adding code

01.fit_a_line/infer.py

+import paddle.v2 as paddle
+
+# Initialize PaddlePaddle.
+paddle.init(use_gpu=False, trainer_count=1)
+
+# Configure the neural network.
+x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+with open('fit_a_line.tar', 'r') as f:
+    parameters = paddle.parameters.Parameters.from_tar(f)
+
+# Infer using provided test data.
+probs = paddle.infer(
+    output_layer=y_predict, parameters=parameters,
+    input=[item for item in paddle.dataset.uci_housing.test()()])
+
+for i in xrange(len(probs)):
+    print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)