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计算机代写|机器学习代写Machine Learning代考|COMP7703 Training the Perceptron Model

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计算机代写|机器学习代写Machine Learning代考|Training the Perceptron Model

To train the perceptron to place objects into two different categories a training set of data is used. The training set contains the relationship between a collection of given input features and the desired output category for those specific inputs. The training set may contain multiple entries of desired input/output pairs.

To initiate the training sequence, the weights of the perceptron model and the bias are initially set to zero or some small random values. The inputs are then multiplied by the input weights. The weighted inputs are summed with the bias to determine the value of net and then passed to the activation function. The activation function generates the appropriate output ( 1 or 0 ) if the net value has exceeded the set threshold of zero.

The perceptron output is compared to the desired output provided by the training set. The error is then calculated. The error is the difference between the desired training set output and the actual output provided by the perceptron output. The weights and the bias are then updated using the equations provided in Fig.6.2. In addition to the error term, the weights and update equations also provide a learning rate term (alpha). The value of alpha is set to a value from 0 and 1 . A larger value provides for more dramatic weight changes. A smaller value of alpha may require additional computation time but potentially yield better convergence results.

The perceptron now processes the second input/output pair from the training set using the updated weights and bias values. This process continues through the entire training set, called the epoch, until the model converges. The model converges when the error for each entry in the training set is zero or at a desired error goal. This may require multiple iterations of applying the training set to the perceptron model. In the first upcoming example, convergence required two sequential applications of the training set. The second example required 1,500 iterative applications of the training set.

计算机代写|机器学习代写Machine Learning代考|Single Perceptron Run Mode

In the previous sketch, the “Run Mode begins at the “else” statement. To implement the “Run Mode,” the following additions are required for the sketch:

Equip the Arduino UNO R3 with an external switch to select between the Train(1)/Run(0) mode.

Provide an external LED to indicate the current sketch mode: Train(on)/Run(off).

Provide the sketch the ability to write the value of weights and bias to EEPROM in the Train mode.

Provide the sketch the ability to read the value of weights and bias from the EEPROM in the Run mode.

In the Run Mode, have the sketch prompt the user for a new input data pair outside (or inside) the original training set.

Provide code to categorize the provided data input/output pair into a specific category based on the training set weights and bias.

Provide external LEDs to indicate the two different object categories.
A UML diagram for the sketch was provided earlier in Fig. 6.5.

We use a dual inline package (DIP) switch to select the sketch mode (Train(1)/Run(0)). Also, three LEDs are used to indicate the sketch mode (Train(on)/Run(off)) and the category the new input pair is placed as shown in Fig. 6.7.

To test the sketch, begin by setting the “Train/Run Mode” DIP switch to the “Train” position. When the sketch has completed training (MSE $=0$ or desired MSE goal), change the switch position to “Run.” The user is prompted for values of $x 1$ and $x 2$ input feature values. To test the perceptron, use input values both inside and outside the original training set. Verify the algorithm places the data into the proper category.

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