Alexander Munch-Hansen
9a2d87516e
evaluating a list of states with network.py. We can also save and restore models.
101 lines
2.5 KiB
Python
101 lines
2.5 KiB
Python
from network import Network
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import tensorflow as tf
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import random
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import numpy as np
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from board import Board
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import main
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config = main.config.copy()
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config['model'] = "tesauro_blah"
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config['force_creation'] = True
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config['board_representation'] = 'quack-fat'
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network = Network(config, config['model'])
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network.restore_model()
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initial_state = Board.initial_state
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initial_state_1 = ( 0,
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0, 0, 0, 2, 0, -5,
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0, -3, 0, 0, 0, 0,
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-5, 0, 0, 0, 3, 5,
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0, 0, 0, 0, 5, -2,
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0 )
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initial_state_2 = ( 0,
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-5, -5, -3, -2, 0, 0,
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0, 0, 0, 0, 0, 0,
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0, 0, 0, 15, 0, 0,
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0, 0, 0, 0, 0, 0,
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0 )
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boards = {initial_state,
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initial_state_1,
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initial_state_2 }
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def gen_21_rolls():
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"""
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Calculate all possible rolls, [[1,1], [1,2] ..]
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:return: All possible rolls
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"""
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a = []
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for x in range(1, 7):
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for y in range(1, 7):
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if not [x, y] in a and not [y, x] in a:
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a.append([x, y])
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return a
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def calculate_possible_states(board):
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possible_rolls = [(1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
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(1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
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(2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
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(4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
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(6, 6)]
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for roll in possible_rolls:
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meh = Board.calculate_legal_states(board, -1, roll)
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print(len(meh))
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return [Board.calculate_legal_states(board, -1, roll)
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for roll
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in possible_rolls]
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#for board in boards:
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# calculate_possible_states(board)
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#print("-"*30)
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#print(network.calculate_1_ply(session, Board.initial_state, [2,4], 1))
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board = network.board_trans_func(Board.initial_state, 1)
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input = [0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0, 0, 0, 1, 0]
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all_input = np.array([input for _ in range(20)])
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print(network.calc_vals(all_input))
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#print(" "*10 + "network_test")
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#print(" "*20 + "Depth 1")
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#print(network.calc_n_ply(1, session, Board.initial_state, 1, [2, 4]))
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#print(scores)
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#print(" "*20 + "Depth 2")
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#print(network.n_ply(2, session, boards, 1))
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# #print(x.shape)
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# with graph_lol.as_default():
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# session_2 = tf.Session(graph = graph_lol)
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# network_2 = Network(session_2)
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# network_2.restore_model()
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# print(network_2.eval_state(initial_state))
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# print(network.eval_state(initial_state))
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