def supervised_learning(s, readout, h_fc1, sess, train_step, socket,saver):
#LOAD NET
# saving and loading networks
directory = 'LogGameFile'
numOfLogGameFile = len([item for item in os.listdir(directory) if os.path.isfile(os.path.join(directory, item))])
#NUM_OF_LEARNED_GAME = numOfLogGameFile
print("START OF TRAINING BY SUPERVISED NETWORK")
#first state
for i in range (0,NUM_OF_LEARNED_GAME):
nameLogFile =directory+"/F0000"[:-len(str(i))] + str(i)
logFile = LogFile(nameLogFile)
gameState = logFile.get_all_game_state()
tranning_network(s, readout, h_fc1, sess, gameState,train_step, socket,saver,i)
print("DONE SUPERVISED LEARNING")
return
#LOAD NETWORK INCASE ALREADY LEARNING
checkpoint = tf.train.get_checkpoint_state("saved_networks")
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(sess, checkpoint.model_checkpoint_path)
print("Successfully loaded:", checkpoint.model_checkpoint_path)
else:
print("Could not find old network weights")
print("Done loading network")
while (1):
string = input()
values = string.split(" ")
game = int(values[0])
stateIndex = int(values[1])
print("Game: " + str(game) + " State " + str(stateIndex))
directory = 'logCNN'
nameLogFile = directory + "/logGameCNN" + str(game)
logFile = LogFile(nameLogFile)
listGameState = logFile.get_all_game_state()
if (stateIndex > len(listGameState)):
print("State out of Range")
else:
gameState = listGameState[stateIndex]
print(gameState)
calculate_value_game_state(input_layer, readout, h_fc1, sess,
gameState)
print("-NEXT-")
