From c8878c800cf8f9cb60bb9e2cac061c708ae6230a Mon Sep 17 00:00:00 2001 From: Mimee Date: Wed, 27 Sep 2017 16:31:15 -0700 Subject: [PATCH] Formatting and writing. --- 01.fit_a_line/README.md | 12 ++++++------ 01.fit_a_line/index.html | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 814cd1c..ce4b533 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -72,16 +72,16 @@ The UCI housing dataset has 506 instances. Each instance describes the attribute | CRIM | per capita crime rate by town | Continuous| | ZN | proportion of residential land zoned for lots over 25,000 sq.ft. | Continuous | | INDUS | proportion of non-retail business acres per town | Continuous | -| CHAS | Charles River dummy variable | Discrete, 1 if tract bounds river; 0 otherwise| -| NOX | nitric oxides concentration (parts per 10 million) | Continuous | +| CHAS | whether the property is close to Charles River | Discrete| +| NOX | nitric oxide concentration (parts per 10 million) | Continuous | | RM | average number of rooms per dwelling | Continuous | | AGE | proportion of owner-occupied units built prior to 1940 | Continuous | | DIS | weighted distances to five Boston employment centres | Continuous | | RAD | index of accessibility to radial highways | Continuous | -| TAX | full-value property-tax rate per $10,000 | Continuous | +| TAX | full-value property-tax rate per \$10,000 | Continuous | | PTRATIO | pupil-teacher ratio by town | Continuous | | B | 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town | Continuous | -| LSTAT | % lower status of the population | Continuous | +| LSTAT | percentage lower status of the population | Continuous | | MEDV | Median value of owner-occupied homes in $1000's | Continuous | The last entry is the median home price. @@ -129,8 +129,8 @@ Logistic regression is essentially a fully-connected layer with linear activatio ```python 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()) + size=1, + act=paddle.activation.Linear()) y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1)) cost = paddle.layer.mse_cost(input=y_predict, label=y) ``` diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html index f143f7f..22afb00 100644 --- a/01.fit_a_line/index.html +++ b/01.fit_a_line/index.html @@ -114,16 +114,16 @@ The UCI housing dataset has 506 instances. Each instance describes the attribute | CRIM | per capita crime rate by town | Continuous| | ZN | proportion of residential land zoned for lots over 25,000 sq.ft. | Continuous | | INDUS | proportion of non-retail business acres per town | Continuous | -| CHAS | Charles River dummy variable | Discrete, 1 if tract bounds river; 0 otherwise| -| NOX | nitric oxides concentration (parts per 10 million) | Continuous | +| CHAS | whether the property is close to Charles River | Discrete| +| NOX | nitric oxide concentration (parts per 10 million) | Continuous | | RM | average number of rooms per dwelling | Continuous | | AGE | proportion of owner-occupied units built prior to 1940 | Continuous | | DIS | weighted distances to five Boston employment centres | Continuous | | RAD | index of accessibility to radial highways | Continuous | -| TAX | full-value property-tax rate per $10,000 | Continuous | +| TAX | full-value property-tax rate per \$10,000 | Continuous | | PTRATIO | pupil-teacher ratio by town | Continuous | | B | 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town | Continuous | -| LSTAT | % lower status of the population | Continuous | +| LSTAT | percentage lower status of the population | Continuous | | MEDV | Median value of owner-occupied homes in $1000's | Continuous | The last entry is the median home price. @@ -171,8 +171,8 @@ Logistic regression is essentially a fully-connected layer with linear activatio ```python 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()) + size=1, + act=paddle.activation.Linear()) y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1)) cost = paddle.layer.mse_cost(input=y_predict, label=y) ``` -- GitLab