150 lines
6.8 KiB
Python
150 lines
6.8 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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class Network:
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hidden_size = 40
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input_size = 26
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output_size = 1
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# Can't remember the best learning_rate, look this up
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learning_rate = 0.1
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# TODO: Actually compile tensorflow properly
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#os.environ["TF_CPP_MIN_LOG_LEVEL"]="2"
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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, session, config, name):
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self.config = config
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self.session = session
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self.checkpoint_path = config['model_path']
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self.name = name
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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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# input = x
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self.x = tf.placeholder('float', [1, Network.input_size], name='x')
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self.value_next = tf.placeholder('float', [1, Network.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", (Network.input_size, Network.hidden_size),
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initializer=xavier_init)
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W_2 = tf.get_variable("w_2", (Network.hidden_size, Network.output_size),
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initializer=xavier_init)
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b_1 = tf.get_variable("b_1", (Network.hidden_size,),
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initializer=tf.zeros_initializer)
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b_2 = tf.get_variable("b_2", (Network.output_size,),
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initializer=tf.zeros_initializer)
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value_after_input = self.custom_tanh(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
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self.value = self.custom_tanh(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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difference_in_values = tf.reduce_sum(self.value_next - self.value, name='difference')
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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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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 = Network.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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self.session.run(tf.global_variables_initializer())
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def eval_state(self, 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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val = self.session.run(self.value, feed_dict={self.x: state})
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#print("eval ({})".format(self.name), state, val, sep="\n")
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return val
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def save_model(self, episode_count):
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self.saver.save(self.session, os.path.join(self.checkpoint_path, 'model.ckpt'))
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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):
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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(self.session, latest_checkpoint)
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# Have a circular dependency, #fuck, need to rewrite something
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def train(self, board, v_next):
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# print("lol")
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board = np.array(board).reshape((1,26))
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self.session.run(self.training_op, feed_dict = {self.x:board, self.value_next: v_next})
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# while game isn't done:
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#x_next = g.next_move()
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#value_next = network.eval_state(x_next)
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#self.session.run(self.training_op, feed_dict={self.x: x, self.value_next: value_next})
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#x = x_next
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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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