def construct_steps()

in ebm_sandbox.py [0:0]

• 25 lines of code
• 6 McCabe index (conditional complexity)

def construct_steps(weights, X, Y_GT, model, target_vars):
    n = 50
    scale_fac = 1.0

    # if FLAGS.task == 'cycleclass':
    #     scale_fac = 10.0

    X_mods = []
    X = tf.identity(X)

    mask = np.zeros((1, 32, 32, 3))

    if FLAGS.task == "boxcorrupt":
        mask[:, 16:, :, :] = 1
    else:
        mask[:, :, :, :] = 1

    mask = tf.Variable(tf.convert_to_tensor(mask, dtype=tf.float32), trainable=False)

    # X_targ = tf.placeholder(shape=(None, 32, 32, 3), dtype = tf.float32)

    for i in range(FLAGS.num_steps):
        X_old = X
        X = X + tf.random_normal(tf.shape(X), mean=0.0, stddev=0.005*scale_fac) * mask

        energy_noise = model.forward(X, weights, label=Y_GT, reuse=True)
        x_grad = tf.gradients(energy_noise, [X])[0]

        if FLAGS.proj_norm != 0.0:
            x_grad = tf.clip_by_value(x_grad, -FLAGS.proj_norm, FLAGS.proj_norm)

        X = X - FLAGS.step_lr * x_grad * scale_fac * mask
        X = tf.clip_by_value(X, 0, 1)

        if i % n == (n-1):
            X_mods.append(X)

        print("Constructing step {}".format(i))

    target_vars['X_final'] = X
    target_vars['X_mods'] = X_mods