. We conducted positron emission tomography scans at baseline, in the premonitory phase without pain and during migraine headache in eight patients. We used glyceryl trinitrate (nitroglycerin) to trigger premonitory symptoms and migraine headache in patients with episodic migraine without aura who habitually experienced premonitory symptoms during spontaneous attacks. The main outcome was comparing the first premonitory scans in all patients to baseline scans in all patients. We found activations in the posterolateral hypothalamus, midbrain tegmental area, periaqueductal grey, dorsal pons and various cortical areas including occipital, temporal and prefrontal cortex. Brain activations, in particular of the hypothalamus, seen in the premonitory phase of glyceryl trinitrate-triggered migraine attacks can explain many of the premonitory symptoms and may provide some insight into why migraine is commonly activated by a change in homeostasis.
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