From c071ab0793871c68be1dc1fc36b70dc46ac8d112 Mon Sep 17 00:00:00 2001 From: luhao2013 Date: Thu, 21 Mar 2019 13:48:23 +0800 Subject: [PATCH] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1. 补充缺失的右括号 2. 从txt文件中读取数据的Python代码片段，改为可以在markdown文件中高亮显示 --- 01.fit_a_line/README.md | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 23a9762..965718e 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -40,7 +40,7 @@ $$\hat{Y} = \omega_1X_{1} + \omega_2X_{2} + \ldots + \omega_{13}X_{13} + b$$ $\hat{Y}$ represents the predicted result of the model and is used to distinguish it from the real value $Y$. The parameters to be learned by the model are: $\omega_1, \ldots, \omega_{13}, b$. -After building the model, we need to give the model an optimization goal so that the learned parameters can make the predicted value $\hat{Y}$ get as close to the true value $Y$. Here we introduce the concept of loss function ([Loss Function](https://en.wikipedia.org/wiki/Loss_function), or Cost Function. Input the target value $y_{i}$ of any data sample and the predicted value $\hat{y_{i}}$ given by a model. Then the loss function outputs a non-negative real number, which is usually used to represent model error. +After building the model, we need to give the model an optimization goal so that the learned parameters can make the predicted value $\hat{Y}$ get as close to the true value $Y$. Here we introduce the concept of loss function ([Loss Function](https://en.wikipedia.org/wiki/Loss_function), or Cost Function). Input the target value $y_{i}$ of any data sample and the predicted value $\hat{y_{i}}$ given by a model. Then the loss function outputs a non-negative real number, which is usually used to represent model error. For linear regression models, the most common loss function is the Mean Squared Error ([MSE](https://en.wikipedia.org/wiki/Mean_squared_error)), which is: @@ -158,39 +158,33 @@ test_reader = paddle.batch( If you want to read data directly from \*.txt file, you can refer to the method as follows. +```python feature_names = [ 'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'convert' ] feature_num = len(feature_names) - data = numpy.fromfile(filename, sep=' ') # Read primary data from file - data = data.reshape(data.shape[0] // feature_num, feature_num) - maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(axis=0)/data.shape[0] - for i in six.moves.range(feature_num-1): - data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves is compatible to python2 and python3 + data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves is compatible to python2 and python3 ratio = 0.8 # distribution ratio of train dataset and verification dataset - -offset = int(data.shape[0]\*ratio) - +offset = int(data.shape[0]*ratio) train_data = data[:offset] - test_data = data[offset:] train_reader = paddle.batch( paddle.reader.shuffle( train_data, buf_size=500), batch_size=BATCH_SIZE) - test_reader = paddle.batch( paddle.reader.shuffle( test_data, buf_size=500), batch_size=BATCH_SIZE) +``` ### Configure Program for Training The aim of the program for training is to define a network structure of a training model. For linear regression, it is a simple fully connected layer from input to output. More complex result, such as Convolutional Neural Network and Recurrent Neural Network, will be introduced in later chapters. It must return `mean error` as the first return value in program for training, for that `mean error` will be used for BackPropagation. -- GitLab