@@ -65,25 +65,25 @@ def calc_predict_and_activations(wids, tag, words):
6565 pool_out = dy .rectify (pool_out )
6666
6767 scores = (W_sm * pool_out + b_sm ).npvalue ()
68- print '%d ||| %s' % (tag , ' ' .join (words ))
68+ print ( '%d ||| %s' % (tag , ' ' .join (words ) ))
6969 predict = np .argmax (scores )
70- print display_activations (words , activations )
71- print 'scores=%s, predict: %d' % (scores , predict )
70+ print ( display_activations (words , activations ) )
71+ print ( 'scores=%s, predict: %d' % (scores , predict ) )
7272 features = pool_out .npvalue ()
7373 W = W_sm .npvalue ()
7474 bias = b_sm .npvalue ()
75- print ' bias=%s' % bias
75+ print ( ' bias=%s' % bias )
7676 contributions = W * features
77- print ' very bad (%.4f): %s' % (scores [0 ], contributions [0 ])
78- print ' bad (%.4f): %s' % (scores [1 ], contributions [1 ])
79- print ' neutral (%.4f): %s' % (scores [2 ], contributions [2 ])
80- print ' good (%.4f): %s' % (scores [3 ], contributions [3 ])
81- print 'very good (%.4f): %s' % (scores [4 ], contributions [4 ])
77+ print ( ' very bad (%.4f): %s' % (scores [0 ], contributions [0 ]) )
78+ print ( ' bad (%.4f): %s' % (scores [1 ], contributions [1 ]) )
79+ print ( ' neutral (%.4f): %s' % (scores [2 ], contributions [2 ]) )
80+ print ( ' good (%.4f): %s' % (scores [3 ], contributions [3 ]) )
81+ print ( 'very good (%.4f): %s' % (scores [4 ], contributions [4 ]) )
8282
8383
8484def display_activations (words , activations ):
85- pad_begin = ( WIN_SIZE - 1 ) / 2
86- pad_end = WIN_SIZE - 1 - pad_begin
85+ pad_begin = int (( WIN_SIZE - 1 ) / 2 )
86+ pad_end = int ( WIN_SIZE - 1 - pad_begin )
8787 words_padded = ['pad' for i in range (pad_begin )] + words + ['pad' for i in range (pad_end )]
8888
8989 ngrams = []
@@ -121,4 +121,5 @@ def display_activations(words, activations):
121121
122122for words , wids , tag in dev :
123123 calc_predict_and_activations (wids , tag , words )
124- raw_input ()
124+ # input prompt so that the next example is revealed on a key press
125+ input ()
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