class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
self.l1 = L.Linear(None, n_units) # n_in -> n_units
self.l2 = L.Linear(None, n_units) # n_units -> n_units
self.l3 = L.Linear(None, n_out) # n_units -> n_out
@within_name_scope('MLP')
def __call__(self, x):
with name_scope('linear1', self.l1.params()):
h1 = F.relu(self.l1(x))
with name_scope('linear2', self.l2.params()):
h2 = F.relu(self.l2(h1))
with name_scope('linear3', self.l3.params()):
o = self.l3(h2)
return o
model = L.Classifier(MLP(1000, 10))
res = model(chainer.Variable(np.random.rand(1, 784).astype(np.float32)),
chainer.Variable(np.random.rand(1).astype(np.int32)))
writer = SummaryWriter('runs/' + datetime.now().strftime('%B%d %H:%M:%S'))
writer.add_graph([res])
writer.add_all_variable_images([res], pattern='.*MLP.*')
writer.add_all_parameter_histograms([res], pattern='.*MLP.*')
writer.close()
[str(float(l.data))
for l in model.predictor.loss]) + '\n')
logf.flush()
if args.gpu >= 0: model.to_gpu()
if (count % args.save) == 0:
print('save the model')
serializers.save_npz('models/' + str(count) + '.model', model)
print('save the optimizer')
serializers.save_npz('models/' + str(count) + '.state',
optimizer)
if args.tensorboard:
for name, param in model.predictor.namedparams():
writer.add_histogram(name,
chainer.cuda.to_cpu(param.data),
count)
writer.add_scalar('loss', float(model.loss.data), count)
x_batch[0] = y_batch[0]
if count > args.period:
break
count += 1
seq = (seq + 1) % len(sequencelist)
# For logging graph structure
if args.tensorboard:
model(chainer.Variable(xp.asarray(x_batch)),
chainer.Variable(xp.asarray(y_batch)))
writer.add_graph(model.y)
writer.close()