This topic presents part of a typical multilayer network workflow. For more information and other steps, see Multilayer Shallow Neural Networks and Backpropagation Training. Show
Neural network training can be more efficient if you perform certain preprocessing steps on the network inputs and targets. This section describes several preprocessing routines that you can use. (The most common of these are provided automatically when you create a network, and they become part of the network object, so that whenever the network is used, the data coming into the network is preprocessed in the same way.) For example, in multilayer networks, sigmoid transfer functions are generally used in the hidden layers. These functions become essentially saturated when the net input is greater than three (exp (−3) ≅ 0.05). If this happens at the beginning of the training process, the gradients will be very small, and the network training will be very slow. In the first layer of the network, the net input is a product of the input times the weight plus the bias. If the input is very large, then the weight must be very small in order to prevent the transfer function from becoming saturated. It is standard practice to normalize the inputs before applying them to the network. Generally, the normalization step is applied to both the input vectors and the target vectors in the data set. In this way, the network output always falls into a normalized range. The network output can then be reverse transformed back into the units of the original target data when the network is put to use in the field. It is easiest to think of the neural network as having a preprocessing block that appears between the input and the first layer of the network and a postprocessing block that appears between the last layer of the network and the output, as shown in the following figure. Most of the network creation functions in the toolbox, including the multilayer network creation functions, such as You can override the default input and output processing functions by adjusting network properties after you create the network. To see a cell array list of processing functions assigned to the input of a network, access this property: net.inputs{1}.processFcns where the index 1 refers to the first input vector. (There is only one input vector for the feedforward network.) To view the processing functions returned by the output of a two-layer network, access this network property: net.outputs{2}.processFcns where the index 2 refers to the output vector coming from the second layer. (For the feedforward network, there is only one output vector, and it comes from the final layer.) You can use these properties to change the processing functions that you want your network to apply to the inputs and outputs. However, the defaults usually provide excellent performance. Several processing functions have parameters that customize their operation. You can access or change the parameters of the net.inputs{1}.processParams{i} You can access or change the parameters of the net.outputs{2}.processParams{i} For multilayer network creation functions, such as The following table lists the most common preprocessing and postprocessing functions. In most cases, you will not need to use them directly, since the preprocessing steps become part of the network object. When you simulate or train the network, the preprocessing and postprocessing will be done automatically.
Representing Unknown or Don't-Care TargetsUnknown or “don't care” targets can be represented with What type of objects are used in C++ to handle standard input and output operations?C++ uses a convenient abstraction called streams to perform input and output operations in sequential media such as the screen or the keyboard. A stream is an object where a program can either insert or extract characters to/from it.
When using flowcharts the input output symbol is represented by an oval?An End or Beginning While Creating a Flowchart
The oval, or terminator, is used to represent the start and end of a process. Use the Gliffy flowchart tool to drag and drop one of these bad boys and you've got yourself the beginning of a flowchart.
When creating a program using C++ which symbol is used for the insertion operator?In C++, stream insertion operator “<<” is used for output and extraction operator “>>” is used for input. We must know the following things before we start overloading these operators. 2) These operators must be overloaded as a global function.
Which type of structure makes a decision based on one or more conditions?Selection Structure. Use to make a decision or comparison and then, based on the result of that decision or comparison, to select one of two paths. The condition must result in either a true (yes) or false (no) answer. If the condition is true, the program performs one set of tasks.
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