From c9023e06b477e6a5c85946ee67da790f200d0ee5 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Sun, 2 Apr 2017 23:53:22 +0800 Subject: [PATCH] update 01 and 02 markdown --- 01.fit_a_line/README.en.md | 33 +++++++------------ 01.fit_a_line/README.md | 32 +++++++----------- 01.fit_a_line/image/ranges.png | Bin 27886 -> 6681 bytes 01.fit_a_line/image/train-and-test.png | Bin 17970 -> 0 bytes 01.fit_a_line/index.en.html | 33 +++++++------------ 01.fit_a_line/index.html | 32 +++++++----------- 02.recognize_digits/README.en.md | 32 +++++++++++++++++- 02.recognize_digits/README.md | 32 +++++++++++++++++- 02.recognize_digits/image/train_and_test.png | Bin 0 -> 17904 bytes 02.recognize_digits/index.en.html | 32 +++++++++++++++++- 02.recognize_digits/index.html | 32 +++++++++++++++++- 11 files changed, 170 insertions(+), 88 deletions(-) delete mode 100644 01.fit_a_line/image/train-and-test.png create mode 100644 02.recognize_digits/image/train_and_test.png diff --git a/01.fit_a_line/README.en.md b/01.fit_a_line/README.en.md index 7f42b7d..3ed49f0 100644 --- a/01.fit_a_line/README.en.md +++ b/01.fit_a_line/README.en.md @@ -163,40 +163,31 @@ feeding={'x': 0, 'y': 1} Moreover, an event handler is provided to print the training progress: ```python -import matplotlib.pyplot as plt -from IPython import display -import cPickle +# event_handler to print training and testing info +from paddle.v2.plot import Ploter -step=0 +train_title = "Train cost" +test_title = "Test cost" +plot_cost = Ploter(train_title, test_title) -train_costs=[],[] -test_costs=[],[] +step = 0 def event_handler(event): global step - global train_costs - global test_costs if isinstance(event, paddle.event.EndIteration): - need_plot = False if step % 10 == 0: # every 10 batches, record a train cost - train_costs[0].append(step) - train_costs[1].append(event.cost) + plot_cost.append(train_title, step, event.cost) - if step % 1000 == 0: # every 1000 batches, record a test cost + if step % 100 == 0: # every 100 batches, record a test cost result = trainer.test( reader=paddle.batch( uci_housing.test(), batch_size=2), feeding=feeding) - test_costs[0].append(step) - test_costs[1].append(result.cost) + plot_cost.append(test_title, step, result.cost) if step % 100 == 0: # every 100 batches, update cost plot - plt.plot(*train_costs) - plt.plot(*test_costs) - plt.legend(['Train Cost', 'Test Cost'], loc='upper left') - display.clear_output(wait=True) - display.display(plt.gcf()) - plt.gcf().clear() + plot_cost.plot() + step += 1 ``` @@ -213,7 +204,7 @@ trainer.train( num_passes=30) ``` -![png](./image/train-and-test.png) +![png](./image/train_and_test.png) ## Summary This chapter introduces *Linear Regression* and how to train and test this model with PaddlePaddle, using the UCI Housing Data Set. Because a large number of more complex models and techniques are derived from linear regression, it is important to understand its underlying theory and limitation. diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 2c3f1fd..0eee811 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -159,40 +159,30 @@ feeding={'x': 0, 'y': 1} ```python # event_handler to print training and testing info -import matplotlib.pyplot as plt -from IPython import display -import cPickle +from paddle.v2.plot import Ploter -step=0 +train_title = "Train cost" +test_title = "Test cost" +cost_ploter = Ploter(train_title, test_title) -train_costs=[],[] -test_costs=[],[] +step = 0 def event_handler(event): global step - global train_costs - global test_costs if isinstance(event, paddle.event.EndIteration): - need_plot = False if step % 10 == 0: # every 10 batches, record a train cost - train_costs[0].append(step) - train_costs[1].append(event.cost) + cost_ploter.append(train_title, step, event.cost) - if step % 1000 == 0: # every 1000 batches, record a test cost + if step % 100 == 0: # every 100 batches, record a test cost result = trainer.test( reader=paddle.batch( uci_housing.test(), batch_size=2), feeding=feeding) - test_costs[0].append(step) - test_costs[1].append(result.cost) + cost_ploter.append(test_title, step, result.cost) if step % 100 == 0: # every 100 batches, update cost plot - plt.plot(*train_costs) - plt.plot(*test_costs) - plt.legend(['Train Cost', 'Test Cost'], loc='upper left') - display.clear_output(wait=True) - display.display(plt.gcf()) - plt.gcf().clear() + cost_ploter.plot() + step += 1 ``` @@ -209,7 +199,7 @@ trainer.train( num_passes=30) ``` -![png](./image/train-and-test.png) +![png](./image/train_and_test.png) ## 总结 在这章里,我们借助波士顿房价这一数据集,介绍了线性回归模型的基本概念,以及如何使用PaddlePaddle实现训练和测试的过程。很多的模型和技巧都是从简单的线性回归模型演化而来,因此弄清楚线性模型的原理和局限非常重要。 diff --git a/01.fit_a_line/image/ranges.png b/01.fit_a_line/image/ranges.png index 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