my toy tensorflow rnn project doesn't works. cost doesn't decrease

    import numpy as np
    import tensorflow as tf
    sess = tf.Session()

    x = [[1,0,0,0],# 4*4  #h
            [0,1,0,0], #e
            [0,0,1,0], #l
            [0,0,0,1]] #o

    y = [[0,1,0,0], # 5*4
            [0,0,1,0],
            [0,0,1,0],
            [0,0,0,1]]

    c_ = 0.5 * tf.constant([[1,0,0,0], # 4*4
                            [0,2,0,0],
                            [0,0,3,0],
                            [0,0,0,2]], dtype=tf.float32)

    h_ = 0.5 * tf.constant([[1,0,0,0], # 4*4
                           [0,1,0,0],
                           [0,0,1,0],
                           [0,0,0,1]], dtype=tf.float32)

    X = tf.placeholder(dtype=tf.float32, shape=[None, 4])
    Y = tf.placeholder(dtype=tf.float32, shape=[None, 4])
    W = tf.Variable(tf.random_normal([4, 1]))
    b = tf.Variable(tf.random_normal([1]))
    sess.run(tf.global_variables_initializer())

    #he
    #el
    #ll
    #lo

    seq_len = 4
    num_units = 4

    class lstm:
        def build(c, h):
            args = tf.concat((X,h), axis=1)

            out_size = 4 * num_units
            proj_size = args.shape[-1]

            weights = tf.ones([proj_size, out_size]) * 0.5

            out = tf.matmul(args, weights)

            bias = tf.ones([out_size]) * 0.5

            concat = out + bias

            i, j, f, o = tf.split(concat, 4, 1)

            g = tf.tanh(j)

            def sigmoid_array(x):
                return 1 / (1 + tf.exp(-x))

            forget_bias = 1.0

            sigmoid_f = sigmoid_array(f + forget_bias)

            sigmoid_array(i) * g

            new_c = c * sigmoid_f + sigmoid_array(i) * g

            new_h = tf.tanh(new_c) * sigmoid_array(o)

            return new_c, new_h

    ta_c = tf.TensorArray(size=seq_len, dtype=tf.float32)
    ta_h = tf.TensorArray(size=seq_len, dtype=tf.float32)

    def body(last_state, last_output, step, ta_c, ta_h):

        output = lstm.build(last_state, last_output)[0]
        state = lstm.build(last_state, last_output)[1]
        ta_c = ta_c.write(step, state)
        ta_h = ta_h.write(step, output)
        return state, output, tf.add(step, 1), ta_c, ta_h


    timesteps = seq_len

    steps = lambda a, b, step, c, d: tf.less(step, timesteps)

    lstm_output, lstm_state, step, ta_c, ta_h = tf.while_loop(steps, body, (c_, h_, 0, ta_c, ta_h), parallel_iterations=20)

    output = lstm_output[-1]
    output = tf.reshape(output, [-1, 4])
    logits = tf.matmul(lstm_output, W) + b

    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y))
    train = tf.train.AdamOptimizer(0.1).minimize(cost)

    is_correct = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))

    sess.run(tf.global_variables_initializer())

    for i in range(1000):
        sess.run(train, feed_dict={X:x, Y:y})
        a, c = sess.run([accuracy, cost],feed_dict={X:x, Y:y})
        print("##############",a)
        print("##############",c)

It is a excuse, I have been holding this code for a few days, but I have no idea why the cost does not go down.

############## 0.0
############## 1.3862944
############## 0.0
############## 1.3862944
############## 0.0
############## 1.3862944
############## 0.0
############## 1.3862944
############## 0.0
############## 1.3862944

these are accuracy and cost. As you can see, it doesn't works at all.

I thought I put the X placeholder inside the "class" and connected the new output and rnn cell, but it was not. What should I fix?

please, please help me