def _fit_model(epochs):
# try:
# global model
# global training_input
# global training_output
#
# log.info('Fitting model')
#
# model.fit(training_input, training_output, epochs=epochs) #Not currently working
#
#
# log.info('\tModel fitted\n')
# return True
#
# except:
# log.error('\tUnknown error in NeuralNetwork._fit_model\n')
# return False
global model
global training_input
global training_output
print(model.summary())
log.info('Fitting model')
model.fit(training_input, training_output,
epochs=epochs) #Not currently working
log.info('\tModel fitted\n')
return True
def save_to_db(self, url, r):
while True:
if r.ready():
result = r.result
log.info('{:30} 查询结果: {}'.format(url, result.__repr__()))
# 更新数据库数据
break
def file_import(file_path, db=None):
log.info('正在向数据库导入 {} 文件'.format(file_path))
with OpenMysqlConn(db) as cursor:
with open(file_path, 'r') as file_hand:
for sql in file_hand.read().split(';'):
if sql == '\n':
continue
log.info('执行SQL: {}'.format(sql))
cursor.execute(sql)
def _quit(code=0):
"""Cleanly closes the game"""
# In error codes, 0 = clean close, no errors
# 1 = closed with errors, very bad
if code == 1:
log.critical("Game quiting with errors!")
else:
log.info("Game quiting...")
pygame.display.quit()
pygame.quit()
exit(code)
def run():
all_thd = []
for _ in range(THREAD_NUM):
log.info('正在启动线程 {}'.format(_))
t = Spider()
all_thd.append(t)
t.daemon = True
t.start()
for t in all_thd:
t.join()
def wrapper(self, count=0, *args, **kwargs):
try:
return fun(self, *args, **kwargs)
except requests.exceptions.ConnectionError:
log.error('{} 不存在'.format(self._url))
except Exception as e:
if count < retry_num:
count += 1
log.info("{}: 第{}次重试".format(self._url, count))
time.sleep(1)
return wrapper(self, count, *args, **kwargs)
else:
raise Exception(e)
def _add_input_layer():
try:
global model
log.info('Adding input layer')
model.add(keras.layers.Flatten(input_shape=(42)))
log.info('\tInput layer added\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._add_input_layer\n')
return False
def _create_model():
try:
global model
log.info('Creating network model')
model = Sequential()
log.info('\tNetwork model created\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._create_model\n')
return False
def _get_AI_move(playField):
field = _flatten_field(playField)
moves = AI.predict(
field) #Should return array eg: [0.1, 0.1, 0.3, 0.8, 0.4, 0.2, 0.1]
best_move = np.argmax(
moves) #Returns location of highest val, only first occurrence
while not _validate_move(playField, best_move):
moves[best_move] = 0.0
best_move = np.argmax(moves)
log.info("AI selected move at {}".format(best_move))
return best_move
def _compile_model():
try:
global model
log.info('Network compiling')
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
log.info('\tNetwork compiled\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._compile_model\n')
return False
def _evaluate_model():
try:
log.info('Evaluating model')
global model
global test_input # Test data doesn't exist, not strictly required
global test_output # Unless you want to make 1,000 more data sets
accuracy = model.evaluate(test_input, test_output)
log.info('\tModel evaluated at {}% loss\n'.format(accuracy))
return True
except:
log.error('\tUnknown error in NeuralNetwork._evaluate_model\n')
return False
def exportPlay(column):
"""
Converts play to an array and exports this data to a text file
:param column: The players move
:return:
"""
global GatherMove
GatherMove = False # Ensures that only plays after exported boards are recorded
dataForExport = [] # Prepare a list for the data to be dumped into
"""
#Intended AI output would be list of probabilities indicating best move
#e.g: [0.1, 0.3, 0.5, 0.7, 0.5, 0.2, 0.1]
#Would indicate best move to be 4th column
"""
for i in range(0, 7):
if column == i:
dataForExport.append(
1.0) #For move at col 2 would result in [0,0,1,0,0,0,0]
else:
dataForExport.append(0.0)
# EXPORTING CODE #
if not os.path.isdir("trainingData"):
# Verify the desired folder exists, if not, create it
log.debug("Training Data folder missing... creating")
os.mkdir("trainingData")
fileNum = 0
try:
while True:
# this loop makes sure it wont overwrite an existing file, then writes
filename = "trainingData/ExportedMove{}.txt".format(fileNum)
if os.path.isfile(filename):
fileNum += 1
else:
f = open(filename, "w")
f.write(str(dataForExport))
f.close()
log.info("Exported current state to {}".format(filename))
return True
except Exception as e:
# Error handling ^-^
log.error("Failed to export game state: {}".format(e))
return False
def _add_output_layer(size):
# try:
global model
log.info('Adding output layer')
model.add(keras.layers.Flatten())
# model.add(keras.layers.Dense(size, activation=tf.nn.softmax, output_size=6))
model.add(keras.layers.Dense(size, activation=tf.nn.softmax))
# todo: ValueError: Error when checking target: expected dense_4 to have shape (1,), but got array with shape (6,)
log.info('\tOutput layer added\n')
return True
# except:
log.error('\tUnknown error in NeuralNetwork._add_output_layer\n')
return False
def _add_hidden_layers(layers, nodes):
try:
global model
log.info('Adding {} hidden layers'.format(layers))
for _ in range(layers):
model.add(
keras.layers.Dense(nodes,
input_shape=(41, ),
activation=tf.nn.relu))
log.info('\tHidden layers aadded\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._add_hidden_layers\n')
return False
def _add_hidden_layers(layers, nodes):
# try:
global model
log.info('Adding {} hidden layers'.format(layers))
for _ in range(layers):
# model.add(keras.layers.Dense(nodes, activation=tf.nn.relu, output_size=42))
model.add(keras.layers.Dense(nodes, activation=tf.nn.relu))
# model.add(keras.layers.flatten())
log.info('\tHidden layers aadded\n')
return True
# except:
log.error('\tUnknown error in NeuralNetwork._add_hidden_layers\n')
return False
def get_result(self, url, r):
while True:
if r.ready():
try:
result = r.result
log.info('{:30} 查询共获取到: {}'.format(url, len(result)))
for new_url in result:
self.conn.lpush('task', new_url)
self.conn.lpush('cms_task', new_url)
conn = POOL.connection()
cursor = conn.cursor()
_sql = 'INSERT INTO Domain (domain) VALUES (%s);'
cursor.executemany(_sql, result)
conn.commit()
conn.close()
finally:
break
time.sleep(1)
def _fit_model(epochs):
# try:
# global model
# global training_input
# global training_output
#
# log.info('Fitting model')
#
# model.fit(training_input, training_output, epochs=epochs) #Not currently working
#
#
# log.info('\tModel fitted\n')
# return True
#
# except:
# log.error('\tUnknown error in NeuralNetwork._fit_model\n')
# return False
# global model
# global training_input
# global training_output
#
# # print(model.summary())
#
# log.info('Fitting model')
# model.fit(training_input, training_output, epochs=epochs) # Not currently working
#
#
# log.info('\tModel fitted\n')
try:
log.info('Fitting model')
global model
model.fit(training_input,
training_output,
epochs=epochs,
batch_size=128) # Trains neural network
log.debug("\tModel fitted\n")
return True
except:
log.error("\tFailed to fit model\n")
return False
def _load_model(name):
try:
global model
log.info('Loading model "{}"'.format(name))
name = name + '.model'
try:
new_model = keras.models.load_model(name)
log.info('\tLoaded model "{}"\n'.format(name))
return True
except:
log.error('\tModel "{}" failed to load\n'.format(name))
except:
log.error('\tUnknown error in NeuralNetwork._load_model\n')
return False
def _save_model(name):
try:
global model
log.info('Saving model')
name = name + '.model'
try:
model.save(name)
log.info('\tModel saved as {}'.format(name))
except:
log.error('\tFailed to save model as {}'.format(name))
return False
except:
log.error('\tUnknown error in NeuralNetwork._save_model\n')
return False
def _get_AI_move(playField):
"""
Gets the AIs next move
:param playField:
:return: int of move
"""
global AI
if not AI:
log.error(
"No AI found"
) # Ensures that an AI exists, does happen earlier as well, just making sure
nn._construct("AI")
nn._save_model("AI")
log.info("AI constructed")
AI = True
field = _flatten_field(playField)
print(np.array(field).shape)
moves = nn._predict(
field) # Should return array eg: [0.1, 0.1, 0.3, 0.8, 0.4, 0.2, 0.1]
best_move = np.argmax(
moves) # Returns location of highest val, only first occurrence
while not _validate_move(playField,
best_move): # Ensures return value is valid
moves[best_move] = 0.0
if np.sum(moves) == 0:
for i in range(8):
if _validate_move(
playField,
i): # If AI fails, just uses next available slot
return i
log.error("AI failed to return usable value")
return False
best_move = np.argmax(moves)
log.info("AI selected move at {}".format(best_move))
return best_move
def _get_data():
try:
global training_input
global training_output
training_input = []
training_output = []
log.info('Fetching training data')
try:
number_of_files = int(len(os.listdir('trainingData')))
except:
log.error('Directory trainingData does not exist')
return False
for file_num in range(0, int(number_of_files / 2)):
state_file = 'trainingData/ExportedState{}.txt'.format(file_num)
move_file = 'trainingData/ExportedMove{}.txt'.format(file_num)
state_data = df._format_array_v2(state_file)
move_data = df._format_array_v2(move_file)
if not state_data:
log.error('Failed to load board state data')
return False
if not move_data:
log.error('Failed to load move data')
return False
state_data = np.array(state_data)
move_data = np.array(move_data)
training_input.append(state_data)
training_output.append(move_data)
training_input = np.array(training_input)
training_output = np.array(training_output)
log.info('\tData fetched')
log.info('\t\tTraining_input length: {}\tShape: {}'.format(
len(training_input), training_input.shape))
log.info('\t\tTraining_out length: {}\tShape: {}\n'.format(
len(training_output), training_output.shape))
return True
except:
log.error('\tUnknown error in NeuralNetwork._get_data\n')
return False
def _compile_model():
try:
log.info('Compiling model')
global model
# log.info('Network compiling')
#
# # model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# model.compile(optimizer='adam')
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy']) # trains it using tensoroflow
log.info('\tModel compiled\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._compile_model\n')
return False
def flattenAndExport(playfield):
"""
Flattens the 2D array into a 1D array and exports this data to a text file
:param playfield: The game's playfield
:return:
"""
global GatherMove
GatherMove = True # The play made at this point to be recorded, for AI target data
dataForExport = [] # Prepare a list for the data to be dumped into
for row in playfield:
for item in row:
dataForExport.append(
item) # dump each item one by one into this new list
# EXPORTING CODE #
if not os.path.isdir("trainingData"):
# Verify the desired folder exists, if not, create it
log.debug("Training Data folder missing... creating")
os.mkdir("trainingData")
fileNum = 0
try:
while True:
# this loop makes sure it wont overwrite an existing file, then writes
filename = "trainingData/ExportedState{}.txt".format(fileNum)
if os.path.isfile(filename):
fileNum += 1
else:
f = open(filename, "w")
f.write(str(dataForExport))
f.close()
log.info("Exported current state to {}".format(filename))
return True
except Exception as e:
# Error handling ^-^
log.error("Failed to export game state: {}".format(e))
return False
def _load_model(name):
try:
global new_model
global has_new_model
log.info('Loading model "{}"'.format(name))
name = name + '.model'
try:
new_model = keras.models.load_model(name)
log.info('\tLoaded model "{}"\n'.format(name))
has_new_model = True
# keras.plot_model(new_model, to_file='Model.png') # Would be a nice idea
return True
except:
log.error('\tModel "{}" failed to load\n'.format(name))
except:
log.error('\tUnknown error in NeuralNetwork._load_model\n')
return False
def _add_input_layer():
# try:
global model
log.info('Adding input layer')
# model.add(keras.layers.Flatten(input_shape=(None, 42), output_size=42))
# model.add(keras.layers.Flatten)
model.add(keras.layers.Flatten(input_shape=(
1000,
41,
)))
# model.add(keras.layers.Input( shape=(1000, 41, )))
# model.add(keras.layers.Flatten(input_dim=41))
log.info('\tInput layer added\n')
return True
# except:
log.error('\tUnknown error in NeuralNetwork._add_input_layer\n')
return False
def _add_hidden_layers(layers, nodes):
try:
global model
log.info('Adding {} hidden layers'.format(layers))
# for _ in range(layers):
# # model.add(keras.layers.Dense(nodes, activation=tf.nn.relu, output_size=42))
# model.add(keras.layers.Dense(nodes, activation=tf.nn.relu))
# model.add(keras.layers.Dropout(0.01)) # Prevents over-fitting the model while training (memorisation)
# # model.add(keras.layers.Flatten())
for _ in range(layers):
model.add(Dense(nodes, activation='relu'))
model.add(Dropout(0.5))
log.info('\tHidden layers aadded\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._add_hidden_layers\n')
return False
def _add_output_layer(size):
try:
log.info('Adding output layer')
global model
# log.info('Adding output layer')
# print(model.summary())
# model.add(keras.layers.Flatten()) # todo: Currently cuts off here
# # todo: Flatten layer expecten min_ndim=3, got ndim=2
#
# # model.add(keras.layers.Dense(size, activation=tf.nn.softmax, output_size=6))
# model.add(keras.layers.Dense(size, activation=tf.nn.softmax))
# # todo: ValueError: Error when checking target: expected dense_4 to have shape (1,), but got array with shape (6,) Sorted?
model.add(Dense(size, activation='sigmoid')) # output layer
log.info('\tOutput layer added\n')
return True
except:
log.error('\tUnknown error in NeuralNetwork._add_output_layer\n')
return False
def _add_input_layer():
# try:
global model
log.info('Adding input layer')
# # model.add(keras.layers.Flatten(input_shape=(None, 42), output_size=42))
#
# # model.add(keras.layers.Flatten)
# model.add(keras.layers.Flatten(input_shape=(1000, 41, 1)))
# # model.add(keras.layers.Input( shape=(1000, 41, )))
# # model.add(keras.layers.Flatten(input_dim=41))
model.add(Dense(
32, activation='relu',
input_shape=(42, ))) # relu activiation layer, better perfomance.
model.add(Dropout(0.5))
log.info('\tInput layer added\n')
return True
# except:
log.error('\tUnknown error in NeuralNetwork._add_input_layer\n')
return False
def _get_data():
try:
global training_input
global training_output
training_input = []
training_output = []
log.info('Fetching training data')
number_of_files = int(len(os.listdir('trainingData')))
for file_num in range(0, int(number_of_files / 2)):
state_file = 'trainingData/ExportedState{}.txt'.format(file_num)
move_file = 'trainingData/ExportedMove{}.txt'.format(file_num)
f = open(state_file, "r")
data = list(f.read())
# data = np.load(file)
data = df._format_array_v2(state_file)
data = np.array(data)
training_input.append(data)
f = open(move_file, "r")
data = list(f.read())
# data = np.load(file)
data = df._format_array_v2(move_file)
data = np.array(data)
training_output.append(data)
training_input = np.array(training_input)
training_output = np.array(training_output)
log.info('\tData fetched')
log.info('\t\tTraining_input length: {}'.format(len(training_input)))
log.info('\t\tTraining_out length: {}\n'.format(len(training_output)))
return True
except:
log.error('\tUnknown error in NeuralNetwork._get_data\n')
return False
def start_game():
"""
Initialises the game and begins the main loop
:return: None
"""
log.info("Initialising game...")
playField = _create_playField(
ROW_COUNT, COLUMN_COUNT
) # Creates a playfield of size designated at the top of this file
log.info("Rendering playfield...")
if not TestMode:
renderer(playField) # Draw the User Interface
pygame.display.update() # Refresh the screen so drawing can be seen
log.info("Ready!")
_game_loop(playField) # Start the game loop
