random_embedding=True
hidden_units = 128
learning_rate=0.001
batch_size = 1024
epochs = 50
model = DSSM(sequence_length, vocab_size)()
# with tf.Graph().as_default():
with tf.Session() as sess:
# initialize all variables
sess.run(tf.global_variables_initializer())
def train(train_q, train_h, y):
feed_dict = {
model.query : train_q,
model.doc : train_h,
model.y : y,
model.keep_prob: 0.5
}
loss, acc = sess.run([model.loss, model.acc], feed_dict)
print(loss, acc)
batches = data_handler.batch_iter(list(zip(train_p, train_h, train_y)),
batch_size,
epochs)
for batch in batches:
train_q_batch, train_h_batch, train_y_batch = zip(*batch)
train(train_q_batch, train_h_batch, train_y_batch)
