title: How can adjust train-set and test-set and validation-set for Keras in RNN?
body: <p>I'm playing with the dataset which are time-series and by help of reshape I fetch them to RNN.</p>
  
  <p>The dataset contains 40 time-steps <a href="https://drive.google.com/file/d/1YgbhwIgnW42bwOnaxM2e_JHwsf3Q9O2_/view?usp=sharing" rel="nofollow noreferrer">dataset</a> I hope each time 1 row which contains 1440 columns fetch to RNN and get trained andin the end 40 times (1, 1440) feed and learn in RNN.</p>
  
  <pre><code>X_train size: (40, 1, 1440)
  X_test size: (40, 1, 1440)
  </code></pre>
  
  <p><strong>Problem</strong> is I want to split dataset into : 
  <strong>data-set</strong> , <strong>validation-set</strong> and <strong>test-set</strong> so that I can predict after <strong>timestep=t</strong> and plot the output.</p>
  
  <p><img src="https://i.imgur.com/ZZEVdeU.jpg" alt="img"> I already read @YSelf <a href="https://stackoverflow.com/a/48811058/10452700">answer</a> and <a href="https://stackoverflow.com/a/55847067/10452700">the reason why</a> it's helpful but unfortunately I haven't set up successfully yet due to structure of data. 
  is I want to provide the condition that I can adjust in implementation scripts so that let's say I'm interested in predicting timesteps after 35th row or time-step till end. How I can fulfill it and how I can demonstrate its plot?</p>
  
  <p>my code is following:</p>
  
  <pre class="lang-py prettyprint-override"><code>import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from keras.layers import Dense , Activation , BatchNormalization
  from keras.layers import LSTM,SimpleRNN
  from keras.models import Sequential
  from keras.optimizers import Adam, RMSprop
  
  data_train = pd.read_csv("D:\Train.csv", header=None)
  
  #select interested columns to predict 980 out of 1440 for prediction
  j=0
  index=[]
  for i in range(1439):
      if j==2:
          j=0
          continue
      else:
          index.append(i)
          j+=1
  
  Y_train = data_train[index]
  Y_test  = data_test[index]
  data_train = data_train.values
  data_test  = data_test.values
  X_train = data_train .reshape((data_train.shape[0], 1,data_train.shape[1]))
  X_test  = data_test  .reshape((data_test.shape[0] , 1 ,data_test.shape[1]))
  
  
  # create and fit the SimpleRNN model
  model_RNN = Sequential()
  model_RNN.add(SimpleRNN(units=1440, input_shape=(X_train.shape[1], X_train.shape[2]))) #in real data units=1440
  model_RNN.add(Dense(960)) # in real data Dense(960)
  model_RNN.add(BatchNormalization())
  model_RNN.add(Activation('tanh'))
  model_RNN.compile(loss='mean_squared_error', optimizer='adam')
  hist_RNN=model_RNN.fit(X_train[:30, :], Y_train[30:, :], epochs =50, batch_size =20,validation_data=(X_train[:30, :], Y_train[30:, :]),verbose=1)
  </code></pre>
  
  <p>I would really appreciate it if someone could explain answer of questions. Thanks in advance.</p>