From eeb1dcacc9a996a52c83183d900e3b0fd92e50c5 Mon Sep 17 00:00:00 2001 From: Mimee Date: Wed, 27 Sep 2017 16:44:56 -0700 Subject: [PATCH] chapter1 book check. --- 01.fit_a_line/README.md | 8 ++++---- 01.fit_a_line/index.html | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 363f9d0..44c79d3 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -1,5 +1,5 @@ # Linear Regression -Let us begin the tutorial with a classical problem called Linear Regression \[[1](#References)\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example. +Let us begin the tutorial with a classical problem called Linear Regression \[[1](#references)\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example. The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book). @@ -12,7 +12,7 @@ In our problem setup, the attribute $x_{i,j}$ denotes the $j$th characteristic o $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b, i=1,\ldots,n$$ -where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\]. +where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#references)\]. ## Results Demonstration We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the closer the point is to the dotted line, better the model's prediction. @@ -135,10 +135,10 @@ y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1)) cost = paddle.layer.square_error_cost(input=y_predict, label=y) ``` -### Save Topology +### Save The Model Topology ```python -# Save the inference topology to protobuf. +# Save the inference topology to protobuf and write to a file. inference_topology = paddle.topology.Topology(layers=y_predict) with open("inference_topology.pkl", 'wb') as f: inference_topology.serialize_for_inference(f) diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html index 28f72ca..ea17c18 100644 --- a/01.fit_a_line/index.html +++ b/01.fit_a_line/index.html @@ -41,7 +41,7 @@