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 _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)
data = df._format_array_v2(state_file)
if not data:
log.error('Failed to load data')
return False
data = np.array(data)
training_input.append(data)
data = df._format_array_v2(move_file)
if not data:
log.error('Failed to load data')
return False
data = np.array(data)
training_output.append(data)
training_input = np.array(training_input)
training_output = np.array(training_output)
log.info("Input array shape: {}".format(training_input.shape))
log.info("Input data array shape: {}".format(training_input[0].shape))
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
