Merge master

Author: dmitrinesterenko

assignment3/rnn.py

@@ -1,6 +1,7 @@
 import sys
 import os
 import numpy as np
+#import matplotlib.pyplot as plt
 import math
 import time
 import itertools
@@ -187,17 +188,19 @@ def add_model(self, node):
         node_tensors[node] = curr_node_tensor
         return node_tensors
 
-
     def add_projections(self, node_tensors):
         """Add projections to the composition vectors to compute the raw sentiment scores
 
         Hint: Reuse the "Projection" variable_scope here
         Args:
             node_tensors: tensor(?, embed_size)
+<<<<<<< HEAD
             When we are evaluating just on the root node then the first
             dimension would be 1, if we are looking through a hierarchy of terms in a
             sentence then that would be equal to the number of words we want to include i.e.
             5
+=======
+>>>>>>> master
         Returns:
             output: tensor(?, label_size)
         """
@@ -339,6 +342,7 @@ def run_epoch(self, new_model = False, verbose=50):
                     if i == 0: # this is GLORIOUS :(
                         self.add_model_vars()
                     # Define training operations in the graph
+
                     tree = self.train_data[step]
                     logits = self.inference(tree)
                     labels = [l for l in tree.labels if l!=2]
@@ -495,5 +499,13 @@ def test_RNN():
     test_acc = np.equal(predictions, labels).mean()
     print('Test acc: {}'.format(test_acc))
 
+    #plt.plot(stats['loss_history'])
+    #plt.title('Loss history')
+    #plt.xlabel('Iteration')
+    #plt.ylabel('Loss')
+    #plt.savefig("loss_history.png")
+    #plt.show()
+
+
 if __name__ == "__main__":
         test_RNN()

assignment3/tree.py

@@ -81,6 +81,7 @@ def get_words(self):
 def leftTraverse(node, nodeFn=None, args=None):
     """
     Recursive function traverses tree
+<<<<<<< HEAD
     from left to right.
     Calls nodeFn at each node
     """
@@ -149,7 +150,7 @@ def simplified_data(num_train, num_dev, num_test):
     rndstate = random.getstate()
     random.seed(0)
     trees = loadTrees('train') + loadTrees('dev') + loadTrees('test')
-    
+
     #filter extreme trees
     pos_trees = [t for t in trees if t.root.label==4]
     neg_trees = [t for t in trees if t.root.label==0]

assignment3/utils.py

@@ -22,7 +22,6 @@ def construct(self, words):
             self.add_word(word)
         self.total_words = float(sum(self.word_freq.values()))
         print('{} total words with {} uniques'.format(self.total_words, len(self.word_freq)))
-
     def encode(self, word):
         if word not in self.word_to_index:
             word = self.unknown