title: Predict next number in a pattern
body: <p>I am trying to write a simple program using TensorFlow to predict the next number in a sequence.</p>
  
  <p>I am not experienced in TensorFlow so instead of starting from scratch I started with this guide: <a href="http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/" rel="nofollow">http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/</a></p>
  
  <p>However, in contrast to the implementation in the link above I do not want to treat the problem as a classification problem - where I only have n possible outcomes - but instead just calculate a single value for a sequence.</p>
  
  <p>I tried modifying the code to fit my problem:</p>
  
  <pre><code>import numpy as np
  import random
  from random import shuffle
  import tensorflow as tf
  
  NUM_EXAMPLES = 10000
  
  train_input = ['{0:020b}'.format(i) for i in range(2**20)]
  shuffle(train_input)
  train_input = [map(int,i) for i in train_input]
  ti  = []
  for i in train_input:
      temp_list = []
      for j in i:
              temp_list.append([j])
      ti.append(np.array(temp_list))
  train_input = ti
  
  train_output = []
  for i in train_input:
      count = 0
      for j in i:
          if j[0] == 1:
              count+=1
      #temp_list = ([0]*21)
      #temp_list[count]=1
      #train_output.append(temp_list)
      train_output.append(count)
  
  test_input = train_input[NUM_EXAMPLES:]
  test_output = train_output[NUM_EXAMPLES:]
  train_input = train_input[:NUM_EXAMPLES]
  train_output = train_output[:NUM_EXAMPLES]
  
  print "test and training data loaded"
  
  
  target = tf.placeholder(tf.float32, [None, 1])
  data = tf.placeholder(tf.float32, [None, 20,1]) #Number of examples, number of input, dimension of each input
  #target = tf.placeholder(tf.float32, [None, 1])
  
  #print('target shape: ', target.get_shape())
  #print('shape[0]', target.get_shape()[1])
  #print('int(shape) ', int(target.get_shape()[1]))
  
  num_hidden = 24
  cell = tf.nn.rnn_cell.LSTMCell(num_hidden)
  val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
  val = tf.transpose(val, [1, 0, 2])
  
  print('val shape, ', val.get_shape())
  
  last = tf.gather(val, int(val.get_shape()[0]) - 1)
  
  weight = tf.Variable(tf.truncated_normal([num_hidden, int(target.get_shape()[1])]))
  bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))
  
  #prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
  prediction = tf.matmul(last, weight) + bias
  
  cross_entropy = -tf.reduce_sum(target - prediction)
  optimizer = tf.train.AdamOptimizer()
  minimize = optimizer.minimize(cross_entropy)
  
  mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))
  error = tf.reduce_mean(tf.cast(mistakes, tf.float32))
  
  init_op = tf.initialize_all_variables()
  sess = tf.Session()
  sess.run(init_op)
  
  batch_size = 100
  no_of_batches = int(len(train_input)) / batch_size
  epoch = 500
  
  for i in range(epoch):
      ptr = 0
      for j in range(no_of_batches):
          inp, out = train_input[ptr:ptr+batch_size], train_output[ptr:ptr+batch_size]
          ptr+=batch_size
          sess.run(minimize,{data: inp, target: out})
      print "Epoch ",str(i)
  
  incorrect = sess.run(error,{data: test_input, target: test_output})
  
  #print sess.run(prediction,{data: [[[1],[0],[0],[1],[1],[0],[1],[1],[1],[0],[1],[0],[0],[1],[1],[0],[1],[1],[1],[0]]]})
  #print('Epoch {:2d} error {:3.1f}%'.format(i + 1, 100 * incorrect))
  
  sess.close()
  </code></pre>
  
  <p>It is still work in progress, since the input is bogus as well as the cross entropy calculation.</p>
  
  <p>However, my main problem is that the code doesn't compile at all.</p>
  
  <p>I get this error:</p>
  
  <blockquote>
    <p>ValueError: Cannot feed value of shape (100,) for Tensor
    u'Placeholder:0', which has shape '(?, 1)'</p>
  </blockquote>
  
  <p>The number 100 comes from the "batch_size" and the (?, 1) comes from the fact that my prediction is a one dimensional number. However, I do not have any idea where the problem is in my code?</p>
  
  <p>Can anyone help me get the dimensions to match?</p>