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Paddle/demo/mnist/api_train_v2.py

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2.2 KiB

import paddle.v2 as paddle
def main():
paddle.init(use_gpu=False, trainer_count=1)
# define network topology
images = paddle.layer.data(
name='pixel', type=paddle.data_type.dense_vector(784))
label = paddle.layer.data(
name='label', type=paddle.data_type.integer_value(10))
hidden1 = paddle.layer.fc(input=images, size=200)
hidden2 = paddle.layer.fc(input=hidden1, size=200)
inference = paddle.layer.fc(input=hidden2,
size=10,
act=paddle.activation.Softmax())
cost = paddle.layer.classification_cost(input=inference, label=label)
parameters = paddle.parameters.create(cost)
adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=adam_optimizer)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1000 == 0:
result = trainer.test(reader=paddle.reader.batched(
paddle.dataset.mnist.test(), batch_size=256))
print "Pass %d, Batch %d, Cost %.2f, %s\n" \
"Testing cost %.2f metrics %s" % (
event.pass_id, event.batch_id, event.cost,
event.metrics,
result.cost, result.metrics)
else:
pass
trainer.train(
reader=paddle.reader.batched(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=32),
event_handler=event_handler)
# output is a softmax layer. It returns probabilities.
# Shape should be (100, 10)
probs = paddle.infer(
output=inference,
parameters=parameters,
reader=paddle.reader.batched(
paddle.reader.firstn(
paddle.reader.map_readers(lambda item: (item[0], ),
paddle.dataset.mnist.test()),
n=100),
batch_size=32))
print probs.shape
if __name__ == '__main__':
main()