def test(self, data, label='Test'):
N = int(math.ceil(len(data) / self.batch_size))
cost = 0
x = np.ndarray([self.batch_size, self.edim], dtype=np.float32)
time = np.ndarray([self.batch_size, self.mem_size], dtype=np.int32)
target = np.zeros([self.batch_size, self.nwords])
context = np.ndarray([self.batch_size, self.mem_size])
x.fill(self.init_hid)
for t in xrange(self.mem_size):
time[:, t].fill(t)
if self.show:
from util import ProgressBar
bar = ProgressBar(label, max=N)
m = self.mem_size
for idx in xrange(N):
if self.show: bar.next()
target.fill(0)
for b in xrange(self.batch_size):
target[b][data[m]] = 1
context[b] = data[m - self.mem_size:m]
m += 1
if m >= len(data):
m = self.mem_size
loss = self.sess.run([self.loss], feed_dict={self.input: x,
self.time: time,
self.target: target,
self.context: context})
cost += np.sum(loss)
if self.show: bar.finish()
return cost / N / self.batch_size
def train(self, data):
N = int(math.ceil(len(data) / self.batch_size))
cost = 0
x = np.ndarray([self.batch_size, self.edim], dtype=np.float32)
time = np.ndarray([self.batch_size, self.mem_size], dtype=np.int32)
target = np.zeros([self.batch_size, self.nwords])
context = np.ndarray([self.batch_size, self.mem_size])
x.fill(self.init_hid)
for t in xrange(self.mem_size):
time[:, t].fill(t)
if self.show:
from util import ProgressBar
bar = ProgressBar('Train', max=N)
for idx in xrange(N):
if self.show: bar.next()
target.fill(0)
for b in xrange(self.batch_size):
m = random.randrange(self.mem_size, len(data))
target[b][data[m]] = 1
context[b] = data[m - self.mem_size:m]
_, loss, self.step = self.sess.run([self.optim,
self.loss,
self.global_step],
feed_dict={
self.input: x,
self.time: time,
self.target: target,
self.context: context})
cost += np.sum(loss)
if self.show: bar.finish()
return cost / N / self.batch_size
