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Belle II Software development
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Public Member Functions | |
__init__ (self, train_x, train_y, valid_x, valid_y, batch_size, seed=None, epoch_random_shuffle=True) | |
sanitize_labels (self) | |
batch_iterator (self) | |
Public Attributes | |
train_x = train_x.astype(np.float32) | |
training features | |
int | train_y = train_y.astype(np.float32) |
training targets | |
valid_x = valid_x.astype(np.float32) | |
validation features | |
int | valid_y = valid_y.astype(np.float32) |
validation targets | |
batch_size = batch_size | |
batch size | |
seed = seed | |
random generator seed | |
epoch_random_shuffle = epoch_random_shuffle | |
bool, enables shuffling | |
train_events = self.train_x.shape[0] | |
number of training events | |
valid_events = self.valid_x.shape[0] | |
number of validation events | |
feature_number = self.train_x.shape[1] | |
number of features | |
batches = self.train_x.shape[0] // self.batch_size | |
number of batches | |
train_idx = np.zeros(self.train_x.shape[0]) | |
indices required for shuffling | |
batch_train_x = np.zeros((self.feature_number, self.batch_size)) | |
np ndarray for training batch features | |
int | batch_train_y = np.zeros(self.batch_size) |
np ndarray for training batch of targets | |
random_state = np.random.RandomState(seed) | |
set random generator | |
__init__ | ( | self, | |
train_x, | |||
train_y, | |||
valid_x, | |||
valid_y, | |||
batch_size, | |||
seed = None, | |||
epoch_random_shuffle = True ) |
declaration of class variables
Definition at line 20 of file TfData.py.
batch_iterator | ( | self | ) |
iterator to provide training batches
Definition at line 90 of file TfData.py.
sanitize_labels | ( | self | ) |
checks for a binary classification problem transforms the two class labels to {0,1}
Definition at line 66 of file TfData.py.
batch_train_x = np.zeros((self.feature_number, self.batch_size)) |
int batch_train_y = np.zeros(self.batch_size) |
batches = self.train_x.shape[0] // self.batch_size |
epoch_random_shuffle = epoch_random_shuffle |
random_state = np.random.RandomState(seed) |
train_events = self.train_x.shape[0] |
train_idx = np.zeros(self.train_x.shape[0]) |
valid_events = self.valid_x.shape[0] |