@@ -129,15 +129,17 @@ PaddlePaddle provides a Python module, `paddle.dataset.mnist`, which downloads a
...
@@ -129,15 +129,17 @@ PaddlePaddle provides a Python module, `paddle.dataset.mnist`, which downloads a
A PaddlePaddle program starts from importing the API package:
A PaddlePaddle program starts from importing the API package:
```python
```python
importpaddle.v2aspaddle
importpaddle.fluidasfluid
```
```
We want to use this program to demonstrate three different classifiers, each defined as a Python function:
We want to use this program to demonstrate three different classifiers, each defined as a Python function. We need to feed image data to the classifier.
PaddlePaddle provides a special layer `layer.data` for reading data. Let us create a data layer for reading images and connect it to a classification network.
- Softmax regression: the network has a fully-connection layer with softmax activation:
- Softmax regression: the network has a fully-connection layer with softmax activation:
PaddlePaddle provides a special layer `layer.data` for reading data. Let us create a data layer for reading images and connect it to a classification network created using one of above three functions. We also need a cost layer for training the model.
Then we need to setup the the `train_program`. It takes the prediction from the classifier first. During the training, it will calculate the `avg_loss` from the prediction.
Now, it is time to specify training parameters. In the following `Momentum` optimizer, `momentum=0.9` means that 90% of the current momentum comes from that of the previous iteration. The learning rate relates to the speed at which the network training converges. Regularization is meant to prevent over-fitting; here we use the L2 regularization.
Now, we need to setup the trainer. The trainer need to take in `train_program`, `place`, and `optimizer`.
In the following `Momentum` optimizer, `momentum=0.9` means that 90% of the current momentum comes from that of the previous iteration. The learning rate relates to the speed at which the network training converges. Regularization is meant to prevent over-fitting; here we use the L2 regularization.
Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*. A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*. A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
...
@@ -225,35 +217,14 @@ Then we specify the training data `paddle.dataset.mnist.train()` and testing dat
...
@@ -225,35 +217,14 @@ Then we specify the training data `paddle.dataset.mnist.train()` and testing dat
`batch` is a special decorator, which takes a reader and outputs a *batch reader*, which doesn't yield an instance, but a minibatch at a time.
`batch` is a special decorator, which takes a reader and outputs a *batch reader*, which doesn't yield an instance, but a minibatch at a time.
`event_handler_plot` is used to plot a figure like below：
# Test with Pass 0, Cost 0.326659, {'classification_error_evaluator': 0.09470000118017197}
# Test with Pass 0, Cost 0.326659, {'classification_error_evaluator': 0.09470000118017197}
```
```
After the training, we can check the model's prediction accuracy.
After the training, we can check the model's prediction accuracy.
...
@@ -315,27 +287,25 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
...
@@ -315,27 +287,25 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
## Application
## Application
After training, users can use the trained model to classify images. The following code shows how to inference MNIST images through `paddle.infer` interface.
After training, users can use the trained model to classify images. The following code shows how to inference MNIST images through `fluid.Inferencer`.
```python
```python
fromPILimportImage
inferencer=fluid.Inferencer(
importnumpyasnp
# infer_func=softmax_regression, # uncomment for softmax regression
importos
# infer_func=multilayer_perceptron, # uncomment for MLP
defload_image(file):
infer_func=convolutional_neural_network,# uncomment for LeNet5
@@ -171,15 +171,17 @@ PaddlePaddle provides a Python module, `paddle.dataset.mnist`, which downloads a
...
@@ -171,15 +171,17 @@ PaddlePaddle provides a Python module, `paddle.dataset.mnist`, which downloads a
A PaddlePaddle program starts from importing the API package:
A PaddlePaddle program starts from importing the API package:
```python
```python
import paddle.v2 as paddle
import paddle.fluid as fluid
```
```
We want to use this program to demonstrate three different classifiers, each defined as a Python function:
We want to use this program to demonstrate three different classifiers, each defined as a Python function. We need to feed image data to the classifier.
PaddlePaddle provides a special layer `layer.data` for reading data. Let us create a data layer for reading images and connect it to a classification network.
- Softmax regression: the network has a fully-connection layer with softmax activation:
- Softmax regression: the network has a fully-connection layer with softmax activation:
PaddlePaddle provides a special layer `layer.data` for reading data. Let us create a data layer for reading images and connect it to a classification network created using one of above three functions. We also need a cost layer for training the model.
Then we need to setup the the `train_program`. It takes the prediction from the classifier first. During the training, it will calculate the `avg_loss` from the prediction.
Now, it is time to specify training parameters. In the following `Momentum` optimizer, `momentum=0.9` means that 90% of the current momentum comes from that of the previous iteration. The learning rate relates to the speed at which the network training converges. Regularization is meant to prevent over-fitting; here we use the L2 regularization.
Now, we need to setup the trainer. The trainer need to take in `train_program`, `place`, and `optimizer`.
In the following `Momentum` optimizer, `momentum=0.9` means that 90% of the current momentum comes from that of the previous iteration. The learning rate relates to the speed at which the network training converges. Regularization is meant to prevent over-fitting; here we use the L2 regularization.
```python
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
```python
learning_rate=0.1 / 128.0,
use_cude = False # set to True if training with GPU
momentum=0.9,
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*. A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*. A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
...
@@ -267,35 +259,14 @@ Then we specify the training data `paddle.dataset.mnist.train()` and testing dat
...
@@ -267,35 +259,14 @@ Then we specify the training data `paddle.dataset.mnist.train()` and testing dat
`batch` is a special decorator, which takes a reader and outputs a *batch reader*, which doesn't yield an instance, but a minibatch at a time.
`batch` is a special decorator, which takes a reader and outputs a *batch reader*, which doesn't yield an instance, but a minibatch at a time.
`event_handler_plot` is used to plot a figure like below：
```python
```python
from paddle.v2.plot import Ploter
train_reader = paddle.batch(
paddle.reader.shuffle(
train_title = "Train cost"
paddle.dataset.mnist.train(), buf_size=500),
test_title = "Test cost"
batch_size=64)
cost_ploter = Ploter(train_title, test_title)
step = 0
# event_handler to plot a figure
def event_handler_plot(event):
global step
if isinstance(event, paddle.event.EndIteration):
if step % 100 == 0:
cost_ploter.append(train_title, step, event.cost)
cost_ploter.plot()
step += 1
if isinstance(event, paddle.event.EndPass):
# save parameters
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
trainer.save_parameter_to_tar(f)
result = trainer.test(reader=paddle.batch(
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=128))
paddle.dataset.mnist.test(), batch_size=64)
cost_ploter.append(test_title, step, result.cost)
```
```
`event_handler` is used to plot some text data when training.
`event_handler` is used to plot some text data when training.
# Test with Pass 0, Cost 0.326659, {'classification_error_evaluator': 0.09470000118017197}
# Test with Pass 0, Cost 0.326659, {'classification_error_evaluator': 0.09470000118017197}
```
```
After the training, we can check the model's prediction accuracy.
After the training, we can check the model's prediction accuracy.
...
@@ -357,27 +329,25 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
...
@@ -357,27 +329,25 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
## Application
## Application
After training, users can use the trained model to classify images. The following code shows how to inference MNIST images through `paddle.infer` interface.
After training, users can use the trained model to classify images. The following code shows how to inference MNIST images through `fluid.Inferencer`.
```python
```python
from PIL import Image
inferencer = fluid.Inferencer(
import numpy as np
# infer_func=softmax_regression, # uncomment for softmax regression
import os
# infer_func=multilayer_perceptron, # uncomment for MLP
def load_image(file):
infer_func=convolutional_neural_network, # uncomment for LeNet5
