def main():
data = Input(split='train')
model = Model(data)
for v in tf.global_variables():
print(v)
with tf.Session() as sess:
sess.run([model.data.initializer, tf.global_variables_initializer()], )
# q, a, s = sess.run([model.ques_enc, model.ans_enc, model.subt_enc])
# print(q.shape, a.shape, s.shape)
t, tt = sess.run([model.output, data.gt])
print(t, tt)
print(t.shape, tt.shape)
def main():
data = Input(split='train')
model = Model(data)
for v in tf.global_variables():
print(v)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
with tf.Session(config=config) as sess:
sess.run([model.data.initializer, tf.global_variables_initializer()], )
# q, a, s = sess.run([model.ques_enc, model.ans_enc, model.subt_enc])
# print(q.shape, a.shape, s.shape)
t, tt = sess.run([model.output, data.gt])
print(t, tt)
print(t.shape, tt.shape)
def main():
data = Input(split='train', mode='subt')
model = Model(data)
for v in tf.global_variables():
print(v)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
# config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
with tf.Session(config=config) as sess:
sess.run([model.data.initializer, tf.global_variables_initializer()], )
# q, a, s = sess.run([model.ques_enc, model.ans_enc, model.subt_enc])
# print(q.shape, a.shape, s.shape)
# a, b, c, d = sess.run(model.tri_word_encodes)
# print(a, b, c, d)
# print(a.shape, b.shape, c.shape, d.shape)
a, b = sess.run([model.ans_vec, model.output])
print(a, b)
print(a.shape, b.shape)