diff --git a/develop/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt b/develop/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt index c243083794bb3c4659242de99b3b2715af9d7c24..e695ff283e2e806377a51c559b37e8068360a4ff 100644 --- a/develop/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt +++ b/develop/doc_cn/_sources/getstarted/concepts/use_concepts_cn.rst.txt @@ -147,4 +147,9 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和 .. literalinclude:: src/train.py :linenos: +使用以上训练好的模型进行预测，取其中一个模型params_pass_90.tar，输入需要预测的向量组，然后打印输出： + +.. literalinclude:: src/infer.py + :linenos: + 有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 `_。 diff --git a/develop/doc_cn/getstarted/concepts/use_concepts_cn.html b/develop/doc_cn/getstarted/concepts/use_concepts_cn.html index 6c4745544eb99f29c00702835ebc2d31030cc744..0d5765feadc1c7a3197b538366dac1f7aa803c51 100644 --- a/develop/doc_cn/getstarted/concepts/use_concepts_cn.html +++ b/develop/doc_cn/getstarted/concepts/use_concepts_cn.html @@ -400,7 +400,12 @@ trainer.train( 49 50 51 -52
import paddle.v2 as paddle
+52
+53
+54
+55
+56
+57
import paddle.v2 as paddle
 import numpy as np
 
 # init paddle
@@ -428,6 +433,11 @@ trainer.train(
         if event.batch_id % 1 == 0:
             print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id,
                                                   event.cost)
+    # product model every 10 pass
+    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)
 
 
 # define training dataset reader
@@ -454,6 +464,44 @@ trainer.train(
     num_passes=100)
+

使用以上训练好的模型进行预测，取其中一个模型params_pass_90.tar，输入需要预测的向量组，然后打印输出：

+
 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
import paddle.v2 as paddle
+import numpy as np
+
+paddle.init(use_gpu=False)
+x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2))
+y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+# loading the model which generated by training
+with open('params_pass_90.tar', 'r') as f:
+    parameters = paddle.parameters.Parameters.from_tar(f)
+
+# Input multiple sets of data，Output the infer result in a array.
+i = [[[1, 2]], [[3, 4]], [[5, 6]]]
+print paddle.infer(output_layer=y_predict, parameters=parameters, input=i)
+# Will print:
+# [[ -3.24491572]
+#  [ -6.94668722]
+#  [-10.64845848]]
+
+

有关线性回归的实际应用，可以参考PaddlePaddle book的 第一章节。

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