x_batch = x_train[indexes[i:i+batchsize]]
loss = da.train(x_batch)
sum_loss += float(loss.data) * batchsize
if epoch == 0 and i == 0:
with open('../output/da/graph.dot', 'w') as o:
o.write(computational_graph.build_computational_graph((loss, )).dump())
with open('../output/da/graph.wo_split.dot', 'w') as o:
g = computational_graph.build_computational_graph((loss, ), remove_split=True)
o.write(g.dump())
print 'train mean loss={}'.format(sum_loss / N)
losses += [sum_loss / N]
# 評価
sum_loss = 0
for i in xrange(0, N_test, batchsize):
x_batch = x_test[i:i+batchsize]
loss = da.test(x_batch)
sum_loss += float(loss.data) * batchsize
print 'test mean loss={}'.format(sum_loss / N_test)
losses += [sum_loss / N_test]
all_loss.append(losses)
# 可視化
h = MinMaxScaler().fit_transform(da.model.encoder.W)
visualize(h, '../img/da/w/da_{0:04d}.jpg'.format(epoch + 1), (8, 8), (10, 10))
# モデルの保存
pickle.dump(da, open('../output/da/model.pkl', 'wb'))
# loss, accuracyの保存
pickle.dump(all_loss, open('../output/da/loss.pkl', 'wb'))
# 可視化
for epoch in xrange(n_epoch):
print 'epoch', epoch
indexes = np.random.permutation(N)
losses = []
sum_loss = 0
for i in xrange(0, N, batchsize):
x_batch = x_train[indexes[i:i+batchsize]]
loss = da1.train(x_batch)
sum_loss += float(loss.data) * batchsize
print 'train mean loss={}'.format(sum_loss / N)
losses += [sum_loss / N]
# 評価
sum_loss = 0
for i in xrange(0, N_test, batchsize):
x_batch = x_test[i:i+batchsize]
loss = da1.test(x_batch)
sum_loss += float(loss.data) * batchsize
print 'test mean loss={}'.format(sum_loss / N_test)
losses += [sum_loss / N_test]
all_loss.append(losses)
# 可視化
h1 = MinMaxScaler().fit_transform(da1.model.encoder.W)
visualize(h1, '../img/sda/da1/sda_da1_{0:04d}.jpg'.format(epoch + 1), (8, 8), (10, 10))
pickle.dump(da1, open(da1_filename, 'wb'))
pickle.dump(all_loss, open('../output/sda/loss_da1.pkl', 'wb'))
# 2層目
da2_filename = '../output/sda/model_da2.pkl'
try:
da2 = pickle.load(open(da2_filename))
except IOError:
