def test():
trainer = algos.algos_factory.gen_tranier(FLAGS.algo)
input_app = InputApp.InputApp()
sess = input_app.sess = tf.InteractiveSession()
input_results = input_app.gen_input()
#--------train
image_name, image_feature, text, text_str = input_results[
input_app.input_train_name]
#--------train neg
neg_text, neg_text_str = input_results[input_app.input_train_neg_name]
loss = trainer.build_train_graph(image_feature, text, neg_text)
eval_names = ['loss']
print('eval_names:', eval_names)
test_flow([loss],
names=eval_names,
gen_feed_dict=input_app.gen_feed_dict,
model_dir=FLAGS.model_dir,
num_interval_steps=FLAGS.num_interval_steps,
num_epochs=FLAGS.num_epochs,
eval_times=FLAGS.eval_times,
sess=sess)
def test():
trainer = algos_factory.gen_tranier(FLAGS.algo)
input_app = InputApp.InputApp()
sess = input_app.sess = tf.InteractiveSession()
#init_op = tf.group(tf.global_variables_initializer(),
# tf.local_variables_initializer())
#sess.run(init_op)
input_results = input_app.gen_input()
#--------train
image_name, image_feature, text, text_str, input_text, input_text_str = input_results[
input_app.input_valid_name]
#How to get ride of main scope, not use it ? use make_template to make auto share
with tf.variable_scope(FLAGS.main_scope):
loss = trainer.build_train_graph(image_feature, input_text, text)
eval_names = ['loss']
print('eval_names:', eval_names)
ops = [loss]
test_flow(ops,
names=eval_names,
model_dir=FLAGS.model_dir,
num_interval_steps=FLAGS.num_interval_steps,
num_epochs=FLAGS.num_epochs,
eval_times=FLAGS.eval_times,
sess=sess)
def train():
with tf.Session() as sess:
input_app = InputApp.InputApp()
input_results = input_app.gen_input(train_only=True)
query, query_str, text, text_str = input_results[
input_app.input_train_name]
neg_text, neg_text_str = input_results[input_app.input_train_neg_name]
#print input_results[input_app.input_train_neg_name]
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step = 0
try:
while not coord.should_stop():
start_time = time.time()
step = step + 1
#a,b,c,d,e,f = sess.run([query_str[0],text_str[0],neg_text_str[0],query[0],text[0],neg_text[0]])
a,b,c,d,e,f,g,h,i,j,k,l = sess.run([query_str[0],text_str[0],query_str[1],text_str[1], \
query_str[2],text_str[2],query_str[3],text_str[3], \
query_str[4],text_str[4],query_str[5],text_str[5]
])
print a
print b
print c
print d
print e
print f
print g
print h
print i
print j
print k
print l
print '-----------------------'
duration = time.time() - start_time
#if step%10 == 0:
#saver.save(sess, os.path.join(FLAGS.model, 'model_%d.ckpt'%step))
except:
traceback.print_exc()
pass
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
def test():
trainer, predictor = algos_factory.gen_trainer_and_predictor(FLAGS.algo)
trainer.is_training = False
input_app = InputApp.InputApp()
sess = input_app.sess = tf.InteractiveSession()
input_results = input_app.gen_input()
with tf.variable_scope(FLAGS.main_scope) as scope:
eval_image_name, eval_text, eval_text_str, eval_input_text, eval_input_text_str = input_results[
input_app.input_valid_name]
eval_loss = trainer.build_train_graph(eval_input_text, eval_text)
scope.reuse_variables()
gen_predict_graph(predictor)
print('---------------', tf.get_collection('scores'))
eval_scores = tf.get_collection('scores')[-1]
eval_names = ['loss']
print('eval_names:', eval_names)
print('gen_validate-------------------------',
tf.get_collection('scores'))
eval_ops = [eval_loss]
eval_ops, deal_eval_results = \
gen_evalulate(
input_app,
input_results,
predictor,
eval_ops,
eval_scores)
test_flow(eval_ops,
names=eval_names,
gen_feed_dict=None,
deal_results=deal_eval_results,
model_dir=FLAGS.model_dir,
num_interval_steps=FLAGS.num_interval_steps,
num_epochs=FLAGS.num_epochs,
eval_times=FLAGS.eval_times,
sess=sess)
def test():
trainer = algos_factory.gen_tranier(FLAGS.algo)
input_app = InputApp.InputApp()
sess = input_app.sess = tf.InteractiveSession()
#init_op = tf.group(tf.global_variables_initializer(),
# tf.local_variables_initializer())
#sess.run(init_op)
input_results = input_app.gen_input()
#--------train
image_name, image_feature, text, text_str, input_text, input_text_str = input_results[
input_app.input_valid_name]
#How to get ride of main scope, not use it ? use make_template to make auto share
with tf.variable_scope(FLAGS.main_scope):
loss = trainer.build_train_graph(image_feature, input_text, text)
melt.flow.tf_flow(read_once, num_steps=1)