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Tensorflow RNN-LSTM implementation to count number of set bits in a binary string
#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf
# from tensorflow.models.rnn import rnn_cell
# from tensorflow.models.rnn import rnn
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)
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"
data = tf.placeholder(tf.float32, [None, 20,1]) #Number of examples, number of input, dimension of each input
target = tf.placeholder(tf.float32, [None, 21])
num_hidden = 24
cell = tf.nn.rnn_cell.LSTMCell(num_hidden,state_is_tuple=True)
val, _ = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
val = tf.transpose(val, [1, 0, 2])
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)
cross_entropy = -tf.reduce_sum(target * tf.log(tf.clip_by_value(prediction,1e-10,1.0)))
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 = 1000
no_of_batches = int(len(train_input)) / batch_size
epoch = 5000
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()
@martianmartian

seriously this code has not been optimized at all. can't even run it

@Fahad615
Fahad615 commented Mar 7, 2017

The code is not working.

@Fahad615
Fahad615 commented Mar 7, 2017

I got the following error: "tensorflow.python.ops.nn' has no attribute 'rnn_cell"

I think It was moved to contrib. Take a look here

@mg64ve
mg64ve commented Mar 11, 2017 edited

This code runs on TensorFlow 0.12
What TensorFlow version are your using guys?

@Fahad615

I'm using TensorFlow 1.0.0

@rickysahu
rickysahu commented Mar 21, 2017 edited

on TF 1.0.0, the only thing you need to change is Line 44 which should be this:
cell = tf.contrib.rnn.LSTMCell(num_hidden,state_is_tuple=True)
Also, you probably dont need to run all 5000 epochs, my machine got to 0.1% error in 600.

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