Implement a Multilayer Neural Network using supervized learning to approximate/simulate world terrains that look real.

• Open Octave
• Load multilayer.m file.
• Run study_terrain(terrain_path, epochs, n_samples) where:
  • terrain_path is the relative path to a terrain data file.
  • epochs is the number of epochs to run.
  • n_samples is the amounts of data points to use from the data file.

Alternatively, you may run study_terrain_custom which offers richer configuration arguments:

• study_terrain_custom(terrain_file, epochs, n_samples, learn_function, eta, momentum) where:
  • terrain_file, epochs, n_samples are the same as above.
  • learn_function must be either "incremental" or "batch" and is used to set the learning function.
  • eta is the learning rate.
  • momentum is the momentum factor.

• multilayer.m contains the functions necessary to make the neural network.
• gen_arq.m generates the eleven architectures we experimented with.
• study_terrain uses the already pre-set best parameters to learn a new terrain.
• test_results.data contains the results of running the 11th architecture with different epochs and sample sizes.
• initial_W.data contains the initial weights matrix utilized in the tests.
• /terrains contains all the terrains provided.
• /images contains the different graphs we gathered, such as different error rates for different architectures and many other things