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predict

Predict responses using a trained deep learning neural network

You can make predictions using a trained neural network for deep learning on either a CPU or GPU. Using a GPU requires Parallel Computing Toolbox™ and a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the hardware requirements using the ExecutionEnvironment name-value pair argument.

Syntax

YPred = predict(net,X)
YPred = predict(net,sequences)
YPred = predict(___,Name,Value)

Description

YPred = predict(net,X) predicts responses for the image data in X using the trained network net.

example

YPred = predict(net,sequences) predicts responses for the sequence or time series data in sequences using the trained LSTM network net.

example

YPred = predict(___,Name,Value) predicts responses with additional options specified by one or more name-value pair arguments.

Tip

When making predictions with sequences of different lengths, the mini-batch size can impact the amount of padding added to the input data which can result in different predicted values. Try using different values to see which works best with your network. To specify mini-batch size and padding options, use the 'MiniBatchSize' and 'SequenceLength' options.

Examples

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Load the sample data.

[XTrain,YTrain] = digitTrain4DArrayData;

digitTrain4DArrayData loads the digit training set as 4-D array data. XTrain is a 28-by-28-by-1-by-5000 array, where 28 is the height and 28 is the width of the images. 1 is the number of channels and 5000 is the number of synthetic images of handwritten digits. YTrain is a categorical vector containing the labels for each observation.

Construct the convolutional neural network architecture.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

Set the options to default settings for the stochastic gradient descent with momentum.

options = trainingOptions('sgdm');

Train the network.

rng('default')
net = trainNetwork(XTrain,YTrain,layers,options);
Training on single CPU.
Initializing input data normalization.
|========================================================================================|
|  Epoch  |  Iteration  |  Time Elapsed  |  Mini-batch  |  Mini-batch  |  Base Learning  |
|         |             |   (hh:mm:ss)   |   Accuracy   |     Loss     |      Rate       |
|========================================================================================|
|       1 |           1 |       00:00:00 |       11.72% |       2.2909 |          0.0100 |
|       2 |          50 |       00:00:05 |       52.34% |       1.8173 |          0.0100 |
|       3 |         100 |       00:00:10 |       66.41% |       1.1120 |          0.0100 |
|       4 |         150 |       00:00:15 |       67.19% |       0.9866 |          0.0100 |
|       6 |         200 |       00:00:21 |       73.44% |       0.7928 |          0.0100 |
|       7 |         250 |       00:00:26 |       81.25% |       0.6349 |          0.0100 |
|       8 |         300 |       00:00:31 |       83.59% |       0.6306 |          0.0100 |
|       9 |         350 |       00:00:37 |       83.59% |       0.4726 |          0.0100 |
|      11 |         400 |       00:00:41 |       92.97% |       0.3709 |          0.0100 |
|      12 |         450 |       00:00:47 |       95.31% |       0.2843 |          0.0100 |
|      13 |         500 |       00:00:51 |       92.19% |       0.2760 |          0.0100 |
|      15 |         550 |       00:00:56 |       98.44% |       0.2187 |          0.0100 |
|      16 |         600 |       00:01:01 |       96.88% |       0.2164 |          0.0100 |
|      17 |         650 |       00:01:05 |       96.88% |       0.1960 |          0.0100 |
|      18 |         700 |       00:01:10 |      100.00% |       0.1066 |          0.0100 |
|      20 |         750 |       00:01:14 |       99.22% |       0.0850 |          0.0100 |
|      21 |         800 |       00:01:19 |       99.22% |       0.1224 |          0.0100 |
|      22 |         850 |       00:01:23 |       99.22% |       0.0832 |          0.0100 |
|      24 |         900 |       00:01:28 |       97.66% |       0.1246 |          0.0100 |
|      25 |         950 |       00:01:32 |       98.44% |       0.0821 |          0.0100 |
|      26 |        1000 |       00:01:38 |       99.22% |       0.0601 |          0.0100 |
|      27 |        1050 |       00:01:46 |       99.22% |       0.0679 |          0.0100 |
|      29 |        1100 |       00:01:54 |       99.22% |       0.0519 |          0.0100 |
|      30 |        1150 |       00:02:00 |       99.22% |       0.0590 |          0.0100 |
|      30 |        1170 |       00:02:02 |      100.00% |       0.0579 |          0.0100 |
|========================================================================================|

Run the trained network on a test set and predict the scores.

[XTest,YTest] = digitTest4DArrayData;
YPred = predict(net,XTest);

predict, by default, uses a CUDA® enabled GPU with compute capability 3.0, when available. You can also choose to run predict on a CPU using the 'ExecutionEnvironment','cpu' name-value pair argument.

Display the first 10 images in the test data and compare to the predictions from predict.

YTest(1:10,:)
ans = 10x1 categorical array
     0 
     0 
     0 
     0 
     0 
     0 
     0 
     0 
     0 
     0 

YPred(1:10,:)
ans = 10x10 single matrix

    0.9988    0.0000    0.0002    0.0006    0.0000    0.0000    0.0000    0.0000    0.0000    0.0004
    0.9188    0.0000    0.0206    0.0001    0.0000    0.0002    0.0030    0.0001    0.0017    0.0554
    0.9999    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000
    0.9704    0.0000    0.0000    0.0000    0.0000    0.0000    0.0038    0.0000    0.0029    0.0229
    0.9775    0.0000    0.0036    0.0001    0.0000    0.0000    0.0001    0.0001    0.0186    0.0001
    0.9708    0.0000    0.0004    0.0000    0.0000    0.0000    0.0028    0.0000    0.0245    0.0015
    0.9921    0.0000    0.0001    0.0000    0.0000    0.0000    0.0075    0.0000    0.0004    0.0000
    1.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000    0.0000
    0.9594    0.0000    0.0004    0.0000    0.0000    0.0004    0.0000    0.0000    0.0002    0.0397
    0.9420    0.0000    0.0050    0.0014    0.0001    0.0001    0.0352    0.0000    0.0130    0.0033

YTest contains the digits corresponding to the images in XTest. The columns of YPred contain predict’s estimation of a probability that an image contains a particular digit. That is, the first column contains the probability estimate that the given image is digit 0, the second column contains the probability estimate that the image is digit 1, the third column contains the probability estimate that the image is digit 2, and so on. You can see that predict’s estimation of probabilities for the correct digits are almost 1 and the probability for any other digit is almost 0. predict correctly estimates the first 10 observations as digit 0.

Load pretrained network. JapaneseVowelsNet is a pretrained LSTM network trained on the Japanese Vowels dataset as described in [1] and [2]. It was trained on the sequences sorted by sequence length with a mini-batch size of 27.

load JapaneseVowelsNet

View the network architecture.

net.Layers
ans = 
  5x1 Layer array with layers:

     1   'sequenceinput'   Sequence Input          Sequence input with 12 dimensions
     2   'lstm'            LSTM                    LSTM with 100 hidden units
     3   'fc'              Fully Connected         9 fully connected layer
     4   'softmax'         Softmax                 softmax
     5   'classoutput'     Classification Output   crossentropyex with '1' and 8 other classes

Load the test data.

load JapaneseVowelsTest

Make predictions on the test data.

YPred = predict(net,XTest);

View the prediction scores for the first 10 sequences.

YPred(1:10,:)
ans = 10x9 single matrix

    0.9918    0.0000    0.0000    0.0000    0.0006    0.0010    0.0001    0.0006    0.0059
    0.9868    0.0000    0.0000    0.0000    0.0006    0.0010    0.0001    0.0010    0.0105
    0.9924    0.0000    0.0000    0.0000    0.0006    0.0010    0.0001    0.0006    0.0054
    0.9896    0.0000    0.0000    0.0000    0.0006    0.0009    0.0001    0.0007    0.0080
    0.9965    0.0000    0.0000    0.0000    0.0007    0.0009    0.0000    0.0003    0.0016
    0.9888    0.0000    0.0000    0.0000    0.0006    0.0010    0.0001    0.0008    0.0087
    0.9886    0.0000    0.0000    0.0000    0.0006    0.0010    0.0001    0.0008    0.0089
    0.9982    0.0000    0.0000    0.0000    0.0006    0.0007    0.0000    0.0001    0.0004
    0.9883    0.0000    0.0000    0.0000    0.0006    0.0010    0.0001    0.0008    0.0093
    0.9959    0.0000    0.0000    0.0000    0.0007    0.0011    0.0000    0.0004    0.0019

Compare these prediction scores to the labels of these sequences. The function assigns high prediction scores to the correct class.

YTest(1:10)
ans = 10x1 categorical array
     1 
     1 
     1 
     1 
     1 
     1 
     1 
     1 
     1 
     1 

Input Arguments

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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 alexnet function) or by training your own network using trainNetwork.

Image data, specified as one of the following.

InputDescription
3-D arrayNumeric array that represents a single image. The array has size h-by-w-by-c, where h, w, and c correspond to the height, width, and the number of channels of the image, respectively.
4-D arrayNumeric array that represents a stack of images. The array has size h-by-w-by-c-by-N, where N is the number of images in the image stack.
Image datastore

ImageDatastore with categorical labels.

For more information, see the imds argument of trainNetwork.

Datastore

Datastore that returns data as a single image, a cell array of images, or a table whose first column contains images.

For more information, see Datastores for Deep Learning.

Table

The first column of the table contains either image paths or 3-D arrays representing images. Subsequent columns contain the responses.

For more information, see the tbl argument of trainNetwork.

Sequence or time series data, specified as an N-by-1 cell array of numeric arrays, where N is the number of observations, a numeric array representing a single sequence, or a datastore.

For cell array or numeric array input, the dimensions of the numeric arrays containing the sequences depend on the type of data.

InputDescription
Vector sequencesc-by-s matrices, where c is the number of features of the sequences and s is the sequence length.
2-D image sequencesh-by-w-by-c-by-s arrays, where h, w, and c correspond to the height, width, and number of channels of the images, respectively, and s is the sequence length.
3-D image sequencesh-by-w-by-d-by-c-by-s, where h, w, d, and c correspond to the height, width, depth, and number of channels of the 3-D images, respectively, and s is the sequence length.

For datastore input, the datastore must return data as a cell array of sequences or a table whose first column contains sequences. The dimensions of the sequence data must correspond to the table above.

Name-Value Pair Arguments

Example: 'MiniBatchSize',256 specifies the mini-batch size as 256.

Specify optional comma-separated pair of Name,Value argument. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' ').

Size of mini-batches to use for prediction, specified as a positive integer. Larger mini-batch sizes require more memory, but can lead to faster predictions.

When making predictions with sequences of different lengths, the mini-batch size can impact the amount of padding added to the input data which can result in different predicted values. Try using different values to see which works best with your network. To specify mini-batch size and padding options, use the 'MiniBatchSize' and 'SequenceLength' options.

Example: 'MiniBatchSize',256

Performance optimization, specified as the comma-separated pair consisting of 'Acceleration' and one of the following:

  • 'auto' — Automatically apply a number of optimizations suitable for the input network and hardware resource.

  • 'mex' — Compile and execute a MEX function. This option is available when using a GPU only. Using a GPU requires Parallel Computing Toolbox and a CUDA enabled NVIDIA GPU with compute capability 3.0 or higher. If Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

  • 'none' — Disable all acceleration.

The default option is 'auto'. If 'auto' is specified, MATLAB® will apply a number of compatible optimizations. If you use the 'auto' option, MATLAB does not ever generate a MEX function.

Using the 'Acceleration' options 'auto' and 'mex' can offer performance benefits, but 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 'mex' option is only available for input data specified as a numeric array, cell array of numeric arrays, table, or image datastore. No other types of datastore support the 'mex' option.

The 'mex' option is only available when you are using a GPU. You must also have a C/C++ compiler installed. For setup instructions, see MEX Setup (GPU Coder).

'mex' acceleration does not support all layers. For a list of supported layers, see Supported Layers (GPU Coder).

Example: 'Acceleration','mex'

Hardware resource, specified as the comma-separated pair consisting of 'ExecutionEnvironment' and one of the following:

  • 'auto' — Use a GPU if one is available; otherwise, use the CPU.

  • 'gpu' — Use the GPU. Using a GPU requires Parallel Computing Toolbox and a CUDA enabled NVIDIA GPU with compute capability 3.0 or higher. If Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

  • 'cpu' — Use the CPU.

Example: 'ExecutionEnvironment','cpu'

Option to pad, truncate, or split input sequences, specified as one of the following:

  • 'longest' — Pad sequences in each mini-batch to have the same length as the longest sequence. This option does not discard any data, though padding can introduce noise to the network.

  • 'shortest' — Truncate sequences in each mini-batch to have the same length as the shortest sequence. This option ensures that no padding is added, at the cost of discarding data.

  • 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. Use this option if the full sequences do not fit in memory. Alternatively, try reducing the number of sequences per mini-batch by setting the 'MiniBatchSize' option to a lower value.

If you specify the sequence length as a positive integer, then the software processes the smaller sequences in consecutive iterations. The network updates the network state between the split sequences.

The software pads and truncates the sequences on the right. To learn more about the effect of padding, truncating, and splitting the input sequences, see Sequence Padding, Truncation, and Splitting.

Example: 'SequenceLength','shortest'

Value by which to pad input sequences, specified as a scalar. The option is valid only when SequenceLength is 'longest' or a positive integer. Do not pad sequences with NaN, because doing so can propagate errors throughout the network.

Example: 'SequencePaddingValue',-1

Output Arguments

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Predicted scores or responses, returned as a matrix, a 4-D numeric array, or a cell array of matrices. The format of YPred depends on the type of problem.

The following table describes the format for classification problems.

TaskFormat
Image classificationN-by-K matrix, where N is the number of observations, and K is the number of classes
Sequence-to-label classification
Sequence-to-sequence classification

N-by-1 cell array of matrices, where N is the number of observations. The sequences are matrices with K rows, where K is the number of responses. Each sequence has the same number of time steps as the corresponding input sequence.

The following table describes the format for regression problems.

TaskFormat
Image Regression
  • N-by-R matrix, where N is the number of observations and R is the number of responses.

  • h-by-w-by-c-by-N numeric array, where N is the number of observations and h-by-w-by-c is the image size of a single response.

Sequence-to-one regressionN-by-R matrix, where N is the number of observations and R is the number of responses.
Sequence-to-sequence regression

N-by-1 cell array of numeric sequences, where N is the number of observations. The sequences are matrices with R rows, where R is the number of responses. Each sequence has the same number of time steps as the corresponding input sequence.

For sequence-to-sequence regression problems with one observation, sequences can be a matrix. In this case, YPred is a matrix of responses.

Algorithms

If the image data contains NaNs, predict propagates them through the network. If the network has ReLU layers, these layers ignore NaNs. However, if the network does not have a ReLU layer, then predict returns NaNs as predictions.

All functions for deep learning training, prediction, and validation in Deep Learning Toolbox™ perform computations using single-precision, floating-point arithmetic. Functions for deep learning include trainNetwork, predict, classify, and activations. The software uses single-precision arithmetic when you train networks using both CPUs and GPUs.

Alternatives

You can compute the predicted scores and the predicted classes from a trained network using classify.

You can also compute the activations from a network layer using activations.

For sequence-to-label and sequence-to-sequence classification networks (LSTM networks), you can make predictions and update the network state using classifyAndUpdateState and predictAndUpdateState.

References

[1] M. Kudo, J. Toyama, and M. Shimbo. "Multidimensional Curve Classification Using Passing-Through Regions." Pattern Recognition Letters. Vol. 20, No. 11–13, pages 1103–1111.

[2] UCI Machine Learning Repository: Japanese Vowels Dataset. https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels

Extended Capabilities

Introduced in R2016a