The source code for this tutorial is live at[book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
The source code for this tutorial locates in[book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
## Introduction
When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, the equivalent task is to train a model to recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, an equivalent task is to train a model to recognize hand-written digits using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
The MNIST dataset is created from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students the in U.S. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that there wasn't a complete overlap of annotators of training set and test set.
The MNIST dataset is from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set of 60,000 samples and test set of 10,000 samples. 250 annotators labeled the training set, thus guaranteed that there wasn't a complete overlap of annotators of training set and test set.
Yann LeCun, one of the founders of Deep Learning, has previously made tremendous contributions to handwritten character recognition and proposed the **Convolutional Neural Network** (CNN), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From the LeNet proposal by Yann LeCun, to those winning models in ImageNet competitions, such as VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNNs have achieved a series of impressive results in Image Classification tasks.
The MNIST dataset has been used for evaluating many image recognition algorithms such as a single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet\[[1](#references)\], K-Nearest Neighbors (k-NN) \[[2](#references)\], Support Vector Machine (SVM) \[[3](#references)\], Neural Networks \[[4-7](#references)\], Boosting \[[8](#references)\] and preprocessing methods like distortion removal, noise removal, and blurring. Among these algorithms, the *Convolutional Neural Network* (CNN) has achieved a series of impressive results in Image Classification tasks, including VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial).
Many algorithms are tested on MNIST. In 1998, LeCun experimented with single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet. These algorithms quickly reduced test error from 12% to 0.7% \[[1](#references)\]. Since then, researchers have worked on many algorithms such as **K-Nearest Neighbors** (k-NN) \[[2](#references)\], **Support Vector Machine** (SVM) \[[3](#references)\], **Neural Networks**\[[4-7](#references)\] and **Boosting**\[[8](#references)\]. Various preprocessing methods like distortion removal, noise removal, and blurring, have also been applied to increase recognition accuracy.
In this tutorial, we tackle the task of handwritten character recognition. We start with a simple **softmax** regression model and guide our readers step-by-step to improve this model's performance on the task of recognition.
In this tutorial, we start with a simple **softmax** regression model and go on with MLP and CNN. Readers will see how these methods improve the recognition accuracy step-by-step.
The **convolutional layer** is the core of a Convolutional Neural Network. The parameters in this layer are composed of a set of filters, also called kernels. We could visualize the convolution step in the following fashion: Each kernel slides horizontally and vertically till it covers the whole image. At every window, we compute the dot product of the kernel and the input. Then, we add the bias and apply an activation function. The result is a two-dimensional activation map. For example, some kernel may recognize corners, and some may recognize circles. These convolution kernels may respond strongly to the corresponding features.
Fig. 4 illustrates the dynamic programming of a convolutional layer, where depths are flattened for simplicity. The input is $W_1=5$, $H_1=5$, $D_1=3$. In fact, this is a common representation for colored images. $W_1$ and $H_1$ correspond to the width and height in a colored image. $D_1$ corresponds to the 3 color channels for RGB. The parameters of the convolutional layer are $K=2$, $F=3$, $S=2$, $P=1$. $K$ denotes the number of kernels; specifically, $Filter$ $W_0$ and $Filter$ $W_1$ are the kernels. $F$ is kernel size while $W0$ and $W1$ are both $F\timesF = 3\times3$ matrices in all depths. $S$ is the stride, which is the width of the sliding window; here, kernels move leftwards or downwards by 2 units each time. $P$ is the width of the padding, which denotes an extension of the input; here, the gray area shows zero padding with size 1.
Fig. 4 illustrates the dynamic programming of a convolutional layer, where depths are flattened for simplicity. The input is $W_1=5$, $H_1=5$, $D_1=3$. In fact, this is a common representation for colored images. $W_1$ and $H_1$ correspond to the width and height in a colored image. $D_1$ corresponds to the three color channels for RGB. The parameters of the convolutional layer are $K=2$, $F=3$, $S=2$, $P=1$. $K$ denotes the number of kernels; specifically, $Filter$ $W_0$ and $Filter$ $W_1$ are the kernels. $F$ is kernel size while $W0$ and $W1$ are both $F\timesF = 3\times3$ matrices in all depths. $S$ is the stride, which is the width of the sliding window; here, kernels move leftwards or downwards by two units each time. $P$ is the width of the padding, which denotes an extension of the input; here, the gray area shows zero padding with size 1.
[**LeNet-5**](http://yann.lecun.com/exdb/lenet/) is one of the simplest Convolutional Neural Networks. Fig. 6. shows its architecture: A 2-dimensional input image is fed into two sets of convolutional layers and pooling layers. This output is then fed to a fully connected layer and a softmax classifier. Compared to multilayer, fully connected perceptrons, the LeNet-5 can recognize images better. This is due to the following three properties of the convolution:
- The 3D nature of the neurons: a convolutional layer is organized by width, height and depth. Neurons in each layer are connected to only a small region in the previous layer. This region is called the receptive field.
- The 3D nature of the neurons: a convolutional layer is organized by width, height, and depth. Neurons in each layer are connected to only a small region in the previous layer. This region is called the receptive field.
- Local connectivity: A CNN utilizes the local space correlation by connecting local neurons. This design guarantees that the learned filter has a strong response to local input features. Stacking many such layers generates a non-linear filter that is more global. This enables the network to first obtain good representation for small parts of input and then combine them to represent a larger region.
- Weight sharing: In a CNN, computation is iterated on shared parameters (weights and bias) to form a feature map. This means that all the neurons in the same depth of the output respond to the same feature. This allows the network to detect a feature regardless of its position in the input. In other words, it is shift invariant.
- Weight sharing: In a CNN, computation is iterated on shared parameters (weights and bias) to form a feature map. This means that all the neurons in the same depth of the output response to the same feature. This allows the network to detect a feature regardless of its position in the input.
For more details on Convolutional Neural Networks, please refer to the tutorial on [Image Classification](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md) and the [relevant lecture](http://cs231n.github.io/convolutional-networks/) from a Stanford open course.
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.
`shuffle` is a reader decorator. It takes in a reader A as input and returns a new reader B. Under the hood, B calls A to read data in the following fashion: it copies in `buffer_size` instances at a time into a buffer, shuffles the data, and yields the shuffled instances one at a time. A large buffer size would yield very shuffled data.
`shuffle` is a reader decorator. It takes a reader A as input and returns a new reader B. Under the hood, B calls A to read data in the following fashion: it copies in `buffer_size` instances at a time into a buffer, shuffles the data, and yields the shuffled instances one at a time. A large buffer size would yield very shuffled data.
`batch` is a special decorator, which takes in 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：
...
...
@@ -315,7 +313,7 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
## Application
After training is done, user 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 `paddle.infer` interface.
```python
fromPILimportImage
...
...
@@ -343,15 +341,15 @@ print "Label of image/infer_3.png is: %d" % lab[0][0]
This tutorial describes a few common deep learning models using **Softmax regression**, **Multilayer Perceptron Network**, and **Convolutional Neural Network**. Understanding these models is crucial for future learning; the subsequent tutorials derive more sophisticated networks by building on top of them.
When our model evolves from a simple softmax regression to a slightly complex Convolutional Neural Network, the recognition accuracy on the MNIST dataset achieves a large improvement in accuracy. This is due to the Convolutional layers' local connections and parameter sharing. While learning new models in the future, we encourage the readers to understand the key ideas that lead a new model to improve the results of an old one.
When our model evolves from a simple softmax regression to a slightly complex Convolutional Neural Network, the recognition accuracy on the MNIST dataset achieves a large improvement in accuracy. This is due to the Convolutional layers' local connections and parameter sharing. While learning new models in the future, we encourage the readers to understand the key ideas that lead a new model to improve the results of an old one.
Moreover, this tutorial introduces the basic flow of PaddlePaddle model design, which starts with a *dataprovider*, a model layer construction, and finally training and prediction. Motivated readers can leverage the flow used in this MNIST handwritten digit classification example and experiment with different data and network architectures to train models for classification tasks of their choice.
Moreover, this tutorial introduces the basic flow of PaddlePaddle model design, which starts with a *dataprovider*, a model layer construction, and finally training and prediction. Motivated readers can leverage the flow used in this MNIST handwritten digit classification example and experiment with different data and network architectures to train models for classification tasks of their choice.
## References
1. LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. ["Gradient-based learning applied to document recognition."](http://ieeexplore.ieee.org/abstract/document/726791/) Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
2. Wejéus, Samuel. ["A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones."](http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A753279&dswid=-434)(2014).
2. Wejéus, Samuel. ["A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones."](http://www.diva-portal.org/smash/record.jsf?pid=diva2:753279&dswid=-434)(2014).
3. Decoste, Dennis, and Bernhard Schölkopf. ["Training invariant support vector machines."](http://link.springer.com/article/10.1023/A:1012454411458) Machine learning 46, no. 1-3 (2002): 161-190.
4. Simard, Patrice Y., David Steinkraus, and John C. Platt. ["Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.8494&rep=rep1&type=pdf) In ICDAR, vol. 3, pp. 958-962. 2003.
5. Salakhutdinov, Ruslan, and Geoffrey E. Hinton. ["Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure."](http://www.jmlr.org/proceedings/papers/v2/salakhutdinov07a/salakhutdinov07a.pdf) In AISTATS, vol. 11. 2007.
The source code for this tutorial is live at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
The source code for this tutorial locates in [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
## Introduction
When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, the equivalent task is to train a model to recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, an equivalent task is to train a model to recognize hand-written digits using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
The MNIST dataset is created from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students the in U.S. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that there wasn't a complete overlap of annotators of training set and test set.
The MNIST dataset is from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set of 60,000 samples and test set of 10,000 samples. 250 annotators labeled the training set, thus guaranteed that there wasn't a complete overlap of annotators of training set and test set.
Yann LeCun, one of the founders of Deep Learning, has previously made tremendous contributions to handwritten character recognition and proposed the **Convolutional Neural Network** (CNN), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From the LeNet proposal by Yann LeCun, to those winning models in ImageNet competitions, such as VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNNs have achieved a series of impressive results in Image Classification tasks.
The MNIST dataset has been used for evaluating many image recognition algorithms such as a single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet\[[1](#references)\], K-Nearest Neighbors (k-NN) \[[2](#references)\], Support Vector Machine (SVM) \[[3](#references)\], Neural Networks \[[4-7](#references)\], Boosting \[[8](#references)\] and preprocessing methods like distortion removal, noise removal, and blurring. Among these algorithms, the *Convolutional Neural Network* (CNN) has achieved a series of impressive results in Image Classification tasks, including VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial).
Many algorithms are tested on MNIST. In 1998, LeCun experimented with single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet. These algorithms quickly reduced test error from 12% to 0.7% \[[1](#references)\]. Since then, researchers have worked on many algorithms such as **K-Nearest Neighbors** (k-NN) \[[2](#references)\], **Support Vector Machine** (SVM) \[[3](#references)\], **Neural Networks** \[[4-7](#references)\] and **Boosting** \[[8](#references)\]. Various preprocessing methods like distortion removal, noise removal, and blurring, have also been applied to increase recognition accuracy.
In this tutorial, we tackle the task of handwritten character recognition. We start with a simple **softmax** regression model and guide our readers step-by-step to improve this model's performance on the task of recognition.
In this tutorial, we start with a simple **softmax** regression model and go on with MLP and CNN. Readers will see how these methods improve the recognition accuracy step-by-step.
The **convolutional layer** is the core of a Convolutional Neural Network. The parameters in this layer are composed of a set of filters, also called kernels. We could visualize the convolution step in the following fashion: Each kernel slides horizontally and vertically till it covers the whole image. At every window, we compute the dot product of the kernel and the input. Then, we add the bias and apply an activation function. The result is a two-dimensional activation map. For example, some kernel may recognize corners, and some may recognize circles. These convolution kernels may respond strongly to the corresponding features.
Fig. 4 illustrates the dynamic programming of a convolutional layer, where depths are flattened for simplicity. The input is $W_1=5$, $H_1=5$, $D_1=3$. In fact, this is a common representation for colored images. $W_1$ and $H_1$ correspond to the width and height in a colored image. $D_1$ corresponds to the 3 color channels for RGB. The parameters of the convolutional layer are $K=2$, $F=3$, $S=2$, $P=1$. $K$ denotes the number of kernels; specifically, $Filter$ $W_0$ and $Filter$ $W_1$ are the kernels. $F$ is kernel size while $W0$ and $W1$ are both $F\timesF = 3\times3$ matrices in all depths. $S$ is the stride, which is the width of the sliding window; here, kernels move leftwards or downwards by 2 units each time. $P$ is the width of the padding, which denotes an extension of the input; here, the gray area shows zero padding with size 1.
Fig. 4 illustrates the dynamic programming of a convolutional layer, where depths are flattened for simplicity. The input is $W_1=5$, $H_1=5$, $D_1=3$. In fact, this is a common representation for colored images. $W_1$ and $H_1$ correspond to the width and height in a colored image. $D_1$ corresponds to the three color channels for RGB. The parameters of the convolutional layer are $K=2$, $F=3$, $S=2$, $P=1$. $K$ denotes the number of kernels; specifically, $Filter$ $W_0$ and $Filter$ $W_1$ are the kernels. $F$ is kernel size while $W0$ and $W1$ are both $F\timesF = 3\times3$ matrices in all depths. $S$ is the stride, which is the width of the sliding window; here, kernels move leftwards or downwards by two units each time. $P$ is the width of the padding, which denotes an extension of the input; here, the gray area shows zero padding with size 1.
[**LeNet-5**](http://yann.lecun.com/exdb/lenet/) is one of the simplest Convolutional Neural Networks. Fig. 6. shows its architecture: A 2-dimensional input image is fed into two sets of convolutional layers and pooling layers. This output is then fed to a fully connected layer and a softmax classifier. Compared to multilayer, fully connected perceptrons, the LeNet-5 can recognize images better. This is due to the following three properties of the convolution:
- The 3D nature of the neurons: a convolutional layer is organized by width, height and depth. Neurons in each layer are connected to only a small region in the previous layer. This region is called the receptive field.
- The 3D nature of the neurons: a convolutional layer is organized by width, height, and depth. Neurons in each layer are connected to only a small region in the previous layer. This region is called the receptive field.
- Local connectivity: A CNN utilizes the local space correlation by connecting local neurons. This design guarantees that the learned filter has a strong response to local input features. Stacking many such layers generates a non-linear filter that is more global. This enables the network to first obtain good representation for small parts of input and then combine them to represent a larger region.
- Weight sharing: In a CNN, computation is iterated on shared parameters (weights and bias) to form a feature map. This means that all the neurons in the same depth of the output respond to the same feature. This allows the network to detect a feature regardless of its position in the input. In other words, it is shift invariant.
- Weight sharing: In a CNN, computation is iterated on shared parameters (weights and bias) to form a feature map. This means that all the neurons in the same depth of the output response to the same feature. This allows the network to detect a feature regardless of its position in the input.
For more details on Convolutional Neural Networks, please refer to the tutorial on [Image Classification](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md) and the [relevant lecture](http://cs231n.github.io/convolutional-networks/) from a Stanford open course.
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.
`shuffle` is a reader decorator. It takes in a reader A as input and returns a new reader B. Under the hood, B calls A to read data in the following fashion: it copies in `buffer_size` instances at a time into a buffer, shuffles the data, and yields the shuffled instances one at a time. A large buffer size would yield very shuffled data.
`shuffle` is a reader decorator. It takes a reader A as input and returns a new reader B. Under the hood, B calls A to read data in the following fashion: it copies in `buffer_size` instances at a time into a buffer, shuffles the data, and yields the shuffled instances one at a time. A large buffer size would yield very shuffled data.
`batch` is a special decorator, which takes in 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：
...
...
@@ -357,7 +355,7 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
## Application
After training is done, user 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 `paddle.infer` interface.
```python
from PIL import Image
...
...
@@ -385,15 +383,15 @@ print "Label of image/infer_3.png is: %d" % lab[0][0]
This tutorial describes a few common deep learning models using **Softmax regression**, **Multilayer Perceptron Network**, and **Convolutional Neural Network**. Understanding these models is crucial for future learning; the subsequent tutorials derive more sophisticated networks by building on top of them.
When our model evolves from a simple softmax regression to a slightly complex Convolutional Neural Network, the recognition accuracy on the MNIST dataset achieves a large improvement in accuracy. This is due to the Convolutional layers' local connections and parameter sharing. While learning new models in the future, we encourage the readers to understand the key ideas that lead a new model to improve the results of an old one.
When our model evolves from a simple softmax regression to a slightly complex Convolutional Neural Network, the recognition accuracy on the MNIST dataset achieves a large improvement in accuracy. This is due to the Convolutional layers' local connections and parameter sharing. While learning new models in the future, we encourage the readers to understand the key ideas that lead a new model to improve the results of an old one.
Moreover, this tutorial introduces the basic flow of PaddlePaddle model design, which starts with a *dataprovider*, a model layer construction, and finally training and prediction. Motivated readers can leverage the flow used in this MNIST handwritten digit classification example and experiment with different data and network architectures to train models for classification tasks of their choice.
Moreover, this tutorial introduces the basic flow of PaddlePaddle model design, which starts with a *dataprovider*, a model layer construction, and finally training and prediction. Motivated readers can leverage the flow used in this MNIST handwritten digit classification example and experiment with different data and network architectures to train models for classification tasks of their choice.
## References
1. LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. ["Gradient-based learning applied to document recognition."](http://ieeexplore.ieee.org/abstract/document/726791/) Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
2. Wejéus, Samuel. ["A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones."](http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A753279&dswid=-434) (2014).
2. Wejéus, Samuel. ["A Neural Network Approach to Arbitrary SymbolRecognition on Modern Smartphones."](http://www.diva-portal.org/smash/record.jsf?pid=diva2:753279&dswid=-434) (2014).
3. Decoste, Dennis, and Bernhard Schölkopf. ["Training invariant support vector machines."](http://link.springer.com/article/10.1023/A:1012454411458) Machine learning 46, no. 1-3 (2002): 161-190.
4. Simard, Patrice Y., David Steinkraus, and John C. Platt. ["Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis."](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.8494&rep=rep1&type=pdf) In ICDAR, vol. 3, pp. 958-962. 2003.
5. Salakhutdinov, Ruslan, and Geoffrey E. Hinton. ["Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure."](http://www.jmlr.org/proceedings/papers/v2/salakhutdinov07a/salakhutdinov07a.pdf) In AISTATS, vol. 11. 2007.
This is intended as a reference tutorial. The source code of this tutorial lives on[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec).
This is intended as a reference tutorial. The source code of this tutorial is located at[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec).
For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
## Background Introduction
This section introduces the concept of **word embedding**, which is a vector representation of words. It is a popular technique used in natural language processing. Word embeddings support many Internet services, including search engines, advertising systems, and recommendation systems.
This section introduces the concept of **word embeddings**, which are vector representations of words. Word embeddings is a popular technique used in natural language processing to support applications such as search engines, advertising systems, and recommendation systems.
### One-Hot Vectors
Building these services requires us to quantify the similarity between two words or paragraphs. This calls for a new representation of all the words to make them more suitable for computation. An obvious way to achieve this is through the vector space model, where every word is represented as an **one-hot vector**.
Building these applications requires us to quantify the similarity between two words or paragraphs. This calls for a new representation of all the words to make them more suitable for computation. An obvious way to achieve this is through the vector space model, where every word is represented as an **one-hot vector**.
For each word, its vector representation has the corresponding entry in the vector as 1, and all other entries as 0. The lengths of one-hot vectors match the size of the dictionary. Each entry of a vector corresponds to the presence (or absence) of a word in the dictionary.
One-hot vectors are intuitive, yet they have limited usefulness. Take the example of an Internet advertising system: Suppose a customer enters the query "Mother's Day", while an ad bids for the keyword carnations". Because the one-hot vectors of these two words are perpendicular, the metric distance (either Euclidean or cosine similarity) between them would indicate little relevance. However, *we* know that these two queries are connected semantically, since people often gift their mothers bundles of carnation flowers on Mother's Day. This discrepancy is due to the low information capacity in each vector. That is, comparing the vector representations of two words does not assess their relevance sufficiently. To calculate their similarity accurately, we need more information, which could be learned from large amounts of data through machine learning methods.
One-hot vectors are intuitive, yet they have limited usefulness. Take the example of an Internet advertising system: Suppose a customer enters the query "Mother's Day", while an ad bids for the keyword "carnations". Because the one-hot vectors of these two words are perpendicular, the metric distance (either Euclidean or cosine similarity) between them would indicate little relevance. However, *we* know that these two queries are connected semantically, since people often gift their mothers bundles of carnation flowers on Mother's Day. This discrepancy is due to the low information capacity in each vector. That is, comparing the vector representations of two words does not assess their relevance sufficiently. To calculate their similarity accurately, we need more information, which could be learned from large amounts of data through machine learning methods.
Like many machine learning models, word embeddings can represent knowledge in various ways. Another model may project an one-hot vector to an embedding vector of lower dimension e.g. $embedding(mother's day) = [0.3, 4.2, -1.5, ...], embedding(carnations) = [0.2, 5.6, -2.3, ...]$. Mapping one-hot vectors onto an embedded vector space has the potential to bring the embedding vectors of similar words (either semantically or usage-wise) closer to each other, so that the cosine similarity between the corresponding vectors for words like "Mother's Day" and "carnations" are no longer zero.
...
...
@@ -33,7 +33,7 @@ The neural network based model does not require storing huge hash tables of stat
## Results Demonstration
In this section, after training the word embedding model, we could use the data visualization algorithm $t-$SNE\[[4](#reference)\] to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we could see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
In this section, we use the $t-$SNE\[[4](#reference)\] data visualization algorithm to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we can see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
@@ -52,14 +52,14 @@ please input two words: from company
similarity: -0.0997506977351
```
The above results could be obtained by running `calculate_dis.py`, which loads the words in the dictionary and their corresponding trained word embeddings. For detailed instruction, see section [Model Application](#Model Application).
The above results could be obtained by running `calculate_dis.py`, which loads the words in the dictionary and their corresponding trained word embeddings. For detailed instruction, see section [Model Application](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec#model-application).
## Model Overview
In this section, we will introduce three word embedding models: N-gram model, CBOW, and Skip-gram, which all output the frequency of each word given its immediate context.
For N-gram model, we will first introduce the concept of language model, and implement it using PaddlePaddle in section [Model Training](#Model Training).
For N-gram model, we will first introduce the concept of language model, and implement it using PaddlePaddle in section [Training](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec#model-application).
The latter two models, which became popular recently, are neural word embedding model developed by Tomas Mikolov at Google \[[3](#reference)\]. Despite their apparent simplicity, these models train very well.
...
...
@@ -93,7 +93,7 @@ Given some real corpus in which all sentences are meaningful, the n-gram model s
where $f(w_t, w_{t-1}, ..., w_{t-n+1})$ represents the conditional probability of the current word $w_t$ given its previous $n-1$ words, and $R(\theta)$ represents parameter regularization term.
where $f(w_t, w_{t-1}, ..., w_{t-n+1})$ represents the conditional logarithmic probability of the current word $w_t$ given its previous $n-1$ words, and $R(\theta)$ represents parameter regularization term.
<palign="center">
<imgsrc="image/nnlm_en.png"width=500><br/>
...
...
@@ -151,7 +151,7 @@ As illustrated in the figure above, skip-gram model maps the word embedding of t
## Dataset
We will use Peen Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#reference)\]. Its statistics are as follows:
We will use Penn Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#reference)\]. Its statistics are as follows:
<palign="center">
<table>
...
...
@@ -346,7 +346,7 @@ After 30 passes, we can get average error rate around 0.735611.
## Model Application
After the model is trained, we can load saved model parameters and uses it for other models. We can also use the parameters in applications.
After the model is trained, we can load the saved model parameters and use it for other models. We can also use the parameters in various applications.
This chapter introduces word embedding, the relationship between language model and word embedding, and how to train neural networks to learn word embedding.
This chapter introduces word embeddings, the relationship between language model and word embedding, and how to train neural networks to learn word embedding.
In information retrieval, the relevance between the query and document keyword can be computed through the cosine similarity of their word embeddings. In grammar analysis and semantic analysis, a previously trained word embedding can initialize models for better performance. In document classification, clustering the word embedding can group synonyms in the documents. We hope that readers can use word embedding models in their work after reading this chapter.
This is intended as a reference tutorial. The source code of this tutorial lives on [book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec).
This is intended as a reference tutorial. The source code of this tutorial is located at [book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec).
For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
## Background Introduction
This section introduces the concept of **word embedding**, which is a vector representation of words. It is a popular technique used in natural language processing. Word embeddings support many Internet services, including search engines, advertising systems, and recommendation systems.
This section introduces the concept of **word embeddings**, which are vector representations of words. Word embeddings is a popular technique used in natural language processing to support applications such as search engines, advertising systems, and recommendation systems.
### One-Hot Vectors
Building these services requires us to quantify the similarity between two words or paragraphs. This calls for a new representation of all the words to make them more suitable for computation. An obvious way to achieve this is through the vector space model, where every word is represented as an **one-hot vector**.
Building these applications requires us to quantify the similarity between two words or paragraphs. This calls for a new representation of all the words to make them more suitable for computation. An obvious way to achieve this is through the vector space model, where every word is represented as an **one-hot vector**.
For each word, its vector representation has the corresponding entry in the vector as 1, and all other entries as 0. The lengths of one-hot vectors match the size of the dictionary. Each entry of a vector corresponds to the presence (or absence) of a word in the dictionary.
One-hot vectors are intuitive, yet they have limited usefulness. Take the example of an Internet advertising system: Suppose a customer enters the query "Mother's Day", while an ad bids for the keyword carnations". Because the one-hot vectors of these two words are perpendicular, the metric distance (either Euclidean or cosine similarity) between them would indicate little relevance. However, *we* know that these two queries are connected semantically, since people often gift their mothers bundles of carnation flowers on Mother's Day. This discrepancy is due to the low information capacity in each vector. That is, comparing the vector representations of two words does not assess their relevance sufficiently. To calculate their similarity accurately, we need more information, which could be learned from large amounts of data through machine learning methods.
One-hot vectors are intuitive, yet they have limited usefulness. Take the example of an Internet advertising system: Suppose a customer enters the query "Mother's Day", while an ad bids for the keyword "carnations". Because the one-hot vectors of these two words are perpendicular, the metric distance (either Euclidean or cosine similarity) between them would indicate little relevance. However, *we* know that these two queries are connected semantically, since people often gift their mothers bundles of carnation flowers on Mother's Day. This discrepancy is due to the low information capacity in each vector. That is, comparing the vector representations of two words does not assess their relevance sufficiently. To calculate their similarity accurately, we need more information, which could be learned from large amounts of data through machine learning methods.
Like many machine learning models, word embeddings can represent knowledge in various ways. Another model may project an one-hot vector to an embedding vector of lower dimension e.g. $embedding(mother's day) = [0.3, 4.2, -1.5, ...], embedding(carnations) = [0.2, 5.6, -2.3, ...]$. Mapping one-hot vectors onto an embedded vector space has the potential to bring the embedding vectors of similar words (either semantically or usage-wise) closer to each other, so that the cosine similarity between the corresponding vectors for words like "Mother's Day" and "carnations" are no longer zero.
...
...
@@ -75,7 +75,7 @@ The neural network based model does not require storing huge hash tables of stat
## Results Demonstration
In this section, after training the word embedding model, we could use the data visualization algorithm $t-$SNE\[[4](#reference)\] to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we could see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
In this section, we use the $t-$SNE\[[4](#reference)\] data visualization algorithm to draw the word embedding vectors after projecting them onto a two-dimensional space (see figure below). From the figure we can see that the semantically relevant words -- *a*, *the*, and *these* or *big* and *huge* -- are close to each other in the projected space, while irrelevant words -- *say* and *business* or *decision* and *japan* -- are far from each other.
@@ -94,14 +94,14 @@ please input two words: from company
similarity: -0.0997506977351
```
The above results could be obtained by running `calculate_dis.py`, which loads the words in the dictionary and their corresponding trained word embeddings. For detailed instruction, see section [Model Application](#Model Application).
The above results could be obtained by running `calculate_dis.py`, which loads the words in the dictionary and their corresponding trained word embeddings. For detailed instruction, see section [Model Application](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec#model-application).
## Model Overview
In this section, we will introduce three word embedding models: N-gram model, CBOW, and Skip-gram, which all output the frequency of each word given its immediate context.
For N-gram model, we will first introduce the concept of language model, and implement it using PaddlePaddle in section [Model Training](#Model Training).
For N-gram model, we will first introduce the concept of language model, and implement it using PaddlePaddle in section [Training](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec#model-application).
The latter two models, which became popular recently, are neural word embedding model developed by Tomas Mikolov at Google \[[3](#reference)\]. Despite their apparent simplicity, these models train very well.
...
...
@@ -135,7 +135,7 @@ Given some real corpus in which all sentences are meaningful, the n-gram model s
where $f(w_t, w_{t-1}, ..., w_{t-n+1})$ represents the conditional probability of the current word $w_t$ given its previous $n-1$ words, and $R(\theta)$ represents parameter regularization term.
where $f(w_t, w_{t-1}, ..., w_{t-n+1})$ represents the conditional logarithmic probability of the current word $w_t$ given its previous $n-1$ words, and $R(\theta)$ represents parameter regularization term.
<palign="center">
<imgsrc="image/nnlm_en.png"width=500><br/>
...
...
@@ -193,7 +193,7 @@ As illustrated in the figure above, skip-gram model maps the word embedding of t
## Dataset
We will use Peen Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#reference)\]. Its statistics are as follows:
We will use Penn Treebank (PTB) (Tomas Mikolov's pre-processed version) dataset. PTB is a small dataset, used in Recurrent Neural Network Language Modeling Toolkit\[[2](#reference)\]. Its statistics are as follows:
<palign="center">
<table>
...
...
@@ -388,7 +388,7 @@ After 30 passes, we can get average error rate around 0.735611.
## Model Application
After the model is trained, we can load saved model parameters and uses it for other models. We can also use the parameters in applications.
After the model is trained, we can load the saved model parameters and use it for other models. We can also use the parameters in various applications.
This chapter introduces word embedding, the relationship between language model and word embedding, and how to train neural networks to learn word embedding.
This chapter introduces word embeddings, the relationship between language model and word embedding, and how to train neural networks to learn word embedding.
In information retrieval, the relevance between the query and document keyword can be computed through the cosine similarity of their word embeddings. In grammar analysis and semantic analysis, a previously trained word embedding can initialize models for better performance. In document classification, clustering the word embedding can group synonyms in the documents. We hope that readers can use word embedding models in their work after reading this chapter.
The source code of this tutorial is in [book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system).
For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
The source code from this tutorial is at [here](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system). For instructions to run it, please refer to [this guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
## Background
With the fast growth of e-commerce, online videos, and online reading business, users have to rely on recommender systems to avoid manually browsing tremendous volume of choices. Recommender systems understand users' interest by mining user behavior and other properties of users and products.
Some well know approaches include:
- User behavior-based approach. A well-known method is collaborative filtering. The underlying assumption is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person.
The recommender system is a component of e-commerce, online videos, and online reading services. There are several different approaches for recommender systems to learn from user behavior and product properties and to understand users' interests.
-Content-based recommendation[[1](#reference)]. This approach infers feature vectors that represent products from their descriptions. It also infers feature vectors that represent users' interests. Then it measures the relevance of users and products by some distances between these feature vectors.
-User behavior-based approach. A well-known method of this approach is collaborative filtering, which assumes that if two users made similar purchases, they share common interests and would likely go on making the same decision. Some variants of collaborative filtering are user-based[[3](#reference)], item-based [[4](#reference)], social network based[[5](#reference)], and model-based.
-Hybrid approach[[2](#reference)]: This approach uses the content-based information to help address the cold start problem[[6](#reference)] in behavior-based approach.
-Content-based approach[[1](#reference)]. This approach represents product properties and user interests as feature vectors of the same space so that it could measure how much a user is interested in a product by the distance between two feature vectors.
Among these options, collaborative filtering might be the most studied one. Some of its variants include user-based[[3](#reference)], item-based [[4](#reference)], social network based[[5](#reference)], and model-based.
- Hybrid approach[[2](#reference)]: This one combines above two to help with each other about the data sparsity problem[[6](#reference)].
This tutorial explains a deep learning based approach and how to implement it using PaddlePaddle. We will train a model using a dataset that includes user information, movie information, and ratings. Once we train the model, we will be able to get a predicted rating given a pair of user and movie IDs.
This tutorial explains a deep learning based hybrid approach and its implement in PaddlePaddle. We are going to train a model using a dataset that includes user information, movie information, and ratings. Once we train the model, we will be able to get a predicted rating given a pair of user and movie IDs.
The source code of this tutorial is in [book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system).
For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
The source code from this tutorial is at [here](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system). For instructions to run it, please refer to [this guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
## Background
With the fast growth of e-commerce, online videos, and online reading business, users have to rely on recommender systems to avoid manually browsing tremendous volume of choices. Recommender systems understand users' interest by mining user behavior and other properties of users and products.
Some well know approaches include:
- User behavior-based approach. A well-known method is collaborative filtering. The underlying assumption is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person.
The recommender system is a component of e-commerce, online videos, and online reading services. There are several different approaches for recommender systems to learn from user behavior and product properties and to understand users' interests.
- Content-based recommendation[[1](#reference)]. This approach infers feature vectors that represent products from their descriptions. It also infers feature vectors that represent users' interests. Then it measures the relevance of users and products by some distances between these feature vectors.
- User behavior-based approach. A well-known method of this approach is collaborative filtering, which assumes that if two users made similar purchases, they share common interests and would likely go on making the same decision. Some variants of collaborative filtering are user-based[[3](#reference)], item-based [[4](#reference)], social network based[[5](#reference)], and model-based.
- Hybrid approach[[2](#reference)]: This approach uses the content-based information to help address the cold start problem[[6](#reference)] in behavior-based approach.
- Content-based approach[[1](#reference)]. This approach represents product properties and user interests as feature vectors of the same space so that it could measure how much a user is interested in a product by the distance between two feature vectors.
Among these options, collaborative filtering might be the most studied one. Some of its variants include user-based[[3](#reference)], item-based [[4](#reference)], social network based[[5](#reference)], and model-based.
- Hybrid approach[[2](#reference)]: This one combines above two to help with each other about the data sparsity problem[[6](#reference)].
This tutorial explains a deep learning based approach and how to implement it using PaddlePaddle. We will train a model using a dataset that includes user information, movie information, and ratings. Once we train the model, we will be able to get a predicted rating given a pair of user and movie IDs.
This tutorial explains a deep learning based hybrid approach and its implement in PaddlePaddle. We are going to train a model using a dataset that includes user information, movie information, and ratings. Once we train the model, we will be able to get a predicted rating given a pair of user and movie IDs.
