because I have this error in Tensorflow?

import tensorflow as tf
import numpy as np

# Dataset
x_data = np.array([[1.,0.,2], [0.,1.,3], [1.,0.,2], [1.,1.,4]])
y_data = np.array([[3.], [0.], [1.], [2.]])

# Hyperparamters
n_input = 3
n_hidden = 10
n_output = 1
lr = 0.01
epochs = 10000
display_step = 200

# Placeholders 
X = tf.placeholder(tf.float32,[None, n_input ])
Y = tf.placeholder(tf.float32,[None, n_output])

# Weights 
W1 = tf.Variable(tf.random_uniform([n_input, n_hidden] , name="W_layer1"))
W2 = tf.Variable(tf.random_uniform([n_hidden, n_output], name="W_layer2"))

#bias
b1 = tf.Variable(tf.random_normal ([n_hidden]), name="b_layer1") 
b2 = tf.Variable(tf.random_normal ([n_output]), name="b_layer2")

L2 = tf.sigmoid(tf.matmul(X, W1) + b1)
hy = tf.sigmoid(tf.matmul(L2, W2) + b2)

cost = tf.reduce_mean(-Y*tf.log(hy) - (1-Y) * tf.log(1-hy))

optimizer = tf.train.GradientDescentOptimizer(lr).minimize(cost)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

       for step in range(epochs):
           _, c = sess.run([optimizer, cost], feed_dict = {X: x_data, Y: y_data})

        if step % 200 == 0:
            print(step, c)

predicted = tf.cast(hy > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
# Accuracy report
h, c, a = sess.run([hy, predicted, accuracy], feed_dict={X: x_data, Y: y_data})
print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)

  #Al correr el codigo tengo este error
  0 -2.18759
  1000 nan
  2000 nan
  3000 nan
  4000 nan
  5000 nan
  6000 nan
  7000 nan
  8000 nan
  9000 nan

 Hypothesis:  [[ nan]
  [ nan]
  [ nan]
  [ nan]] 
 Correct (Y):  [[ 0.]
  [ 0.]
  [ 0.]
  [ 0.]] 
 Accuracy:  0.25