Predict next number in a pattern - tensorflow

Question

I am trying to write a simple program using TensorFlow to predict the next number in a sequence.
I am not experienced in TensorFlow so instead of starting from scratch I started with this guide: http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
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.
I tried modifying the code to fit my problem:
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()
It is still work in progress, since the input is bogus as well as the cross entropy calculation.
However, my main problem is that the code doesn't compile at all.
I get this error:
ValueError: Cannot feed value of shape (100,) for Tensor
u'Placeholder:0', which has shape '(?, 1)'
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?
Can anyone help me get the dimensions to match?

Answer 1

This error means your targets placeholder is being fed something with the wrong shape. To fix it, I think you should reshape something like test_output.reshape([-1, 1])

Answer 2

To fix the placeholders shape, change your code to
for i in range(epoch):
ptr = 0
for j in range(no_of_batches):
inp = train_input[ptr:ptr+batch_size]
out = train_output[ptr:ptr+batch_size]
ptr+=batch_size
out = np.reshape(out, (100,1)) #reshape
sess.run(minimize,{data: inp, target: out})
print ("Epoch ",str(i))
test_output = np.reshape(test_output, (1038576,1)) #reshape
incorrect = sess.run(error,{data: test_input, target: test_output})