文件名称:code-(2)
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Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.-Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.-Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.
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下载文件列表
matlab\batseq.m
......\funapp.m
......\hamex.m
......\hopex.m
......\lrnex.m
......\P2.m
......\rbfex.m
matlab