Last week, while looking up information about distributed deep learning operations, I accidentally stumbled upon PaddlePaddle and found its distributed training features quite impressive. I wanted to share this with you. However, the content is quite complex, so I’ll start by introducing the first "hello world" example in PaddlePaddle—MNIST handwritten digit recognition. In the next article, I'll dive into the details of distributed training with PaddlePaddle. I also wrote an article earlier using CNN for handwritten digit recognition with Keras. Now that I've used PaddlePaddle, I can compare the two frameworks more directly.
What is PaddlePaddle?
PaddlePaddle is a deep learning framework developed by Baidu. While many people are familiar with TensorFlow, Caffe, or MXNet, PaddlePaddle is also a powerful tool. It was previously known as Paddle, but now it's called PaddlePaddle. Although the name change feels a bit strange, the framework itself is robust and user-friendly.
What can PaddlePaddle do?
It supports traditional machine learning tasks, especially in natural language processing (NLP), such as sentiment analysis, word embeddings, and language models. You can use it for various applications, and it’s definitely worth experimenting with.
How to install PaddlePaddle?
The official website mentions Docker as the only supported method, which isn't very popular in China. Most other frameworks offer easy installation via pip, so this approach might seem surprising at first. However, I found that installing via pip is possible even though it's not explicitly stated on the site. For beginners, the easiest way is:
- CPU version: `pip install paddlepaddle`
- GPU version: `pip install paddlepaddle-gpu`
Handwritten Digit Recognition with PaddlePaddle
Training steps include importing data, defining the network structure, training the model, saving it, and testing the results. Here's the code for the MNIST example:
```python
# coding:utf-8
import os
from PIL import Image
import numpy as np
import paddle.v2 as paddle
with_gpu = os.getenv('WITH_GPU', '0') != '1'
def convolutional_neural_network(img):
conv_pool_1 = paddle.networks.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
num_channel=1,
pool_size=2,
pool_stride=2,
act=paddle.activation.Relu()
)
conv_pool_2 = paddle.networks.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
num_channel=20,
pool_size=2,
pool_stride=2,
act=paddle.activation.Relu()
)
predict = paddle.layer.fc(
input=conv_pool_2,
size=10,
act=paddle.activation.Softmax()
)
return predict
def main():
paddle.init(use_gpu=with_gpu, trainer_count=1)
images = paddle.layer.data(name='pixel', type=paddle.data_type.dense_vector(784))
label = paddle.layer.data(name='label', type=paddle.data_type.integer_value(10))
predict = convolutional_neural_network(images)
cost = paddle.layer.classification_cost(input=predict, label=label)
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1 / 128.0,
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128)
)
trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=optimizer)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
print("Pass %d, Batch %d, Cost %f, %s" % (event.pass_id, event.batch_id, event.cost, event.metrics))
elif isinstance(event, paddle.event.EndPass):
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=paddle.batch(paddle.dataset.mnist.test(), batch_size=128))
print("Test with Pass %d, Cost %f, %s" % (event.pass_id, result.cost, result.metrics))
trainer.train(
reader=paddle.batch(paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=8192), batch_size=128),
event_handler=event_handler,
num_passes=10
)
if __name__ == '__main__':
main()
```
This code defines a simple CNN for MNIST classification. The output shows a high accuracy of 98.79% after just 5 passes, which is impressive. I then improved the model by adding dropout layers and batch normalization, leading to an accuracy of 99.28%. Compared to Keras, PaddlePaddle is much faster while maintaining similar accuracy.
In summary, PaddlePaddle is easy to use, fast, and offers great flexibility. Its event handler mechanism allows custom logging, making it more transparent than some other frameworks. While documentation could be better, the community is growing, and the framework has strong potential, especially for distributed training. I plan to write more articles on real-world applications, moving beyond basic examples like “hello world.†Stay tuned!
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