def load_vehicle_data():
# Load data from file
# Make sure that vehicles.dat is in data/
train_x, train_y, test_x, test_y = get_vehicle_data()
num_train = train_x.shape[0]
num_test = test_x.shape[0]
#generate_unit_testcase(train_x.copy(), train_y.copy())
# Normalize our data: choose one of the two methods before training
#train_x, test_x = normalize_all_pixel(train_x, test_x)
train_x, test_x = normalize_per_pixel(train_x, test_x)
# Reshape our data
# train_x: shape=(2400, 64, 64) -> shape=(2400, 64*64)
# test_x: shape=(600, 64, 64) -> shape=(600, 64*64)
train_x = reshape2D(train_x)
test_x = reshape2D(test_x)
# Pad 1 as the last feature of train_x and test_x
train_x = add_one(train_x)
test_x = add_one(test_x)
return train_x, train_y, test_x, test_y
# gt_zero = (x > zeros)
# relu_logits = tf.where(gt_zero, x, zeros)
# neg_abs = tf.where(gt_zero, -x, x)
# c = tf.reduce_mean(relu_logits - tf.multiply(x, L) + tf.log(1 + tf.exp(neg_abs)))
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
labels=y,
logits=logits))
if __name__ == "__main__":
np.random.seed(2018)
tf.set_random_seed(2018)
# Load data from file
# Make sure that vehicles.dat is in data/
train_x, train_y, test_x, test_y = get_vehicle_data()
num_train = train_x.shape[0]
num_test = test_x.shape[0]
#generate_unit_testcase(train_x.copy(), train_y.copy())
#logistic_unit_test()
# Normalize our data: choose one of the two methods before training
#train_x, test_x = normalize_all_pixel(train_x, test_x)
train_x, test_x = normalize_per_pixel(train_x, test_x)
# Reshape our data
# train_x: shape=(2400, 64, 64) -> shape=(2400, 64*64)
# test_x: shape=(600, 64, 64) -> shape=(600, 64*64)
train_x = reshape2D(train_x)
test_x = reshape2D(test_x)
test_dict['y_hat'] = y_hat
test_dict['loss'] = loss
test_dict['grad'] = grad
testcase['output'].append(test_dict)
np.save('./data/logistic_unittest.npy', testcase)
if __name__ == "__main__":
np.random.seed(2018)
# Load data from file
# Make sure that vehicles.dat is in data/
train_x, train_y, test_x, test_y = get_vehicle_data()
num_train = train_x.shape[0]
num_test = test_x.shape[0]
#generate_unit_testcase(train_x.copy(), train_y.copy())
#logistic_unit_test()
# Normalize our data: choose one of the two methods before training
#train_x, test_x = normalize_all_pixel(train_x, test_x)
train_x, test_x = normalize_per_pixel(train_x, test_x)
# Reshape our data
# train_x: shape=(2400, 64, 64) -> shape=(2400, 64*64)
# test_x: shape=(600, 64, 64) -> shape=(600, 64*64)
train_x = reshape2D(train_x)
test_x = reshape2D(test_x)
