Added RNN (LSTM) example

examples/rnn-lstm-example/create_input_data.py

@@ -0,0 +1,39 @@
+maxSeqLength = 250
+batchSize = 24
+
+import numpy as np
+import tensorflow as tf
+import re
+
+wordsList = np.load('other_data/wordsList.npy').tolist()
+wordsList = [word.decode('UTF-8') for word in wordsList]
+wordVectors = np.load('other_data/wordVectors.npy')
+strip_special_chars = re.compile("[^A-Za-z0-9 ]+")
+
+def cleanSentences(string):
+    string = string.lower().replace("<br />", " ")
+    return re.sub(strip_special_chars, "", string.lower())
+
+def getSentenceMatrix(sentence):
+    arr = np.zeros([batchSize, maxSeqLength])
+    sentenceMatrix = np.zeros([batchSize,maxSeqLength], dtype='int32')
+    cleanedSentence = cleanSentences(sentence)
+    split = cleanedSentence.split()
+    for indexCounter,word in enumerate(split):
+        try:
+            sentenceMatrix[0,indexCounter] = wordsList.index(word)
+        except ValueError:
+            sentenceMatrix[0,indexCounter] = 399999
+    return sentenceMatrix
+
+inputText = "That movie was terrible."
+inputMatrix = getSentenceMatrix(inputText)
+print inputMatrix
+print inputMatrix.shape
+np.savetxt("inputMatrixNegative.csv", inputMatrix, delimiter=',', fmt="%i")
+
+secondInputText = "That movie was the best one I have ever seen."
+secondInputMatrix = getSentenceMatrix(secondInputText)
+print secondInputMatrix
+print secondInputMatrix.shape
+np.savetxt("inputMatrixPositive.csv", secondInputMatrix, delimiter=',', fmt="%i")

examples/rnn-lstm-example/freeze.py

@@ -0,0 +1,24 @@
+import tensorflow as tf
+import os
+
+model_dir = './model/'
+output_node_names = 'add'
+
+checkpoint = tf.train.get_checkpoint_state(model_dir)
+input_checkpoint = checkpoint.model_checkpoint_path
+
+absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1])
+output_graph = absolute_model_dir + "/frozen_model_lstm.pb"
+
+clear_devices = True
+
+with tf.Session(graph=tf.Graph()) as sess:
+    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)
+    saver.restore(sess, input_checkpoint)
+    output_graph_def = tf.graph_util.convert_variables_to_constants(sess, tf.get_default_graph().as_graph_def(), output_node_names.split(",")) 
+
+    with tf.gfile.GFile(output_graph, "wb") as f:
+         f.write(output_graph_def.SerializeToString())
+
+    print("%d ops in the final graph." % len(output_graph_def.node))
+