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Fit a line : 2nd fix (#525)

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01.fit_a_line/README.md
浏览文件 @ fdcd800c
......@@ -39,7 +39,7 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
That is, for a dataset of size $n$, MSE is the average value of the the prediction sqaure errors.
### Training
### Training Process
After setting up our model, there are several major steps to go through to train it:
1. Initialize the parameters including the weights $\vec{\omega}$ and the bias $b$. For example, we can set their mean values as $0$s, and their standard deviations as $1$s.
......@@ -48,22 +48,6 @@ After setting up our model, there are several major steps to go through to train
4. Repeat steps 2~3, until the loss is below a predefined threshold or the maximum number of epochs is reached.
## Dataset
### Python Dataset Modules
Our program starts with importing necessary packages:
```python
import paddle
import paddle.fluid as fluid
import numpy
```
We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`. This module can
1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if you haven't yet, and
2. [preprocess](#preprocessing) the dataset.
### An Introduction of the Dataset
The UCI housing dataset has 506 instances. Each instance describes the attributes of a house in surburban Boston. The attributes are explained below:
......@@ -118,8 +102,21 @@ When training complex models, we usually have one more split: the validation set
`fit_a_line/trainer.py` demonstrates the training using [PaddlePaddle](http://paddlepaddle.org).
### Datafeeder Configuration
Our program starts with importing necessary packages:
```python
import paddle
import paddle.fluid as fluid
import numpy
```
We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`. This module can
1. download the dataset to `~/.cache/paddle/dataset/uci_housing/housing.data`, if you haven't yet, and
2. [preprocess](#preprocessing) the dataset.
We first define data feeders for test and train. The feeder reads a `BATCH_SIZE` of data each time and feed them to the training/testing process. Users can shuffle a batch out of a `buf_size` in order to make the data random.
We define data feeders for test and train. The feeder reads a `BATCH_SIZE` of data each time and feed them to the training/testing process. If the user wants some randomness on the data order, she can define both a `BATCH_SIZE` and a `buf_size`. That way the datafeeder will yield the first `BATCH_SIZE` data out of a shuffle of the first `buf_size` data.
```python
BATCH_SIZE = 20
......@@ -136,7 +133,7 @@ test_reader = paddle.batch(
```
### Train Program Configuration
The train_program must return the avg_loss as its first returned parameter and then use the inference_program to setup the train_program
`train_program` sets up the network structure of this current training model. For linear regression, it is simply a fully connected layer from the input to the output. More complex structures like CNN and RNN will be introduced in later chapters. The `train_program` must return an avg_loss as its first returned parameter because it is needed in backpropagation.
```python
def train_program():
......@@ -162,7 +159,7 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
```
### Create Trainer
The trainer will take the train_program.
The trainer will take the `train_program` as input.
```python
trainer = fluid.Trainer(
......@@ -185,25 +182,24 @@ Moreover, an event handler is provided to print the training progress:
```python
# Specify the directory path to save the parameters
params_folder = "fit_a_line.inference.model"
params_dirname = "fit_a_line.inference.model"
# Plot data
from paddle.v2.plot import Ploter
train_title = "Train cost"
test_title = "Test cost"
plot_cost = Ploter(train_title, test_title)
step = 0
# event_handler to print training and testing info
def event_handler(event):
def event_handler_plot(event):
global step
if isinstance(event, fluid.EndStepEvent):
if step % 100 == 0: # every 100 batches, record a test cost
if event.step % 10 == 0: # every 10 batches, record a test cost
test_metrics = trainer.test(
reader=test_reader, feed_order=feed_order)
print(test_metrics[0])
plot_cost.append(test_title, step, test_metrics[0])
plot_cost.plot()
......@@ -212,19 +208,15 @@ def event_handler(event):
print('loss is less than 10.0, stop')
trainer.stop()
if step >= 2000:
# Or if it has been running for enough steps
print('has been running for 2000 steps, stop')
trainer.stop()
# We can save the trained parameters for the inferences later
if params_folder is not None:
trainer.save_params(params_folder)
if params_dirname is not None:
trainer.save_params(params_dirname)
step += 1
```
### Start Training
We now can start training by calling `trainer.train()`.
```python
%matplotlib inline
......@@ -233,19 +225,19 @@ def event_handler(event):
trainer.train(
reader=train_reader,
num_epochs=100,
event_handler=event_handler,
event_handler=event_handler_plot,
feed_order=feed_order)
```
![png](./image/train_and_test.png)
### Inference
## Inference
Initialize the Inferencer with the inference_program and the params_folder, which is where we saved our params
Initialize the Inferencer with the inference_program and the params_dirname, which is where we saved our params
#### Setup the Inference Program.
Similar to the trainer.train, the Inferencer needs to take an inference_program to do inferring.
### Setup the Inference Program
Similar to the trainer.train, the Inferencer needs to take an inference_program to do inference.
Prune the train_program to only have the y_predict.
```python
......@@ -255,9 +247,12 @@ def inference_program():
return y_predict
```
### Infer
Inferencer will load the trained model from `params_dirname` and use it to infer the unseen data.
```python
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=params_folder, place=place)
infer_func=inference_program, param_path=params_dirname, place=place)
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
......
01.fit_a_line/index.html
浏览文件 @ fdcd800c
......@@ -155,13 +155,8 @@ We split the dataset in two, one for adjusting the model parameters, namely, for
When training complex models, we usually have one more split: the validation set. Complex models usually have [Hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_optimization) that need to be set before the training process, such as the number of layers in the network. Because hyperparameters are not part of the model parameters, they cannot be trained using the same loss function. Thus we will try several sets of hyperparameters to train several models and cross-validate them on the validation set to pick the best one; finally, the selected trained model is tested on the test set. Because our model is relatively simple, we will omit this validation process.
## Training
`fit_a_line/trainer.py` demonstrates the training using [PaddlePaddle](http://paddlepaddle.org).
### Datafeeder Configuration
We first define data feeders for test and train. The feeder reads a `BATCH_SIZE` of data each time and feed them to the training/testing process. Users can shuffle a batch out of a `buf_size` in order to make the data random.
## Datafeeder Configuration
We first define data feeders for test and train. The feeder reads a `BATCH_SIZE` of data each time and feed them to the training/testing process. If the user wants some randomness on the data order, she can define both a `BATCH_SIZE` and a `buf_size`. That way the datafeeder will yield the first `BATCH_SIZE` data out of a shuffle of the first `buf_size` data.
```python
BATCH_SIZE = 20
......@@ -177,8 +172,12 @@ test_reader = paddle.batch(
batch_size=BATCH_SIZE)
```
## Training
`fit_a_line/trainer.py` demonstrates the training using [PaddlePaddle](http://paddlepaddle.org).
### Train Program Configuration
The train_program must return the avg_loss as its first returned parameter and then use the inference_program to setup the train_program
`train_program` sets up the network structure of this current training model. For linear regression, it is simply a fully connected layer from the input to the output. More complex structures like CNN and RNN will be introduced in later chapters. The `train_program` must return an avg_loss as its first returned parameter because it is needed in backpropagation.
```python
def train_program():
......@@ -204,7 +203,7 @@ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
```
### Create Trainer
The trainer will take the train_program.
The trainer will take the `train_program` as input.
```python
trainer = fluid.Trainer(
......@@ -227,25 +226,24 @@ Moreover, an event handler is provided to print the training progress:
```python
# Specify the directory path to save the parameters
params_folder = "fit_a_line.inference.model"
params_dirname = "fit_a_line.inference.model"
# Plot data
from paddle.v2.plot import Ploter
train_title = "Train cost"
test_title = "Test cost"
plot_cost = Ploter(train_title, test_title)
step = 0
# event_handler to print training and testing info
def event_handler(event):
def event_handler_plot(event):
global step
if isinstance(event, fluid.EndStepEvent):
if step % 100 == 0: # every 100 batches, record a test cost
if event.step % 10 == 0: # every 10 batches, record a test cost
test_metrics = trainer.test(
reader=test_reader, feed_order=feed_order)
print(test_metrics[0])
plot_cost.append(test_title, step, test_metrics[0])
plot_cost.plot()
......@@ -254,14 +252,9 @@ def event_handler(event):
print('loss is less than 10.0, stop')
trainer.stop()
if step >= 2000:
# Or if it has been running for enough steps
print('has been running for 2000 steps, stop')
trainer.stop()
# We can save the trained parameters for the inferences later
if params_folder is not None:
trainer.save_params(params_folder)
if params_dirname is not None:
trainer.save_params(params_dirname)
step += 1
```
......@@ -275,7 +268,7 @@ def event_handler(event):
trainer.train(
reader=train_reader,
num_epochs=100,
event_handler=event_handler,
event_handler=event_handler_plot,
feed_order=feed_order)
```
......@@ -284,10 +277,10 @@ trainer.train(
### Inference
Initialize the Inferencer with the inference_program and the params_folder, which is where we saved our params
Initialize the Inferencer with the inference_program and the params_dirname, which is where we saved our params
#### Setup the Inference Program.
Similar to the trainer.train, the Inferencer needs to take an inference_program to do inferring.
Similar to the trainer.train, the Inferencer needs to take an inference_program to do inference.
Prune the train_program to only have the y_predict.
```python
......@@ -299,7 +292,7 @@ def inference_program():
```python
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path=params_folder, place=place)
infer_func=inference_program, param_path=params_dirname, place=place)
batch_size = 10
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
......
01.fit_a_line/train.py
浏览文件 @ fdcd800c
......@@ -52,27 +52,25 @@ trainer = fluid.Trainer(
feed_order = ['x', 'y']
# Specify the directory path to save the parameters
params_folder = "fit_a_line.inference.model"
params_dirname = "fit_a_line.inference.model"
# Plot data
from paddle.v2.plot import Ploter
train_title = "Train cost"
test_title = "Test cost"
plot_cost = Ploter(train_title, test_title)
step = 0
# event_handler to print training and testing info
def event_handler(event):
def event_handler_plot(event):
global step
if isinstance(event, fluid.EndStepEvent):
if step % 100 == 0: # every 100 batches, record a test cost
if event.step % 10 == 0: # every 10 batches, record a test cost
test_metrics = trainer.test(
reader=test_reader, feed_order=feed_order)
print(test_metrics[0])
plot_cost.append(test_title, step, test_metrics[0])
plot_cost.plot()
......@@ -81,14 +79,9 @@ def event_handler(event):
print('loss is less than 10.0, stop')
trainer.stop()
if step >= 2000:
# Or if it has been running for enough steps
print('has been running for 2000 steps, stop')
trainer.stop()
# We can save the trained parameters for the inferences later
if params_folder is not None:
trainer.save_params(params_folder)
if params_dirname is not None:
trainer.save_params(params_dirname)
step += 1
......@@ -97,7 +90,7 @@ def event_handler(event):
trainer.train(
reader=train_reader,
num_epochs=100,
event_handler=event_handler,
event_handler=event_handler_plot,
feed_order=feed_order)
......
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