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63 lines
2.1 KiB
63 lines
2.1 KiB
from paddle.trainer_config_helpers import *
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from paddle.trainer.PyDataProvider2 import dense_vector, integer_value
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import paddle.v2 as paddle
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import numpy
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import mnist_util
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def train_reader():
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train_file = './data/raw_data/train'
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generator = mnist_util.read_from_mnist(train_file)
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for item in generator:
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yield item
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def network_config():
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imgs = data_layer(name='pixel', size=784)
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hidden1 = fc_layer(input=imgs, size=200)
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hidden2 = fc_layer(input=hidden1, size=200)
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inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation())
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cost = classification_cost(
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input=inference, label=data_layer(
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name='label', size=10))
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outputs(cost)
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def main():
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paddle.init(use_gpu=False, trainer_count=1)
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model_config = parse_network_config(network_config)
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parameters = paddle.parameters.create(model_config)
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for param_name in parameters.keys():
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array = parameters[param_name]
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array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
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parameters[param_name] = array
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adam_optimizer = paddle.optimizer.Optimizer(
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learning_rate=0.01, learning_method=AdamOptimizer())
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def event_handler(event):
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if isinstance(event, paddle.trainer.EndIteration):
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para = parameters['___fc_layer_2__.w0']
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print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % (
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event.pass_id, event.batch_id, event.cost, para.mean())
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else:
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pass
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trainer = paddle.trainer.SGDTrainer(update_equation=adam_optimizer)
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trainer.train(train_data_reader=train_reader,
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topology=model_config,
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parameters=parameters,
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event_handler=event_handler,
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batch_size=32, # batch size should be refactor in Data reader
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data_types={ # data_types will be removed, It should be in
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# network topology
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'pixel': dense_vector(784),
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'label': integer_value(10)
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})
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if __name__ == '__main__':
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main()
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