def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if event.pass_id % 10 == 0:
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
trainer.save_parameter_to_tar(f)
result = trainer.test(reader=paddle.batch(uci_housing.test(), batch_size=2), feeding=feeding)
print "Test %d, Cost %f" % (event.pass_id, result.cost)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id,
event.cost)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=paddle.batch(uci_housing.test(),
batch_size=2),
feeding=feeding)
print "Test %d, Cost %f" % (event.pass_id, result.cost)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(
reader=paddle.batch(
uci_housing.test(), batch_size=2),
feeding={'x': 0,
'y': 1})
print "Test %d, %.2f" % (event.pass_id, result.cost)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=paddle.batch(uci_housing.test(),
batch_size=2),
feeding=feeding)
print "Test %d, Cost %f" % (event.pass_id, result.cost)
if trainer_id == "0":
with gzip.open("fit-a-line_pass_%05d.tar.gz" % event.pass_id,
"w") as f:
parameters.to_tar(f)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
# FIXME: for cloud data reader, pass number is managed by master
# should print the server side pass number
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(
reader=paddle.batch(
uci_housing.test(), batch_size=2),
feeding={'x': 0,
'y': 1})
print "Test %d, %.2f" % (event.pass_id, result.cost)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
# FIXME: for cloud data reader, pass number is managed by master
# should print the server side pass number
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(reader=paddle.batch(uci_housing.test(),
batch_size=2),
feeding={
'x': 0,
'y': 1
})
print "Test %d, %.2f" % (event.pass_id, result.cost)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
#保存参数
#if event.pass_id % 10 == 0:
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
trainer.save_parameter_to_tar(f)
result = trainer.test(
reader=paddle.batch(uci_housing.test(), batch_size=2),
feeding=feeding)
print "Test %d, Cost %f" % (event.pass_id, result.cost)
#print result.metrics
#保存训练结果损失情况
lists.append((event.pass_id, result.cost,
#result.metrics['classification_error_evaluator']))
result.metrics))
def event_handler_plot(event):
global step
if isinstance(event, paddle.event.EndIteration):
if step % 10 == 0: # every 10 batches, record a train cost
plot_cost.append(train_title, step, event.cost)
if step % 100 == 0: #every 100 batches, record a test cost
result = trainer.test(reader=paddle.batch(uci_housing.test(),
batch_size=2),
feeding=feeding)
plot_cost.append(test_title, step, result.cost)
if step % 100 == 0:
plot_cost.plot()
step += 1
if isinstance(event, paddle.event.EndPass):
if event.pass_id % 10 == 0:
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
parameters.to_tar(f)
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
y = paddle.layer.data(name='yy', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.square_error_cost(input=y_predict, label=y)
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(momentum=0)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer)
feeding = {'xx': 0, 'yy': 1}
trainer.train(reader=paddle.batch(paddle.reader.shuffle(uci_housing.train(),
buf_size=500),
batch_size=2),
feeding=feeding,
num_passes=30)
test_data_creator = uci_housing.test()
test_data = []
test_label = []
for item in test_data_creator():
test_data.append((item[0], ))
test_label.append(item[1])
if len(test_data) == 5:
break
probs = paddle.infer(output_layer=y_predict,
parameters=parameters,
input=test_data)
for i in xrange(len(probs)):
print "label=" + str(test_label[i][0]) + ", predict=" + str(probs[i][0])
