# Load the training data and metadata from Gen-1
g1_d = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/test_fd.pickle'))
g1_md = load_pickle(
os.path.join(__DATA_DIR, 'g1-train-test/test_md.pickle'))
# Convert data to time domain, take magnitude, apply window
g1_d = correct_g1_ini_ant_ang(g1_d)
g1_d = np.abs(to_td(g1_d))
g2_d = np.abs(to_td(g2_d))
# Perform data augmentation
g2_d, g2_md = aug_hor_translate(g2_d, g2_md)
g2_d = resize_features_for_keras(g2_d)
g1_d = resize_features_for_keras(g1_d)
g2_labels = to_categorical(get_class_labels(g2_md))
g1_labels = to_categorical(get_class_labels(g1_md))
n_runs = 20
# Init arrays for storing performance metrics
auc_scores = np.zeros([
n_runs,
])
accs = np.zeros([
n_runs,
])
sens = np.zeros([
n_runs,
g1_tr_d = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/train_fd.pickle'))
g1_tr_md = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/train_md.pickle'))
# Set the G1 data to be the validation data
val_data = g1_tr_d
val_md = g1_tr_md
# Correct the initial antenna position used in G1 scans
val_data = correct_g1_ini_ant_ang(val_data)
# Preprocess data, take magnitude and apply time-window, augment
# training dataset
val_data = np.abs(to_td(val_data))
val_data = resize_features_for_keras(val_data)
train_data = np.abs(to_td(train_data))
train_data, train_md = full_aug(train_data, train_md)
train_data = resize_features_for_keras(train_data)
# Get the validation and train set class labels and make categorical
val_labels = get_class_labels(val_md)
val_labels = to_categorical(val_labels)
train_labels = get_class_labels(train_md)
train_labels = to_categorical(train_labels)
# Create arrays for storing the AUC on the train and validation
# sets for this regularization parameter after training with
# correct labels
train_set_aucs = np.zeros([__N_RUNS, __N_EPOCHS])
val_set_aucs = np.zeros([__N_RUNS, __N_EPOCHS])
test_data = g2_d[tar_idxs, :, :]
train_data = g2_d[~tar_idxs, :, :]
test_md = g2_md[tar_idxs]
train_md = g2_md[~tar_idxs]
# Perform data augmentation on the training set here
train_data, train_md = full_aug(train_data, train_md)
# Get class labels for train/test sets here
test_labels = get_class_labels(test_md)
train_labels = get_class_labels(train_md)
test_labels = to_categorical(test_labels)
train_labels = to_categorical(train_labels)
# Resize data for use with keras
test_data = resize_features_for_keras(test_data)
train_data = resize_features_for_keras(train_data)
# Iterate over number of desired runs
for run_idx in range(__N_RUNS):
logger.info('\tWorking on run [%2d / %2d]...' %
(run_idx + 1, __N_RUNS))
# Create the cnn model
model = get_sino_cnn(input_shape=np.shape(train_data)[1:],
lr=0.001)
# Train the model
model.fit(x=train_data,
y=train_labels,
