tf.reset_default_graph()

x = tf.placeholder(dtype=tf.float32)

N = 1
M = 1

W = tf.Variable(tf.zeros([N, M]))
b = tf.Variable(tf.zeros([M]))

y = tf.sigmoid(W*x + b)
loss = (x - y)**2

grad = tf.gradients(loss, [W, b])

session = tf.Session()
session.run(tf.initialize_all_variables())
session.run(grad, feed_dict={x: [1]})

[array([[-0.25]], dtype=float32), array([-0.25], dtype=float32)]