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import numpy as np
import re
from sklearn.model_selection import train_test_split
import tensorflow as tf
input_seed = 1234
time_steps = 24
n_samples = 100000
X = np.random.randint(1,30,n_samples*time_steps).reshape(n_samples, time_steps)
Y = np.apply_along_axis(lambda x : x + 10,1,X)
X = X.reshape(X.shape[0],X.shape[1],1)
Y = X.reshape(Y.shape[0],Y.shape[1],1)
np.random.seed(input_seed)
idx     = np.arange(len(X))
np.random.shuffle(idx)
X, Y    = X[idx,:,:], Y[idx,:,:]
X_train, X_valid, Y_train, Y_valid = train_test_split(X,Y,test_size=0.25, random_state = input_seed)

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tf.reset_default_graph()
hidden_units  = 32
tf_X          = tf.placeholder(tf.float32, shape=[None, time_steps, 1])
tf_Y          = tf.placeholder(tf.float32, shape=[None, time_steps, 1])
rnn_cell      = tf.contrib.rnn.OutputProjectionWrapper(tf.contrib.rnn.BasicRNNCell(
                                                        num_units= hidden_units,
                                                        activation=tf.nn.relu),
                                                    output_size = 1)
outputs, states  =tf.nn.dynamic_rnn(rnn_cell, inputs = tf_X,
                                   dtype=tf.float32)
loss           = tf.square(outputs - tf_Y)
total_loss     = tf.reduce_mean(loss)
learning_rate  = 0.001
optimizer      = tf.train.AdamOptimizer(learning_rate= learning_rate).minimize(loss=total_loss)
batch_size =1000
n_batches = int(X_train.shape[0]/batch_size)
epochs = 20
i      = 0

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with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for e in range(epochs):
      idx     = np.arange(len(X_train))
      np.random.shuffle(idx)
      X_train, Y_train    = X_train[idx,:,:], Y_train[idx]
      for i in range(n_batches):
          x  = X_train[(i*batch_size):((i+1)*batch_size),:,:]
          y  = Y_train[(i*batch_size):((i+1)*batch_size),:,:]
          _, curr_loss = sess.run([optimizer, total_loss],
                                 feed_dict={tf_X:x, tf_Y:y})
          #print("Epoch:",str(e), "Batch:",str(i), "loss:",str(curr_loss))
      loss_val,output_val = sess.run([total_loss,outputs], feed_dict={tf_X:X_valid, tf_Y:Y_valid})
      print("Epoch:",str(e), " Loss:", loss_val)

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Epoch: 0  Loss: 58.563866
Epoch: 1  Loss: 17.88254
Epoch: 2  Loss: 6.5560427
Epoch: 3  Loss: 4.6802044
Epoch: 4  Loss: 3.2721703
Epoch: 5  Loss: 2.1835458
Epoch: 6  Loss: 1.3963941
Epoch: 7  Loss: 0.8322729
Epoch: 8  Loss: 0.48023996
Epoch: 9  Loss: 0.28172782
Epoch: 10  Loss: 0.17143281
Epoch: 11  Loss: 0.11465253
Epoch: 12  Loss: 0.08481874
Epoch: 13  Loss: 0.07153204
Epoch: 14  Loss: 0.056274623
Epoch: 15  Loss: 0.050688446
Epoch: 16  Loss: 0.04169278
Epoch: 17  Loss: 0.03851114
Epoch: 18  Loss: 0.033528958
Epoch: 19  Loss: 0.03168451

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import matplotlib.pyplot as plt
plt.scatter(np.arange(600000),output_val.flatten() -Y_valid.flatten())
plt.show()