From b6506c9065d7a1d763046f9f88cd5b66784338cf Mon Sep 17 00:00:00 2001
From: tgupta6 <tgupta6@illinois.edu>
Date: Mon, 12 Sep 2016 15:42:25 -0500
Subject: [PATCH] word vector transformation works for obj atr classification
---
constants_crunchy.py | 6 ++---
word2vec/word_vector_management.py | 41 ++++++++++++++++++++++++------
2 files changed, 36 insertions(+), 11 deletions(-)
diff --git a/constants_crunchy.py b/constants_crunchy.py
index 6d46dad..bf4a63b 100644
--- a/constants_crunchy.py
+++ b/constants_crunchy.py
@@ -5,7 +5,7 @@ def mkdir_if_not_exists(dir_name):
if not os.path.exists(dir_name):
os.mkdir(dir_name)
-experiment_name = 'object_attribute_classifier_large_images_vqa_split' #'QA_joint_pretrain_genome_split'
+experiment_name = 'object_attribute_classifier_wordvec_xform' #'QA_joint_pretrain_genome_split'
# Global output directory (all subexperiments will be saved here)
global_output_dir = '/home/tanmay/Code/GenVQA/Exp_Results/VQA'
@@ -117,7 +117,7 @@ region_model = os.path.join(
'model')
# Object Attribute Finetuning Params
-region_fine_tune_from_iter = 34000
+region_fine_tune_from_iter = 50500
region_fine_tune_from = region_model + '-' + str(region_fine_tune_from_iter)
# Object Attribute Model Selection
@@ -129,7 +129,7 @@ region_model_accuracies_txt = os.path.join(
# Object Attribute Classifier Evaluation Params
region_eval_on = 'train_held_out' # One of {'test','train_held_out','train_subset'}
-region_model_to_eval = region_model + '-' + '80000'
+region_model_to_eval = region_model + '-' + '102000'
region_attribute_scores_dirname = os.path.join(
region_output_dir,
diff --git a/word2vec/word_vector_management.py b/word2vec/word_vector_management.py
index 4dd1222..ad91251 100644
--- a/word2vec/word_vector_management.py
+++ b/word2vec/word_vector_management.py
@@ -29,16 +29,37 @@ class word_vector_manager():
with tf.variable_scope('attribute_label_word_vectors'):
self.attribute_label_word_vectors()
-
-
+
+ def word_vec_transformation(self, word_vecs):
+ with tf.variable_scope('word_vec_transform'):
+ word_vecs_fc1 = layers.full(
+ word_vecs,
+ constants.word_vector_size,
+ 'word_vec_fc1')
+
+ word_vecs_fc2 = layers.full(
+ word_vecs_fc1,
+ constants.word_vector_size,
+ 'word_vec_fc2',
+ func = None)
+
+ return word_vecs_fc2
+
def init_word_vector_tensor(self):
-# with tf.device('/cpu:0'):
- self.word_vectors = tf.get_variable(
- name = 'word_vector',
- shape = [self.vocab_size, constants.word_vector_size],
- initializer = tf.constant_initializer(self.vocab_word_vectors))
+ # self.word_vectors_ = tf.get_variable(
+ # name = 'word_vector',
+ # shape = [self.vocab_size, constants.word_vector_size],
+ # initializer = tf.constant_initializer(self.vocab_word_vectors))
+
+ self.pretrained_word_vectors = tf.constant(
+ self.vocab_word_vectors,
+ name = 'pretrained_word_vectors'
+ )
+
+ self.word_vectors = self.word_vec_transformation(
+ self.pretrained_word_vectors)
- tf.add_to_collection('to_regularize', self.word_vectors)
+ #tf.add_to_collection('to_regularize', self.word_vectors)
def read_object_labels(self):
with open(constants.object_labels_json, 'r') as file:
@@ -88,6 +109,7 @@ class word_vector_manager():
self.object_label_vectors = tf.concat(
0, object_label_vector_list)
+
def attribute_label_word_vectors(self):
inv_attribute_labels = {v: k for k, v in self.attribute_labels.items()}
num_attribute_labels = len(inv_attribute_labels)
@@ -101,6 +123,9 @@ class word_vector_manager():
self.attribute_label_vectors = tf.concat(
0, attribute_label_vector_list)
+ def get_trainable_variables():
+ return var_collect.collect_scope('word_vec_transform')
+
if __name__=='__main__':
word_vector_mgr = word_vector_manager()
--
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