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Tensorflow RNN-LSTM implementation to count number of set bits in a binary string
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| #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() |
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