def eval(self, sess, args):
if not os.path.exists(args.logdir + '/output'):
os.makedirs(args.logdir + '/output')
if args.eval_only:
self.test_data = read_data.load_test_dataset(self.dataset_file)
if args.weights is not None:
self.saver.restore(sess, args.weights)
print_in_file("Saved")
lr = self.learning_rate
i_e, t_e, i_t, t_t = read_data.data_iterator(self.test_data,
self.num_steps,
self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
batch_num = len(
list(read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info('Evaluation Batch Num {}'.format(batch_num))
f = open(os.path.join(args.logdir, "output_t.txt"), 'w+')
i = 0
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
# self.input_e: e_x,
self.inputs_t:
np.maximum(np.log(t_x), 0),
self.target_t:
t_y,
# self.targets_e: e_y,
self.sample_t:
np.maximum(np.log(sample_t), 0)
}
if i > 0 and i % (batch_num // 10) == 0:
lr = lr * 2. / 3
# correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost = sess.run(
# [self.correct_pred, self.deviation, self.pred_e, self.pred_t, self.d_cost, self.g_cost,
# self.gen_e_cost, self.gen_t_cost,self.disc_cost_1, self.gradient_penalty],
# feed_dict = feed_dict)
pred_t = sess.run(self.pred_t, feed_dict=feed_dict)
# sum_correct_pred = sum_correct_pred + correct_pred
# sum_iter = sum_iter + 1
# sum_deviation = sum_deviation + deviation
f.write('pred_t: ' +
'\t'.join([str(v) for v in tf.exp(pred_t).eval()]))
f.write('\n')
f.write(
'targ_t: ' +
'\t'.join([str(v) for v in np.array(t_y_list[i]).flatten()]))
f.write('\n')
i += 1
def eval(self, sess, args):
if not os.path.exists(args.logdir + '/output'):
os.makedirs(args.logdir + '/output')
# if args.eval_only:
# self.test_data = read_data.load_test_dataset(self.dataset_file)
if args.weights is not None:
self.saver.restore(sess, args.weights)
print_in_file("Saved")
lr = self.learning_rate
batch_size = 100
input_event_data, target_event_data, input_time_data, target_time_data = read_data.data_iterator(
self.test_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(batch_size, input_time_data,
target_time_data)
f = open(os.path.join(args.logdir, "output_e.txt"), 'w+')
i = 0
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, input_event_data, target_event_data,
input_time_data, target_time_data):
feed_dict = {
self.input_e:
e_x,
self.inputs_t:
np.maximum(np.log(t_x), 0),
# self.target_t : t_y_list[i],
# self.targets_e : e_y_list[i],
self.sample_t:
np.maximum(np.log(sample_t), 0)
}
pred_e = sess.run(self.pred_e, feed_dict=feed_dict)
_, pred_e_index = tf.nn.top_k(pred_e, 1, name=None)
f.write('pred_e: ' + '\t'.join([
str(v)
for v in tf.reshape(tf.squeeze(pred_e_index), [-1]).eval()
]))
f.write('\n')
f.write('targ_e: ' +
'\t'.join([str(v) for v in np.array(e_y[i]).flatten()]))
f.write('\n')
train_data, valid_data, test_data = read_data.data_split(event_file,
time_file,
shuffle=True)
# initialize the model
model = Model.MM_CPred(args)
# Get Config and Create a Session
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
sess = tf.compat.v1.Session(config=config)
sess.run(tf.initialize_all_variables())
# Run Train Epoches
train_i_e, train_t_e, train_i_t, train_t_t = read_data.data_iterator(
train_data, args.T, args.len, overlap=True)
gap = 6
alpha = args.alpha
gamma = args.gamma
for epoch in range(args.train_iter):
logging.info('Training epoch: {}'.format(epoch))
t0 = time.time()
i = 0
sess.run(model.running_MAE_vars_initializer)
sess.run(model.running_precision_vars_initializer)
sess.run(model.running_recall_vars_initializer)
for e_x, e_y, t_x, t_y in read_data.generate_batch(args.batch_size,
train_i_e, train_t_e,
train_i_t, train_t_t):
def train(self, sess, args):
self.logdir = args.logdir + parse_time()
while os.path.exists(self.logdir):
time.sleep(random.randint(1, 5))
self.logdir = args.logdir + parse_time()
os.makedirs(self.logdir)
if not os.path.exists('%s/logs' % self.logdir):
os.makedirs('%s/logs' % self.logdir)
if args.weights is not None:
self.saver.restore(sess, args.weights)
lr = self.learning_rate
for epoch in range(args.iters):
'''training'''
sum_correct_pred = 0.0
sum_iter = 0.0
sum_deviation = 0.0
batch_num, e_x_list, e_y_list, t_x_list, t_y_list = read_data.data_iterator(
self.train_data, self.event_to_id, self.num_steps, self.length,
self.batch_size)
iterations = batch_num // 6
print(iterations)
sample_t_x_list = list(t_x_list)
sample_t_y_list = list(t_y_list)
np.random.shuffle(sample_t_x_list)
np.random.shuffle(sample_t_y_list)
print(len(t_x_list))
print(len(sample_t_x_list))
for i in range(iterations):
d_iters = 5
if i > 0 and i % (iterations // 10) == 0:
lr = lr * 2. / 3
for j in range(d_iters):
feed_dict = {
self.input_e: e_x_list[i * 6 + j],
self.inputs_t: t_x_list[i * 6 + j],
self.target_t: t_y_list[i * 6 + j],
self.targets_e: e_y_list[i * 6 + j],
self.sample_t: sample_t_x_list[i * 6 + j],
self.sample_target_t: sample_t_y_list[i * 6 + j]
}
_ = sess.run(self.d_train_op, feed_dict=feed_dict)
feed_dict = {
self.input_e: e_x_list[i * 6 + d_iters],
self.inputs_t: t_x_list[i * 6 + d_iters],
self.target_t: t_y_list[i * 6 + d_iters],
self.targets_e: e_y_list[i * 6 + d_iters],
self.sample_t: sample_t_x_list[i * 6 + d_iters],
self.sample_target_t: sample_t_y_list[i * 6 + d_iters]
}
_, correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost, disc_cost_1, gradient_penalty = sess.run(
[
self.g_train_op, self.correct_pred, self.deviation,
self.pred_e, self.pred_t, self.d_cost, self.g_cost,
self.gen_e_cost, self.gen_t_cost, self.disc_cost_1,
self.gradient_penalty
],
feed_dict=feed_dict)
sess.run(self.clip_op)
sum_correct_pred = sum_correct_pred + correct_pred
sum_iter = sum_iter + self.length
sum_deviation = sum_deviation + deviation
if i % (iterations // 10) == 0:
print(
'[epoch: %d, %d] precision: %f, deviation: %f, d_loss: %f, g_loss: %f'
% (epoch, int(i //
(iterations // 10)), sum_correct_pred /
(sum_iter * self.batch_size),
sum_deviation / sum_iter, d_loss, g_loss))
print()
print(
'gen_e_loss: %f, gen_t_loss: %f, d_1_loss: %f, g_penal_loss: %f'
% (gen_e_cost, gen_t_cost, disc_cost_1,
gradient_penalty))
print()
'''
evaludation
'''
batch_num, e_x_list, e_y_list, t_x_list, t_y_list = read_data.data_iterator(
self.valid_data, self.event_to_id, self.num_steps, self.length,
self.batch_size)
sample_t_x_list = list(t_x_list)
sample_t_y_list = list(t_y_list)
np.random.shuffle(sample_t_x_list)
np.random.shuffle(sample_t_y_list)
iterations = batch_num
sum_correct_pred = 0.0
sum_iter = 0.0
sum_deviation = 0.0
for i in range(iterations):
feed_dict = {
self.input_e: e_x_list[i],
self.inputs_t: t_x_list[i],
self.target_t: t_y_list[i],
self.targets_e: e_y_list[i],
self.sample_t: sample_t_x_list[i],
self.sample_target_t: sample_t_y_list[i]
}
if i > 0 and i % (iterations // 10) == 0:
lr = lr * 2. / 3
correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost, disc_cost_1, gradient_penalty = sess.run(
[
self.correct_pred, self.deviation, self.pred_e,
self.pred_t, self.d_cost, self.g_cost, self.gen_e_cost,
self.gen_t_cost, self.disc_cost_1,
self.gradient_penalty
],
feed_dict=feed_dict)
sum_correct_pred = sum_correct_pred + correct_pred
sum_iter = sum_iter + self.length
sum_deviation = sum_deviation + deviation
if i % (iterations // 10) == 0:
print(
'%f, precision: %f, deviation: %f, d_loss: %f, g_loss: %f'
% (i // (iterations // 10), sum_correct_pred /
(sum_iter * self.batch_size),
sum_deviation / sum_iter, d_loss, g_loss))
self.save_model(sess, self.logdir, args.iters)
def eval(self, sess, args):
if not os.path.exists(args.logdir + '/output'):
os.makedirs(args.logdir + '/output')
if args.eval_only:
self.test_data = load_test_dataset(self.dataset_file)
if args.weights is not None:
self.saver.restore(sess, args.weights)
print_in_file("Saved")
batch_size = 20
batch_num, e_x_list, e_y_list, t_x_list, t_y_list = read_data.data_iterator(
self.test_data, self.event_to_id, self.num_steps, self.length,
batch_size)
sample_t_x_list = list(t_x_list)
sample_t_y_list = list(t_y_list)
np.random.shuffle(sample_t_x_list)
np.random.shuffle(sample_t_y_list)
iterations = batch_num
sum_correct_pred = 0.0
sum_iter = 0.0
sum_deviation = 0.0
f = open(os.path.join(args.logdir, "output.txt"), 'w+')
for i in range(iterations):
feed_dict = {
self.input_e: e_x_list[i],
self.inputs_t: np.maximum(np.log(t_x_list[i]), 0),
self.target_t: t_y_list[i],
self.targets_e: e_y_list[i],
self.sample_t: np.maximum(np.log(sample_t_x_list[i]), 0),
self.sample_target_t: np.maximum(np.log(sample_t_y_list[i]), 0)
}
if i > 0 and i % (iterations // 10) == 0:
lr = lr * 2. / 3
correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost, disc_cost_1, gradient_penalty = sess.run(
[
self.correct_pred, self.deviation, self.pred_e,
self.pred_t, self.d_cost, self.g_cost, self.gen_e_cost,
self.gen_t_cost, self.disc_cost_1, self.gradient_penalty
],
feed_dict=feed_dict)
sum_correct_pred = sum_correct_pred + correct_pred
sum_iter = sum_iter + self.length
sum_deviation = sum_deviation + deviation
f.write(pred_e)
f.write('\n')
f.write(sess.run(self.targets_e))
f.write('\n')
f.write(pred_t)
f.write('\n')
f.write(sess.run(self.target_t))
f.write('\n\n')
if i % (iterations // 10) == 0:
print(
'%f, precision: %f, deviation: %f, d_loss: %f, g_loss: %f'
% (i // (iterations // 10), sum_correct_pred /
(sum_iter * batch_size), sum_deviation /
(sum_iter * batch_size), d_loss, g_loss))
def train(self, sess, args):
self.logdir = args.logdir + parse_time()
while os.path.exists(self.logdir):
time.sleep(random.randint(1, 5))
self.logdir = args.logdir + parse_time()
os.makedirs(self.logdir)
if not os.path.exists('%s/logs' % self.logdir):
os.makedirs('%s/logs' % self.logdir)
if args.weights is not None:
self.saver.restore(sess, args.weights)
lr = self.learning_rate
for epoch in range(args.iters):
'''training'''
sum_correct_pred = 0.0
sum_iter = 0.0
sum_deviation = 0.0
input_len, input_event_data, target_event_data, input_time_data, target_time_data = read_data.data_iterator(
self.train_data, self.event_to_id, self.num_steps, self.length)
batch_num, e_x_list, e_y_list, t_x_list, t_y_list = read_data.generate_batch(
input_len, self.batch_size, input_event_data,
target_event_data, input_time_data, target_time_data)
_, sample_t_list = read_data.generate_sample_t(
input_len, self.batch_size, input_time_data, target_time_data)
g_iters = 5
gap = g_iters + 1
iterations = batch_num // gap
print(iterations)
for i in range(iterations):
if i > 0 and i % (iterations // 10) == 0:
lr = lr * 2. / 3
for j in range(g_iters):
feed_dict = {
self.input_e:
e_x_list[(i) * gap + j],
self.inputs_t:
np.maximum(np.log(t_x_list[(i) * gap + j]), 0),
self.target_t:
t_y_list[(i) * gap + j],
self.targets_e:
e_y_list[(i) * gap + j],
self.sample_t:
np.maximum(np.log(sample_t_list[(i) * gap + j]), 0)
}
_, correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost, disc_cost_1, gradient_penalty = sess.run(
[
self.g_train_op, self.correct_pred, self.deviation,
self.pred_e, self.pred_t, self.d_cost, self.g_cost,
self.gen_e_cost, self.gen_t_cost, self.disc_cost_1,
self.gradient_penalty
],
feed_dict=feed_dict)
feed_dict = {
self.input_e:
e_x_list[(i) * gap + g_iters],
self.inputs_t:
np.maximum(np.log(t_x_list[(i) * gap + g_iters]), 0),
self.target_t:
t_y_list[(i) * gap + g_iters],
self.targets_e:
e_y_list[(i) * gap + g_iters],
self.sample_t:
np.maximum(np.log(sample_t_list[(i) * gap + g_iters]), 0)
}
_ = sess.run(self.d_train_op, feed_dict=feed_dict)
if self.cell_type == 'T_LSTMCell':
sess.run(self.clip_op)
sum_correct_pred = sum_correct_pred + correct_pred
sum_iter = sum_iter + 1
sum_deviation = sum_deviation + deviation
if i % (iterations // 10) == 0:
print(
'[epoch: %d, %d] precision: %f, deviation: %f, d_loss: %f, g_loss: %f'
% (epoch, int(i //
(iterations // 10)), sum_correct_pred /
(sum_iter * self.batch_size * self.length),
sum_deviation /
(sum_iter * self.batch_size * self.length), d_loss,
g_loss))
print()
print(
'gen_e_loss: %f, gen_t_loss: %f, d_1_loss: %f, g_penal_loss: %f'
% (gen_e_cost, gen_t_cost, disc_cost_1,
gradient_penalty))
print()
'''
evaludation
'''
input_len, input_event_data, target_event_data, input_time_data, target_time_data = read_data.data_iterator(
self.train_data, self.event_to_id, self.num_steps, self.length)
batch_num, e_x_list, e_y_list, t_x_list, t_y_list = read_data.generate_batch(
input_len, self.batch_size, input_event_data,
target_event_data, input_time_data, target_time_data)
_, sample_t_list = read_data.generate_sample_t(
input_len, self.batch_size, input_time_data, target_time_data)
iterations = batch_num
sum_correct_pred = 0.0
sum_iter = 0.0
sum_deviation = 0.0
for i in range(iterations):
feed_dict = {
self.input_e: e_x_list[i],
self.inputs_t: np.maximum(np.log(t_x_list[i]), 0),
self.target_t: t_y_list[i],
self.targets_e: e_y_list[i],
self.sample_t: np.maximum(np.log(sample_t_list[i]), 0)
}
if i > 0 and i % (iterations // 10) == 0:
lr = lr * 2. / 3
correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost, disc_cost_1, gradient_penalty = sess.run(
[
self.correct_pred, self.deviation, self.pred_e,
self.pred_t, self.d_cost, self.g_cost, self.gen_e_cost,
self.gen_t_cost, self.disc_cost_1,
self.gradient_penalty
],
feed_dict=feed_dict)
sum_correct_pred = sum_correct_pred + correct_pred
sum_iter = sum_iter + 1
sum_deviation = sum_deviation + deviation
if i % (iterations // 10) == 0:
print(
'%f, precision: %f, deviation: %f, d_loss: %f, g_loss: %f'
% (i // (iterations // 10), sum_correct_pred /
(sum_iter * self.batch_size * self.length),
sum_deviation /
(sum_iter * self.batch_size * self.length), d_loss,
g_loss))
self.save_model(sess, self.logdir, args.iters)
def eval(self, sess, args):
if not os.path.exists(args.logdir + '/output'):
os.makedirs(args.logdir + '/output')
if args.eval_only:
self.test_data = read_data.load_test_dataset(self.dataset_file)
if args.weights is not None:
self.saver.restore(sess, args.weights)
print_in_file("Saved")
lr = self.learning_rate
batch_size = 100
input_event_data, target_event_data, input_time_data, target_time_data = read_data.data_iterator(
self.test_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(batch_size, input_time_data,
target_time_data)
f = open(os.path.join(args.logdir, "output.txt"), 'w+')
# batch_num = len(list(read_data.generate_batch))
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, input_event_data, target_event_data,
input_time_data, target_time_data):
feed_dict = {
self.input_e:
e_x,
self.inputs_t:
np.maximum(np.log(t_x), 0),
# self.target_t : t_y_list[i],
# self.targets_e : e_y_list[i],
self.sample_t:
np.maximum(np.log(sample_t), 0)
}
# correct_pred, deviation, pred_e, pred_t, d_loss, g_loss, gen_e_cost, gen_t_cost = sess.run(
# [self.correct_pred, self.deviation, self.pred_e, self.pred_t, self.d_cost, self.g_cost,
# self.gen_e_cost, self.gen_t_cost,self.disc_cost_1, self.gradient_penalty],
# feed_dict = feed_dict)
pred_e, pred_t, = sess.run([self.pred_e, self.pred_t],
feed_dict=feed_dict)
# sum_correct_pred = sum_correct_pred + correct_pred
# sum_iter = sum_iter + 1
# sum_deviation = sum_deviation + deviation
_, pred_e_index = tf.nn.top_k(pred_e, 1, name=None)
f.write('pred_e: ' + '\t'.join([
str(v)
for v in tf.reshape(tf.squeeze(pred_e_index), [-1]).eval()
]))
f.write('\n')
f.write('targ_e: ' +
'\t'.join([str(v) for v in np.array(e_y).flatten()]))
f.write('\n')
f.write('pred_t: ' +
'\t'.join([str(v) for v in tf.exp(pred_t).eval()]))
f.write('\n')
f.write('targ_t: ' +
'\t'.join([str(v) for v in np.array(t_y).flatten()]))
f.write('\n')
def train(self, sess, args):
self.logdir = args.logdir + parse_time()
while os.path.exists(self.logdir):
time.sleep(random.randint(1, 5))
self.logdir = args.logdir + parse_time()
os.makedirs(self.logdir)
if not os.path.exists('%s/logs' % self.logdir):
os.makedirs('%s/logs' % self.logdir)
if args.weights is not None:
self.saver.restore(sess, args.weights)
self.lr = self.learning_rate
for epoch in range(args.iters):
'''training'''
sess.run([
self.running_precision_vars_initializer,
self.running_recall_vars_initializer
])
# re initialize the metric variables of metric.precision and metric.recall,
# to calculate these metric for each epoch
batch_precision, batch_recall = 0.0, 0.0
average_deviation, sum_deviation = 0.0, 0.0
d_loss, g_loss, gen_e_cost, gen_t_cost, huber_t_loss = 0.0, 0.0, 0.0, 0.0, 0.0
i_e, t_e, i_t, t_t = read_data.data_iterator(
self.train_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
i = 0
gap = 6
sum_iter = 0.0
batch_num = len(
list(
read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info('Training batch num {}'.format(batch_num))
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
self.input_e: e_x,
self.inputs_t: np.maximum(np.log(t_x), 0),
self.target_t: t_y,
self.targets_e: e_y,
self.sample_t: np.maximum(np.log(sample_t), 0)
}
if i % gap == 0:
_, _ = sess.run([self.d_train_op, self.w_clip_op],
feed_dict=feed_dict)
else:
# train event cross-entropy
_ = sess.run(self.g_e_train_op, feed_dict=feed_dict)
# train time huber-loss
_ = sess.run(self.g_t_train_op, feed_dict=feed_dict)
# jointly update
_, _, _, deviation, batch_precision, batch_recall, d_loss, g_loss, gen_e_cost, gen_t_cost, huber_t_loss = sess.run(
[
self.g_train_op, self.batch_precision_op,
self.batch_recall_op, self.deviation,
self.batch_precision, self.batch_recall,
self.d_cost, self.g_cost, self.gen_e_cost,
self.gen_t_cost, self.huber_t_loss
],
feed_dict=feed_dict)
sum_iter = sum_iter + 1.0
sum_deviation = sum_deviation + deviation
average_deviation = sum_deviation / sum_iter
# if self.cell_type == 'T_LSTMCell':
# sess.run(self.clip_op)
if i % (batch_num // 10) == 0:
logging.info(
'[epoch: {}, {}] precision: {}, recall: {}, deviation: {}'
.format(epoch,
float(i) / (batch_num // 10), batch_precision,
batch_recall, average_deviation))
logging.info(
'd_loss: {}, g_loss: {}, gen_e_loss: {}, gen_t_loss: {}, hunber_t_loss: {}'
.format(d_loss, g_loss, gen_e_cost, gen_t_cost,
huber_t_loss))
i += 1
'''evaluation'''
sess.run([
self.running_precision_vars_initializer,
self.running_recall_vars_initializer
])
# re initialize the metric variables of metric.precision and metric.recall,
# to calculate these metric for each epoch
i_e, t_e, i_t, t_t = read_data.data_iterator(
self.valid_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
sum_iter = 0.0
i = 0
gen_cost_ratio = []
t_cost_ratio = []
batch_num = len(
list(
read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info('Evaluation Batch Num {}'.format(batch_num))
self.lr = self.learning_rate
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
self.input_e: e_x,
self.inputs_t: np.maximum(np.log(t_x), 0),
self.target_t: t_y,
self.targets_e: e_y,
self.sample_t: np.maximum(np.log(sample_t), 0)
}
_, _, deviation, batch_precision, batch_recall, d_loss, g_loss, gen_e_cost, gen_t_cost, gen_t_cost_1, huber_t_loss = sess.run(
[
self.batch_precision_op, self.batch_recall_op,
self.deviation, self.batch_precision,
self.batch_recall, self.d_cost, self.g_cost,
self.gen_e_cost, self.gen_t_cost, self.gen_t_cost_1,
self.huber_t_loss
],
feed_dict=feed_dict)
sum_iter = sum_iter + 1.0
sum_deviation = sum_deviation + deviation
gen_cost_ratio.append(gen_t_cost / gen_e_cost)
t_cost_ratio.append(gen_t_cost_1 / huber_t_loss)
if i % (batch_num // 10) == 0:
logging.info(
'{}, precision {}, recall {}, deviation {}, d_loss {}, g_loss {}, huber_t_loss {}'
.format(
float(i) / (batch_num // 10), batch_precision,
batch_recall, sum_deviation / sum_iter, d_loss,
g_loss, huber_t_loss))
i += 1
self.alpha = tf.reduce_mean(gen_cost_ratio)
self.gamma = tf.reduce_mean(t_cost_ratio)
logging.info('alpha: {}, gamma: {}'.format(sess.run(self.alpha),
sess.run(self.gamma)))
self.save_model(sess, self.logdir, args.iters)
def train(self, sess, args):
self.logdir = args.logdir + parse_time()
while os.path.exists(self.logdir):
time.sleep(random.randint(1, 5))
self.logdir = args.logdir + parse_time()
os.makedirs(self.logdir)
if not os.path.exists('%s/logs' % self.logdir):
os.makedirs('%s/logs' % self.logdir)
if args.weights is not None:
self.saver.restore(sess, args.weights)
self.lr = self.learning_rate
for epoch in range(args.iters):
'''training'''
sess.run([
self.running_precision_vars_initializer,
self.running_recall_vars_initializer
])
if epoch > 0 and epoch % (args.iters // 5) == 0:
self.lr = self.lr * 2. / 3
# re initialize the metric variables of metric.precision and metric.recall,
# to calculate these metric for each epoch
batch_precision, batch_recall = 0.0, 0.0
sum_iter = 0.0
i_e, t_e, i_t, t_t = read_data.data_iterator(
self.train_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
i = 0
hit_sum = 0.0
batch_num = len(
list(
read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info("Total Batch Number {}".format(batch_num))
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
self.input_e: e_x,
self.inputs_t: np.maximum(np.log(t_x), 0),
self.target_t: t_y,
self.targets_e: e_y,
self.sample_t: np.maximum(np.log(sample_t), 0)
}
_, gen_e_cost, hit_count, batch_precision, batch_recall, = sess.run(
[
self.g_train_op, self.gen_e_cost, self.hit_count,
self.batch_precision_op, self.batch_recall_op
],
feed_dict=feed_dict)
sum_iter = sum_iter + 1
hit_sum += hit_count
# if self.cell_type == 'T_LSTMCell':
# sess.run(self.clip_op)
if i % (batch_num // 10) == 0:
logging.info(
'[epoch: {}, {}] hit10: {}, gen_e_loss: {}, precision: {}, recall: {}'
.format(
epoch,
float(i) / batch_num, hit_sum /
(sum_iter * self.batch_size * self.length),
gen_e_cost, batch_precision, batch_recall))
i += 1
'''evaluation'''
# re initialize the metric variables of metric.precision and metric.recall,
# to calculate these metric for each epoch
i_e, t_e, i_t, t_t = read_data.data_iterator(
self.valid_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
sum_iter = 0.0
hit_sum = 0.0
i = 0
self.lr = self.learning_rate
batch_num = len(
list(
read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info(
'Total Batch Number For Evaluation {}'.format(batch_num))
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
self.input_e: e_x,
self.inputs_t: np.maximum(np.log(t_x), 0),
self.target_t: t_y,
self.targets_e: e_y,
self.sample_t: np.maximum(np.log(sample_t), 0)
}
gen_e_cost, hit_count, batch_precision, batch_recall = sess.run(
[
self.gen_e_cost, self.hit_count, self.batch_precision,
self.batch_recall
],
feed_dict=feed_dict)
sum_iter = sum_iter + 1
hit_sum += hit_count
i += 1
if i % (batch_num // 10) == 0:
logging.info(
'{}, gen_e_cost: {}, hit10: {}, precision: {}, recall: {}'
.format(
float(i) / batch_num, gen_e_cost, hit_sum /
(sum_iter * self.batch_size * self.length),
batch_precision, batch_recall))
self.save_model(sess, self.logdir, args.iters)
def train(self, sess, args):
self.logdir = args.logdir + parse_time()
while os.path.exists(self.logdir):
time.sleep(random.randint(1, 5))
self.logdir = args.logdir + parse_time()
os.makedirs(self.logdir)
if not os.path.exists('%s/logs' % self.logdir):
os.makedirs('%s/logs' % self.logdir)
if args.weights is not None:
self.saver.restore(sess, args.weights)
self.lr = self.learning_rate
for epoch in range(args.iters):
'''training'''
sum_iter = 0.0
average_deviation, sum_deviation = 0.0, 0.0
d_loss, gen_t_cost, huber_t_loss = 0.0, 0.0, 0.0
i_e, t_e, i_t, t_t = read_data.data_iterator(
self.train_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
batch_num = len(
list(
read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info('Training batch num {}'.format(batch_num))
g_iters = 5
gap = g_iters + 1
i = 0
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
self.input_e: e_x,
self.inputs_t: np.maximum(np.log(t_x), 0),
self.target_t: t_y,
self.targets_e: e_y,
self.sample_t: np.maximum(np.log(sample_t), 0)
}
if i > 0 and i % (batch_num // 10) == 0:
self.lr = self.lr * 2. / 3
_, deviation, gen_t_cost = sess.run(
[self.g_train_op, self.deviation, self.gen_t_cost],
feed_dict=feed_dict)
sum_iter = sum_iter + 1
sum_deviation = sum_deviation + deviation
average_deviation = sum_deviation / sum_iter
# if self.cell_type == 'T_LSTMCell':
# sess.run(self.clip_op)
if i % (batch_num // 10) == 0:
logging.info('[epoch: {}, {}] deviation: {}'.format(
epoch, int(i // (batch_num // 10)), average_deviation))
logging.info(
'd_loss: {}, gen_t_loss: {}, hunber_t_loss: {}'.format(
d_loss, gen_t_cost, huber_t_loss))
i += 1
'''evaluation'''
i_e, t_e, i_t, t_t = read_data.data_iterator(
self.valid_data, self.num_steps, self.length)
sample_t = read_data.generate_sample_t(self.batch_size, i_t, t_t)
batch_num = len(
list(
read_data.generate_batch(self.batch_size, i_e, t_e, i_t,
t_t)))
logging.info('Evaluation Batch Num {}'.format(batch_num))
sum_iter = 0.0
sum_deviation = 0.0
gen_cost_ratio = []
t_cost_ratio = []
i = 0
self.lr = self.learning_rate
for e_x, e_y, t_x, t_y in read_data.generate_batch(
self.batch_size, i_e, t_e, i_t, t_t):
feed_dict = {
self.input_e: e_x,
self.inputs_t: np.maximum(np.log(t_x), 0),
self.target_t: t_y,
self.targets_e: e_y,
self.sample_t: np.maximum(np.log(sample_t), 0)
}
if i > 0 and i % (batch_num // 10) == 0:
self.lr = self.lr * 2. / 3
deviation, gen_t_cost, = sess.run(
[self.deviation, self.gen_t_cost], feed_dict=feed_dict)
sum_iter = sum_iter + 1
sum_deviation = sum_deviation + deviation
if i % (batch_num // 10) == 0:
logging.info('{} deviation: {}, g_loss: {}'.format(
int(i // (batch_num // 10)), sum_deviation / sum_iter,
gen_t_cost))
i += 1
self.save_model(sess, self.logdir, args.iters)
