text: Title: Multiple encoded outputs into a single classification network
  Body: ![networksketch](https://user-images.githubusercontent.com/29553460/27308621-9efe609c-551d-11e7-81c7-0cbfc898f266.png)
  
  I have multiple data sets, where data sets are distinguished by feature length of a feature vector.
  Regardless input feature size, I would like the network to be able to classify one of the two classes given the input. Hence, I would like to train an encoder for each data set (will train n encoders for n datasets), but pass the encoded outputs from all encoders to a single classification network since the encoded outputs are of the same dimension. See attached image for the network sketch.
  
  I have code written below so far, 
  1. but I am not really sure if the current way of setting up the regression is valid or need to use merge with some sort of 'mean' to average classification results from different data sets.
  2. I am also having trouble figuring out how to perform training given the situation.
  
  Any help will be greatly appreciated. 
  
  ```
  import h5py
  import numpy as np
  import tflearn
  
  STEPS_PER_EPOCH = 10
  NUM_CLASSES = 2
  
  train_Xs = []
  train_Ys = []
  batch_sizes = []
  encodings = []
  
  encode_hidden_1 = 500
  classify_hidden_1 = 500
  classify_hidden_2 = 100
  for trainDataset in trainDatasets:
      train_file = h5py.File(trainDataset,'r')
      train_X = np.array(train_file['features'])
      train_Y = np.array(train_file['label'])
      train_Xs(train_X) # number of samples x number of features
      train_Ys(train_Y) # number of samples x 2 (two classes)
      nb_samples = train_X.shape[0]
      nb_features = train_X.shape[1]
      batch_size = int(nb_samples/STEPS_PER_EPOCH) 
      # batch size is determined by the number of samples in each dataset
      
      encoder = tflearn.input_data(shape=[None, nb_features])
      encoder = tflearn.fully_connected(encoder, encode_hidden_1)
      encodings.append(encoder)   
  
  classifiers_1 = []
  classifiers_2 = []
  softmax_outputs = []
  for encoding in encodings:
      classifier1 = tflearn.fully_connected(encoding, classify_hidden_1, activation='relu')
      classifiers_1.append(classifier1)
      classifier2 = tflearn.fully_connected(classifiers1, classify_hidden_2, activation='relu')
      classifiers_2.append(classifier2)
      softmax = tflearn.fully_connected(classifier2, 2, activation='softmax')
      softmax_outputs.append(softmax)
  
  network = regression(softmax_outputs, optimizer = 'momentum', loss='categorical_crossentropy', learning_rate=0.1)
  
  ```
  
   
  
   