Re: Crack P Code Matlab For Neural Network
Nichelle Gruger
For example, you could create a network with more hidden layers, or a deep neural network. There are many types of deep networks supported in MATLAB and resources for deep learning. For more info, check out the links in the description below.
You can generate a MATLAB function or Simulink diagram for simulating your neural network. Use genfunction to create the neural network including all settings, weight and bias values, functions, and calculations in one MATLAB function file.
A fully connected neural network with many options for customisation.
Basic training:
modelNN = learnNN(X, y);
Prediction:
p = predictNN(X_valid, modelNN);
One can use an arbitrary number of hidden layers, different activation functions (currently tanh or sigm), custom regularisation parameter, validation sets, etc. The code does not use any matlab toolboxes, therefore, it is perfect if you do not have the statistics and machine learning toolbox, or if you have an older version of matlab. I use the conjugate gradient algorithm for minimisation borrowed from Andrew Ngs machine learning course. See the github and comments in the code for more documentation.
Load the digits data as an image datastore using the imageDatastore function and specify the folder containing the image data. An image datastore enables you to store large image data, including data that does not fit in memory, and efficiently read batches of images during training of a convolutional neural network.
Calculate the number of images in each category. labelCount is a table that contains the labels and the number of images having each label. The datastore contains 1000 images for each of the digits 0-9, for a total of 10000 images. You can specify the number of classes in the last fully connected layer of your neural network as the OutputSize argument.
Divide the data into training and validation data sets, so that each category in the training set contains 750 images, and the validation set contains the remaining images from each label. splitEachLabel splits the datastore imds into two new datastores, imdsTrain and imdsValidation.
Image Input Layer An imageInputLayer is where you specify the image size, which, in this case, is 28-by-28-by-1. These numbers correspond to the height, width, and the channel size. The digit data consists of grayscale images, so the channel size (color channel) is 1. For a color image, the channel size is 3, corresponding to the RGB values. You do not need to shuffle the data because trainnet, by default, shuffles the data at the beginning of training. trainnet can also automatically shuffle the data at the beginning of every epoch during training.
Convolutional Layer In the convolutional layer, the first argument is filterSize, which is the height and width of the filters the training function uses while scanning along the images. In this example, the number 3 indicates that the filter size is 3-by-3. You can specify different sizes for the height and width of the filter. The second argument is the number of filters, numFilters, which is the number of neurons that connect to the same region of the input. This parameter determines the number of feature maps. Use the Padding name-value argument to add padding to the input feature map. For a convolutional layer with a default stride of 1, "same" padding ensures that the spatial output size is the same as the input size. You can also define the stride and learning rates for this layer using name-value arguments of convolution2dLayer.
Batch Normalization Layer Batch normalization layers normalize the activations and gradients propagating through a neural network, making neural network training an easier optimization problem. Use batch normalization layers between convolutional layers and nonlinearities, such as ReLU layers, to speed up neural network training and reduce the sensitivity to neural network initialization. Use batchNormalizationLayer to create a batch normalization layer.
ReLU Layer The batch normalization layer is followed by a nonlinear activation function. The most common activation function is the rectified linear unit (ReLU). Use reluLayer to create a ReLU layer.
Max Pooling Layer Convolutional layers (with activation functions) are sometimes followed by a down-sampling operation that reduces the spatial size of the feature map and removes redundant spatial information. Down-sampling makes it possible to increase the number of filters in deeper convolutional layers without increasing the required amount of computation per layer. One way of down-sampling is using a max pooling, which you create using maxPooling2dLayer. The max pooling layer returns the maximum values of rectangular regions of inputs, specified by the first argument, poolSize. In this example, the size of the rectangular region is [2,2]. The Stride name-value argument specifies the step size that the training function takes as it scans along the input.
Fully Connected Layer The convolutional and down-sampling layers are followed by one or more fully connected layers. As its name suggests, a fully connected layer is a layer in which the neurons connect to all the neurons in the preceding layer. This layer combines all the features learned by the previous layers across the image to identify the larger patterns. The last fully connected layer combines the features to classify the images. Therefore, the OutputSize parameter in the last fully connected layer is equal to the number of classes in the target data. In this example, the output size is 10, corresponding to the 10 classes. Use fullyConnectedLayer to create a fully connected layer.
Softmax Layer The softmax activation function normalizes the output of the fully connected layer. The output of the softmax layer consists of positive numbers that sum to one, which can then be used as classification probabilities by the classification layer. Create a softmax layer using the softmaxLayer function after the last fully connected layer.
Specify the training options. Choosing among the options requires empirical analysis. To explore different training option configurations by running experiments, you can use the Experiment Manager app.
Monitor the neural network accuracy during training by specifying validation data and validation frequency. The software trains the neural network on the training data and calculates the accuracy on the validation data at regular intervals during training. The validation data is not used to update the neural network weights.
The training progress plot shows the mini-batch loss and accuracy and the validation loss and accuracy. For more information on the training progress plot, see Monitor Deep Learning Training Progress. The loss is the cross-entropy loss. The accuracy is the percentage of images that the neural network classifies correctly.
Classify the test images. To make predictions with multiple observations, use the minibatchpredict function. To convert the prediction scores to labels, use the scores2label function. The minibatchpredict function automatically uses a GPU if one is available. Otherwise, the function uses the CPU.
Deep Learning is a very hot topic these days especially in computer vision applications and you probably see it in the news and get curious. Now the question is, how do you get started with it? Today's guest blogger, Toshi Takeuchi, gives us a quick tutorial on artificial neural networks as a starting point for your study of deep learning.
Many of us tend to learn better with a concrete example. Let me give you a quick step-by-step tutorial to get intuition using a popular MNIST handwritten digit dataset. Kaggle happens to use this very dataset in the Digit Recognizer tutorial competition. Let's use it in this example. You can download the competition dataset from "Get the Data" page:
The first column is the label that shows the correct digit for each sample in the dataset, and each row is a sample. In the remaining columns, a row represents a 28 x 28 image of a handwritten digit, but all pixels are placed in a single row, rather than in the original rectangular form. To visualize the digits, we need to reshape the rows into 28 x 28 matrices. You can use reshape for that, except that we need to transpose the data, because reshape operates by column-wise rather than row-wise.
The dataset stores samples in rows rather than in columns, so you need to transpose it. Then you will partition the data so that you hold out 1/3 of the data for model evaluation, and you will only use 2/3 for training our artificial neural network model.
W in the diagram stands for weights and b for bias units, which are part of individual neurons. Individual neurons in the hidden layer look like this - 784 inputs and corresponding weights, 1 bias unit, and 10 activation outputs.
If you look inside myNNfun.m, you see variables like IW1_1 and x1_step1_keep that represent the weights your artificial neural network model learned through training. Because we have 784 inputs and 100 neurons, the full layer 1 weights will be a 100 x 784 matrix. Let's visualize them. This is what our neurons are learning!
Now you are ready to use myNNfun.m to predict labels for the heldout data in Xtest and compare them to the actual labels in Ytest. That gives you a realistic predictive performance against unseen data. This is also the metric Kaggle uses to score submissions.
First, you see the actual output from the network, which shows the probability for each possible label. You simply choose the most probable label as your prediction and then compare it to the actual label. You should see 95% categorization accuracy.
You probably noticed that the artificial neural network model generated from the Pattern Recognition Tool has only one hidden layer. You can build a custom model with more layers if you would like, but this simple architecture is sufficient for most common problems.
The next question you may ask is how I picked 100 for the number of hidden neurons. The general rule of thumb is to pick a number between the number of input neurons, 784 and the number of output neurons, 10, and I just picked 100 arbitrarily. That means you might do better if you try other values. Let's do this programmatically this time. myNNscript.m will be handy for this - you can simply adapt the script to do a parameter sweep.
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