def cross_validation(self, **kwargs): from sklearn.model_selection import KFold kfold = KFold(n_splits=5, shuffle=True, random_state=2) input_data, target_data = utils_conv2d.create_data_prediction(**kwargs) count = 0 for train_index, test_index in kfold.split(input_data): count += 1 pivot = int(0.8 * len(train_index)) input_train = input_data[train_index[0:pivot]] input_valid = input_data[train_index[pivot:]] input_test = input_data[test_index] target_train = target_data[train_index[0:pivot]] target_valid = target_data[train_index[pivot:]] target_test = target_data[test_index] self.input_train = input_train self.input_valid = input_valid self.input_test = input_test self.target_train = target_train self.target_valid = target_valid self.target_test = target_test with open("config/conv2d_gsmap.yaml") as f: config = yaml.load(f) config['base_dir'] = "log/conv2d/" + str(count) + '/' self.config_model = common_util.get_config_model(**config) self.log_dir = self.config_model['log_dir'] self.callbacks = self.config_model['callbacks'] self.model = self.build_model_prediction() self.train() self.test() print("Complete " + str(count) + " !!!!")
def __init__(self, **kwargs): self.config_model = common_util.get_config_model(**kwargs) # load_data self.data = utils.load_dataset(**kwargs) self.input_train = self.data['input_train'] self.input_valid = self.data['input_valid'] self.input_test = self.data['input_test'] self.target_train = self.data['target_train'] self.target_valid = self.data['target_valid'] self.target_test = self.data['target_test'] # other configs self.log_dir = self.config_model['log_dir'] self.optimizer = self.config_model['optimizer'] self.loss = self.config_model['loss'] self.activation = self.config_model['activation'] self.batch_size = self.config_model['batch_size'] self.epochs = self.config_model['epochs'] self.callbacks = self.config_model['callbacks'] self.seq_len = self.config_model['seq_len'] self.horizon = self.config_model['horizon'] self.input_dim = self.config_model['input_dim'] self.output_dim = self.config_model['output_dim'] self.rnn_units = self.config_model['rnn_units'] self.dropout = self.config_model['dropout'] self.latent_space = 10 self.model = self.construct_model() self.timestep = kwargs['model'].get('timestep')
def __init__(self, **kwargs): self.config_model = common_util.get_config_model(**kwargs) # load_data self.data = utils_conv2d.load_dataset(**kwargs) self.input_train = self.data['input_train'] self.input_valid = self.data['input_valid'] self.input_test = self.data['input_test'] self.target_train = self.data['target_train'] self.target_valid = self.data['target_valid'] self.target_test = self.data['target_test'] # other configs self.log_dir = self.config_model['log_dir'] self.optimizer = self.config_model['optimizer'] self.loss = self.config_model['loss'] self.activation = self.config_model['activation'] self.batch_size = self.config_model['batch_size'] self.epochs = self.config_model['epochs'] self.callbacks = self.config_model['callbacks'] self.seq_len = self.config_model['seq_len'] self.horizon = self.config_model['horizon'] self.model = self.build_model_prediction()