D20

• Everything is silent again.

GPT-2 Agents

• Continuing with PGNtoEnglish
  • Building out move text
  • Changing board to a dataframe, since I can display it as a table in pyplot – done!

• Here’s the code for making the chesstable table in pyplot:

  import pandas as pd
  import matplotlib.pyplot as plt
  
  class Chessboard():
      board:pd.DataFrame
      rows:List
      cols:List
  
      def __init__(self):
          self.reset()
  
      def reset(self):
          self.cols = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
          self.rows = [8, 7, 6, 5, 4, 3, 2, 1]
          self.board = df = pd.DataFrame(columns=self.cols, index=self.rows)
          for number in self.rows:
              for letter in self.cols:
                  df.at[number, letter] = pieces.NONE.value
  
          self.populate_board()
          self.print_board()
  
      def populate_board(self):
          self.board.at[1, 'a'] = pieces.WHITE_ROOK.value
          self.board.at[1, 'h'] = pieces.WHITE_ROOK.value
          self.board.at[1, 'b'] = pieces.WHITE_KNIGHT.value
          self.board.at[1, 'g'] = pieces.WHITE_KNIGHT.value
          self.board.at[1, 'c'] = pieces.WHITE_BISHOP.value
          self.board.at[1, 'f'] = pieces.WHITE_BISHOP.value
          self.board.at[1, 'd'] = pieces.WHITE_QUEEN.value
          self.board.at[1, 'e'] = pieces.WHITE_KING.value
  
          self.board.at[8, 'a'] = pieces.BLACK_ROOK.value
          self.board.at[8, 'h'] = pieces.BLACK_ROOK.value
          self.board.at[8, 'b'] = pieces.BLACK_KNIGHT.value
          self.board.at[8, 'g'] = pieces.BLACK_KNIGHT.value
          self.board.at[8, 'c'] = pieces.BLACK_BISHOP.value
          self.board.at[8, 'f'] = pieces.BLACK_BISHOP.value
          self.board.at[8, 'd'] = pieces.BLACK_KING.value
          self.board.at[8, 'e'] = pieces.BLACK_QUEEN.value
  
          for letter in self.cols:
              self.board.at[2, letter] = pieces.WHITE_PAWN.value
              self.board.at[7, letter] = pieces.BLACK_PAWN.value
  
      def print_board(self):
          fig, ax = plt.subplots()
  
          # hide axes
          fig.patch.set_visible(False)
          ax.axis('off')
          ax.axis('tight')
  
          ax.table(cellText=self.board.values, colLabels=self.cols, rowLabels=self.rows, loc='center')
  
          fig.tight_layout()
  
          plt.show()

GOES

• Continuing with the MLP sequence-to-sequence NN
• Writing
• Reading
  • Hmm. Just realized that the input vector being defined by the query is a bit problematic. I think I need to define the input vector size and then ensure that the query creates sufficient points. Fixed. It now stores the model with the specified input vector size:

• And here’s the loaded model in newly-retrieved data:

• Here’s the model learning two waveforms. Went from 400×2 neurons to 3200×2:

• Combining with GAN
  • Subtract the sin from the noisy_sin to get the moise and train on that
• Start writing paper? What are other venues beyond GVSETS?
• 2:00 status meeting

JuryRoom

• 3:30 Meeting
• 6:00 Meeting