def train_face_classifier(ntrain, ntest, orientations, wrap180,
model_save_file):
print("Loading training data...")
descriptors_train, classes_train = get_training_data(ntrain,
orientations,
wrap180=wrap180)
print("Finished loading training data.")
print("Loading test data...")
descriptors_test, classes_test = get_testing_data(ntest,
orientations,
wrap180=wrap180)
print("Finished loading test data.")
print("Start training...")
start_time = time.time()
params, _ = logistic_fit(descriptors_train, classes_train)
print("Training took {} seconds.".format(time.time() - start_time))
np.save(model_save_file, params)
predicted_train = logistic_prob(descriptors_train, params)
plot_errors(predicted_train, classes_train, is_training=True)
train_success_rate = classification_rate(predicted_train, classes_train)
print("Training classification rate: {}".format(train_success_rate))
predicted_test = logistic_prob(descriptors_test, params)
plot_errors(predicted_test, classes_test, is_training=False)
test_success_rate = classification_rate(predicted_test, classes_test)
print("Testing classification rate: {}".format(test_success_rate))
def test_face_classifier(ntrain, ntest, orientations, wrap180,
model_save_file):
print("Loading test data...")
descriptors_test, classes_test = get_testing_data(ntest,
orientations,
wrap180=wrap180)
print("Finished loading test data.")
params = np.load(model_save_file)
predicted_test = logistic_prob(descriptors_test, params)
plot_errors(predicted_test, classes_test, is_training=False)
test_success_rate = classification_rate(predicted_test, classes_test)
print("Testing classification rate: {}".format(test_success_rate))
ntest = int(sys.argv[1]); del sys.argv[1]
elif option == "-deskew":
deskew = int(sys.argv[1]); del sys.argv[1]
elif option == "-normalize":
normalize = int(sys.argv[1]); del sys.argv[1]
elif option == "-lsquared":
lsquared = float(sys.argv[1]); del sys.argv[1]
else:
print sys.argv[0], ": invalid option", option
sys.exit(1)
print "Gaussian Processes"
print
print "Reading data..."
# reading the data and applying configured pre-processing steps
X_train, T_train = data.get_training_data(ntrain, normalize=normalize, deskew=deskew)
X_test, T_test = data.get_testing_data(ntest, normalize=normalize, deskew=deskew)
print "{0} training data read".format(len(X_train))
print "{0} testing data read".format(len(X_test))
print
# running a Gaussian process on training and testing sets, with "lsquared"
T_predicted = gaussian_process(X_train, T_train, X_test, lsquared=lsquared)
# evaluating the model performance on the testing set
print "Testing Set Error: {0:.3f}".format(
get_error_score(T_predicted, T_test)
)
print
print sys.argv[0], ": invalid option", option
sys.exit(1)
np.seterr(over="ignore", divide="ignore")
print "Neural Networks"
print
print "Reading data..."
# reading the data, applying configured pre-processing, and adding 1.0 to each vector as a bias input
X_train, T_train = data.get_training_data(ntrain,
normalize=normalize,
deskew=deskew,
add_ones=True)
X_test, T_test = data.get_testing_data(ntest,
normalize=normalize,
deskew=deskew,
add_ones=True)
print "{0} training data read".format(len(X_train))
print "{0} testing data read".format(len(X_test))
print
input_dim = X_train.shape[1]
output_dim = T_train.max() + 1
weights, errors, params = [], [], []
print "{0:40}\tV. Loss\t\tV. Error".format(
"(Func, Hidden, Batch, Learn, Drop)")
print "-------------------------------------------------------------------------------"
for function in functions:
for hidden in hidden_units:
def main(_):
test_x_batches, test_y_batches = get_testing_data(FLAGS.batch_size *
FLAGS.time_steps)
run_testing(test_x_batches, test_y_batches)