436 lines
20 KiB
Python
436 lines
20 KiB
Python
import tensorflow as tf
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from cup import Cup
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import numpy as np
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from board import Board
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import os
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import time
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import sys
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import random
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from eval import Eval
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class Network:
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# board_features_quack has size 28
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# board_features_quack_fat has size 30
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# board_features_tesauro has size 198
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board_reps = {
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'quack-fat' : (30, Board.board_features_quack_fat),
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'quack' : (28, Board.board_features_quack),
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'tesauro' : (198, Board.board_features_tesauro)
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}
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def custom_tanh(self, x, name=None):
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return tf.scalar_mul(tf.constant(2.00), tf.tanh(x, name))
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def __init__(self, config, name):
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self.config = config
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self.checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
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self.name = name
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# Set board representation from config
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self.input_size, self.board_trans_func = Network.board_reps[
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self.config['board_representation']
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]
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self.output_size = 1
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self.hidden_size = 40
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# Can't remember the best learning_rate, look this up
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self.max_learning_rate = 0.1
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self.min_learning_rate = 0.001
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# self.learning_rate = 0.01
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self.global_step = tf.Variable(0, trainable=False, name="global_step")
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self.learning_rate = tf.maximum(self.min_learning_rate, tf.train.exponential_decay(self.max_learning_rate, self.global_step, 50000, 0.96, staircase=True), name="learning_rate")
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# Restore trained episode count for model
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episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
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if os.path.isfile(episode_count_path):
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with open(episode_count_path, 'r') as f:
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self.episodes_trained = int(f.read())
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else:
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self.episodes_trained = 0
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# input = x
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self.x = tf.placeholder('float', [1, self.input_size], name='input')
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self.value_next = tf.placeholder('float', [1, self.output_size], name="value_next")
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xavier_init = tf.contrib.layers.xavier_initializer()
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W_1 = tf.get_variable("w_1", (self.input_size, self.hidden_size),
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initializer=xavier_init)
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W_2 = tf.get_variable("w_2", (self.hidden_size, self.output_size),
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initializer=xavier_init)
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b_1 = tf.get_variable("b_1", (self.hidden_size,),
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initializer=tf.zeros_initializer)
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b_2 = tf.get_variable("b_2", (self.output_size,),
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initializer=tf.zeros_initializer)
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value_after_input = tf.sigmoid(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
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self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
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# tf.reduce_sum basically finds the sum of its input, so this gives the
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# difference between the two values, in case they should be lists, which
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# they might be if our input changes
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# TODO: Alexander thinks that self.value will be computed twice (instead of once)
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difference_in_values = tf.reshape(tf.subtract(self.value_next, self.value, name='difference_in_values'), [])
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tf.summary.scalar("difference_in_values", tf.abs(difference_in_values))
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trainable_vars = tf.trainable_variables()
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gradients = tf.gradients(self.value, trainable_vars)
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apply_gradients = []
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global_step_op = self.global_step.assign_add(1)
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with tf.variable_scope('apply_gradients'):
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for gradient, trainable_var in zip(gradients, trainable_vars):
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# Hopefully this is Δw_t = α(V_t+1 - V_t)▿_wV_t.
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backprop_calc = self.learning_rate * difference_in_values * gradient
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grad_apply = trainable_var.assign_add(backprop_calc)
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apply_gradients.append(grad_apply)
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self.training_op = tf.group(*apply_gradients, name='training_op')
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self.saver = tf.train.Saver(max_to_keep=1)
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def eval_state(self, sess, state):
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# Run state through a network
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# Remember to create placeholders for everything because wtf tensorflow
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# and graphs
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# Remember to create the dense layers
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# Figure out a way of giving a layer a custom activiation function (we
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# want something which gives [-2,2]. Naively tahn*2, however I fell this
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# is wrong.
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# tf.group, groups a bunch of actions, so calculate the different
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# gradients for the different weights, by using tf.trainable_variables()
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# to find all variables and tf.gradients(current_value,
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# trainable_variables) to find all the gradients. We can then loop
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# through this and calculate the trace for each gradient and variable
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# pair (note, zip can be used to combine the two lists found before),
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# and then we can calculate the overall change in weights, based on the
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# formula listed in tesauro (learning_rate * difference_in_values *
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# trace), this calculation can be assigned to a tf variable and put in a
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# list and then this can be grouped into a single operation, essentially
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# building our own backprop function.
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# Grouping them is done by
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# tf.group(*the_gradients_from_before_we_want_to_apply,
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# name="training_op")
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# If we remove the eligibily trace to begin with, we only have to
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# implement learning_rate * (difference_in_values) * gradients (the
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# before-mentioned calculation.
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# print("Network is evaluating")
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# print("eval ({})".format(self.name), state, val, sep="\n")
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return sess.run(self.value, feed_dict={self.x: state})
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def save_model(self, sess, episode_count, global_step):
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self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
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with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
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print("[NETWK] ({name}) Saving model to:".format(name=self.name),
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os.path.join(self.checkpoint_path, 'model.ckpt'))
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f.write(str(episode_count) + "\n")
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def restore_model(self, sess):
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if os.path.isfile(os.path.join(self.checkpoint_path, 'model.ckpt.index')):
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latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_path)
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print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
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str(latest_checkpoint))
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self.saver.restore(sess, latest_checkpoint)
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variables_names = [v.name for v in tf.trainable_variables()]
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values = sess.run(variables_names)
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for k, v in zip(variables_names, values):
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print("Variable: ", k)
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print("Shape: ", v.shape)
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print(v)
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# Restore trained episode count for model
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episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
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if os.path.isfile(episode_count_path):
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with open(episode_count_path, 'r') as f:
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self.config['start_episode'] = int(f.read())
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def make_move(self, sess, board, roll, player):
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# print(Board.pretty(board))
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legal_moves = Board.calculate_legal_states(board, player, roll)
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moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in legal_moves]
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scores = [x[1] if np.sign(player) > 0 else 1-x[1] for x in moves_and_scores]
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best_score_index = np.array(scores).argmax()
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best_move_pair = moves_and_scores[best_score_index]
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# print("Found the best state, being:", np.array(move_scores).argmax())
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return best_move_pair
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def eval(self, episode_count, trained_eps = 0, tf_session = None):
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def do_eval(sess, method, episodes = 1000, trained_eps = 0):
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start_time = time.time()
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def print_time_estimate(eps_completed):
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cur_time = time.time()
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time_diff = cur_time - start_time
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eps_per_sec = eps_completed / time_diff
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secs_per_ep = time_diff / eps_completed
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eps_remaining = (episodes - eps_completed)
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sys.stderr.write(
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"[EVAL ] Averaging {per_sec} episodes per second\n".format(per_sec=round(eps_per_sec, 2)))
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sys.stderr.write(
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"[EVAL ] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(
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eps_remaining=eps_remaining, time_remaining=int(eps_remaining * secs_per_ep)))
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sys.stderr.write(
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"[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
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if method == 'random':
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outcomes = []
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"""for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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board = Board.initial_state
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while Board.outcome(board) is None:
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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board = (self.p1.make_move(sess, board, self.p1.get_sym(), roll))[0]
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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board = Board.flip(Eval.make_random_move(Board.flip(board), 1, roll))
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sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
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outcomes.append(Board.outcome(board)[1])
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sys.stderr.write("\n")
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if i % 50 == 0:
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print_time_estimate(i)"""
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return outcomes
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elif method == 'pubeval':
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outcomes = []
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# Add the evaluation code for pubeval, the bot has a method make_pubeval_move(board, sym, roll),
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# which can be used to get the best move according to pubeval
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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board = Board.initial_state
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# print("init:", board, sep="\n")
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while Board.outcome(board) is None:
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# print("-"*30)
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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# print(roll)
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# prev_board = tuple(board)
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board = (self.make_move(sess, board, roll, 1))[0]
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# print("post p1:", board, sep="\n")
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# print("."*30)
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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# print(roll)
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# prev_board = tuple(board)
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board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
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# print("post pubeval:", board, sep="\n")
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# print("*"*30)
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# print(board)
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# print("+"*30)
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sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
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outcomes.append(Board.outcome(board)[1])
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sys.stderr.write("\n")
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if i % 10 == 0:
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print_time_estimate(i)
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return outcomes
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elif method == 'dumbeval':
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outcomes = []
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# Add the evaluation code for pubeval, the bot has a method make_pubeval_move(board, sym, roll),
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# which can be used to get the best move according to pubeval
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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board = Board.initial_state
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# print("init:", board, sep="\n")
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while Board.outcome(board) is None:
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# print("-"*30)
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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# print(roll)
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# prev_board = tuple(board)
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board = (self.make_move(sess, board, roll, 1))[0]
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# print("post p1:", board, sep="\n")
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# print("."*30)
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roll = (random.randrange(1, 7), random.randrange(1, 7))
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# print(roll)
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# prev_board = tuple(board)
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board = Eval.make_dumbeval_move(board, -1, roll)[0][0:26]
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# print("post pubeval:", board, sep="\n")
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# print("*"*30)
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# print(board)
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# print("+"*30)
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sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
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outcomes.append(Board.outcome(board)[1])
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sys.stderr.write("\n")
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if i % 10 == 0:
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print_time_estimate(i)
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return outcomes
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elif method == 'dumbmodel':
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outcomes = []
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"""
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config_prime = self.config.copy()
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config_prime['model_path'] = os.path.join(config_prime['model_storage_path'], 'dumbmodel')
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eval_bot = Bot(1, config = config_prime, name = "dumbmodel")
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#print(self.config, "\n", config_prime)
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outcomes = []
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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board = Board.initial_state
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while Board.outcome(board) is None:
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roll = (random.randrange(1,7), random.randrange(1,7))
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board = (self.make_move(board, self.p1.get_sym(), roll))[0]
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roll = (random.randrange(1,7), random.randrange(1,7))
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board = Board.flip(eval_bot.make_move(Board.flip(board), self.p1.get_sym(), roll)[0])
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sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
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outcomes.append(Board.outcome(board)[1])
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sys.stderr.write("\n")
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if i % 50 == 0:
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print_time_estimate(i)
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"""
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return outcomes
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else:
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sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
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return [0]
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if tf_session == None:
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with tf.Session() as session:
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session.run(tf.global_variables_initializer())
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self.restore_model(session)
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outcomes = [ (method, do_eval(session,
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method,
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episode_count,
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trained_eps = trained_eps))
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for method
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in self.config['eval_methods'] ]
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return outcomes
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else:
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outcomes = [ (method, do_eval(tf_session,
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method,
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episode_count,
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trained_eps = trained_eps))
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for method
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in self.config['eval_methods'] ]
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return outcomes
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def train_model(self, episodes=1000, save_step_size=100, trained_eps=0):
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with tf.Session() as sess:
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writer = tf.summary.FileWriter("/tmp/log/tf", sess.graph)
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sess.run(tf.global_variables_initializer())
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self.restore_model(sess)
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variables_names = [v.name for v in tf.trainable_variables()]
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values = sess.run(variables_names)
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for k, v in zip(variables_names, values):
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print("Variable: ", k)
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print("Shape: ", v.shape)
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print(v)
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start_time = time.time()
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def print_time_estimate(eps_completed):
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cur_time = time.time()
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time_diff = cur_time - start_time
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eps_per_sec = eps_completed / time_diff
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secs_per_ep = time_diff / eps_completed
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eps_remaining = (episodes - eps_completed)
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sys.stderr.write(
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"[TRAIN] Averaging {per_sec} episodes per second\n".format(per_sec=round(eps_per_sec, 2)))
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sys.stderr.write(
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"[TRAIN] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(
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eps_remaining=eps_remaining, time_remaining=int(eps_remaining * secs_per_ep)))
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sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
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outcomes = []
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for episode in range(1, episodes + 1):
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sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
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# TODO decide which player should be here
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player = 1
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prev_board = Board.initial_state
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# find the best move here, make this move, then change turn as the
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# first thing inside of the while loop and then call
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# best_move_and_score to get V_t+1
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i = 0
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while Board.outcome(prev_board) is None:
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i += 1
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#print("PREEEV_BOOOOAAARD:",prev_board)
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cur_board, cur_board_value = self.make_move(sess,
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prev_board,
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(random.randrange(1, 7), random.randrange(1, 7)), player)
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#print("The current value:",cur_board_value)
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# adjust weights
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sess.run(self.training_op,
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feed_dict={self.x: self.board_trans_func(prev_board, player),
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self.value_next: cur_board_value})
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player *= -1
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prev_board = cur_board
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final_board = prev_board
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sys.stderr.write("\t outcome {}\t turns {}".format(Board.outcome(final_board)[1], i))
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outcomes.append(Board.outcome(final_board)[1])
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final_score = np.array([Board.outcome(final_board)[1]])
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scaled_final_score = ((final_score + 2) / 4)
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#print("The difference in values:", scaled_final_score - cur_board_value)
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# print("scaled_final_score",scaled_final_score)
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with tf.name_scope("final"):
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merged = tf.summary.merge_all()
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summary, _, global_step = sess.run([merged, self.training_op, self.global_step],
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feed_dict={self.x: self.board_trans_func(prev_board, player),
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self.value_next: scaled_final_score.reshape((1, 1))})
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writer.add_summary(summary, episode + trained_eps)
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sys.stderr.write("\n")
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if episode % min(save_step_size, episodes) == 0:
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sys.stderr.write("[TRAIN] Saving model...\n")
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self.save_model(sess, episode + trained_eps, global_step)
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if episode % 50 == 0:
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print_time_estimate(episode)
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sys.stderr.write("[TRAIN] Saving model for final episode...\n")
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self.save_model(sess, episode+trained_eps, global_step=global_step)
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writer.close()
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return outcomes
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# take turn, which finds the best state and picks it, based on the current network
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# save current state
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# run training operation (session.run(self.training_op, {x:x, value_next, value_next})), (something which does the backprop, based on the state after having taken a turn, found before, and the state we saved in the beginning and from now we'll save it at the end of the turn
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# save the current state again, so we can continue running backprop based on the "previous" turn.
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# NOTE: We need to make a method so that we can take a single turn or at least just pick the next best move, so we know how to evaluate according to TD-learning. Right now, our game just continues in a while loop without nothing to stop it!
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