Source code of this chapter is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits) For the first-time use, please refer to PaddlePaddle [installation instructions](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html).
Source code of this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits) For the first-time use, please refer to PaddlePaddle [installation instructions](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html).
## Background
When we study programming, the first program is usually printing “Hello World.” In Machine Learning, or Deep Learning, this is hand-written digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains hand-written digits and corresponding labels (Fig. 1). An image is a 28x28 matrix, and a label corresponds to one of the 10 digits from 0 to 9. Each image is normalized in size and centered.
## Introduction
When we learn programming, the first program is usually printing “Hello World.” In Machine Learning, or Deep Learning, this is handwritten digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains handwritten digits and corresponding labels (Fig. 1). An image is a 28x28 matrix, and a label corresponds to one of the 10 digits from 0 to 9. Each image is normalized in size and centered.
MNIST dataset is made from [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and Special Database 1 (SD-1). Since SD-3 is labeled by staffs in U.S. Census Bureau, while SD-1 is labeled by high school students in U.S., SD-3 is cleaner and easier to recognize than SD-1 is. Yann LeCun et al. extracted half of samples from each of SD-1 and SD-3 for 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 annotators of training set and test set are not completely overlapped.
MNIST dataset is made from [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and Special Database 1 (SD-1). Since SD-3 is labeled by staffs in U.S. Census Bureau, while SD-1 is labeled by high school students in U.S., SD-3 is cleaner and easier to recognize than SD-1 is. Yann LeCun et al. used half of samples from each of SD-1 and SD-3 for 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 annotators of training set and test set are not completely overlapped.
Yann LeCun, one of the founders of Deep Learning, had large contribution on hand-written character recognition in early dates, and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for hand-written characters. CNN is now a critical key for Deep Learning. From Yann LeCun’s first proposal of LeNet, to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification) chapter) CNN achieved a series of astonishing results in Image Classification.
Yann LeCun, one of the founders of Deep Learning, had huge contribution on handwritten character recognition in early dates, and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for handwritten characters. CNN is now a critical key for Deep Learning. From Yann LeCun’s first proposal of LeNet, to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification) tutorial.) CNN achieved a series of impressive results in Image Classification tasks.
Many algorithms are tested on MNIST. In 1998, LeCun experimented single layer linear classifier, MLP (Multilayer Perceptron) and Multilayer CNN LeNet, which continuously reduced test error from 12% to 0.7% \[[1](#References)\]. Since then, researchers worked on many algorithms such as k-NN (K-Nearest Neighbors) \[[2](#References)\], Support Vector Machine (SVM) \[[3](#References)\], Neural Networks \[[4-7](#References)\] and Boosting \[[8](#References)\], and applied various preprocessing methods, such as distortion removal, noise removal and blurring, to increase recognition accuracy.
Many algorithms are tested on MNIST. In 1998, LeCun experimented single layer linear classifier, MLP (Multilayer Perceptron) and Multilayer CNN LeNet. These algorithms constantly reduced test error from 12% to 0.7% \[[1](#References)\]. Since then, researchers worked on many algorithms such as k-NN (K-Nearest Neighbors) \[[2](#References)\], Support Vector Machine (SVM) \[[3](#References)\], Neural Networks \[[4-7](#References)\] and Boosting \[[8](#References)\], and applied various preprocessing methods, such as distortion removal, noise removal and blurring, to increase recognition accuracy.
In this chapter, we start from simple softmax regression model, and guide readers to introduction of hand-written character recognition, and gradual improvement of models.
In this tutorial, we start from simple softmax regression model, and guide readers to introduction of handwritten character recognition, and step-by-step improvement of models.
## Model Overview
Before introducing the classification algorithms and training procedure, we provide some definitions:
- $X$ is input: MNIST image is $28\times28$ two dimensional matrix. It is reshaped to $784$ dimensional vector. $X=\left ( x_0, x_1, \dots, x_{783} \right )$.
- $Y$ is output: Output of classifier is 10 class digits from 0 to 9. $Y=\left ( y_0, y_1, \dots, y_9 \right )$, Each dimension $y_i$ represents a probability that the image belongs to $i$.
- $L$ is a image's ground truth label: $L=\left ( l_0, l_1, \dots, l_9 \right )$ It is also 10 dimensional, but only one dimension is 1 and others are all 0.
Before introducing classification algorithms and training procedure, we provide some definitions:
- $X$ is input: Input is $28\times28$ MNIST image. It is flattened to $784$ dimensional vector. $X=\left ( x_0, x_1, \dots, x_{783} \right )$.
- $Y$ is output: Output of classifier is 10 class digits from 0 to 9. $Y=\left ( y_0, y_1, \dots, y_9 \right )$. Each dimension $y_i$ represents a probability that the image belongs to $i$.
- $L$ is ground truth label: $L=\left ( l_0, l_1, \dots, l_9 \right )$ It is also 10 dimensional, but only one dimension is 1 and others are all 0.
### Softmax Regression
The simplest softmax regression model is to feed input to fully connected layers, and directly use softmax for multi-class classification \[[9](#References)\].
The simplest softmax regression model is to feed input into fully connected layers, and directly use softmax for multiclass classification \[[9](#References)\].
Input $X$ is multiplied with weights $W$, added by bias $b$, and activated.
where $ softmax(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}} $
For a $N$ class classification problem with $N$ output nodes, a $N$ dimensional input features is normalized to $N$ real values in [0, 1], each representing the probability of the sample to belong to the class. Here $y_i$ is the prediction probability that an image is digit $i$.
For a $N$ class classification problem with $N$ output nodes, a $N$ dimensional vector is normalized to $N$ real values in [0, 1], each representing the probability of the sample to belong to the class. Here $y_i$ is the prediction probability that an image is digit $i$.
In classification problem, we usually use cross entropy loss function:
Softmax regression model uses the simplest two layer neural network, i.e. it only contains input layer and output layer, so that it's regression ability is limited. To achieve better recognition effect, we consider adding several hidden layers \[[10](#References)\] between the input layer and the output layer.
1. After the first hidden layer, we get $ H_1 = \phi(W_1X + b_1) $, where $\phi$ represents activation function. Some common ones are sigmoid, tanh and ReLU.
1. After the first hidden layer, we get $ H_1 = \phi(W_1X + b_1) $, where $\phi$ is activation function. Some common ones are sigmoid, tanh and ReLU.
2. After the second hidden layer, we get $ H_2 = \phi(W_2H_1 + b_2) $.
3. Finally, after output layer, we get $Y=softmax(W_3H_2 + b_3)$, the last classification result vector.
Fig. 3. is Multilayer perceptron network, with weights in black, and bias in red. +1 indicates bias is 1.
Fig. 3. is Multilayer Perceptron network, with weights in black, and bias in red. +1 indicates bias is 1.
Convolutional layer is the core of Convolutional Neural Networks. The parameters in this layer are composed of a set of filters, or kernels. In forward step, each kernel moves horizontally and vertically, and compute dot product of the kernel and input on corresponding positions, then add bias and apply activation function. The result is 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.
Convolutional layer is the core of Convolutional Neural Network. The parameters in this layer are composed of a set of filters, or kernels. In forward step, each kernel moves horizontally and vertically, and compute dot product of the kernel and the input on corresponding positions, then add bias and apply activation function. The result is 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 is a dynamic graph for a convolutional layer, where depths are not shown for simplicity. Input is $W_1=5,H_1=5,D_1=3$. In fact, this is a common representation for colored images. The width and height of a colored image corresponds to $W_1$ and $H_1$, and the 3 color channels for RGB corresponds to $D_1$. The parameters of convolutional layers are $K=2,F=3,S=2,P=1$. $K$ is the number of kernels. Here, $Filter W_0$ and $Filter W_1$ are two convolution kernels. $F$ is kernel size. $W0$ and $W1$ are both $3\times3$ matrix in all depths. $S$ is stride. Kernels moves leftwards or downwards by 2 units each time. $P$ is padding, which is the extension for the input.
Fig. 4 is a dynamic graph of convolutional layer, where depths are not shown for simplicity. Input is $W_1=5,H_1=5,D_1=3$. In fact, this is a common representation for colored images. The width and height of a colored image corresponds to $W_1$ and $H_1$, and the 3 color channels for RGB corresponds to $D_1$. The parameters of convolutional layers are $K=2,F=3,S=2,P=1$. $K$ is the number of kernels. Here, $Filter W_0$ and $Filter W_1$ are two convolution kernels. $F$ is kernel size. $W0$ and $W1$ are both $3\times3$ matrix in all depths. $S$ is stride. Kernels moves leftwards or downwards by 2 units each time. $P$ is padding, the extension for the input.
#### Pooling Layer
...
...
@@ -83,7 +83,7 @@ Fig. 4 is a dynamic graph for a convolutional layer, where depths are not shown
Fig. 5 Pooling layer<br/>
</p>
Pooling layer is a sampling method. The main functionality is to reduce computation by reducing network parameters. It also prevents over-fitting to some extent. Usually, a pooling layer is added after a convolutional layer. Pooling layer includes max pooling, average pooling, etc. Max pooling uses rectangles to divide input layer into several parts, and compute maximum value in each part as output (Fig. 5.)
Pooling layer performs downsampling. The main functionality is to reduce computation by reducing network parameters. It also prevents over-fitting to some extent. Usually, a pooling layer is added after a convolutional layer. Pooling layer includes max pooling, average pooling, etc. Max pooling uses rectangles to divide input layer into several parts, and compute maximum value in each part as output (Fig. 5.)
#### LeNet-5 Network
...
...
@@ -92,20 +92,20 @@ Pooling layer is a sampling method. The main functionality is to reduce computat
[LeNet-5](http://yann.lecun.com/exdb/lenet/) is one of the simplest Convolutional Neural Networks. Fig. 6. shows its architecture: 2 dimensional image input is fed into two sets of convolutional layer and pooling layer, then it is fed into fully connected layer and softmax classifier. The following three properties of convolution enable LeNet-5 to better recognize images than Multilayer fully-connected perceptrons:
[LeNet-5](http://yann.lecun.com/exdb/lenet/) is one of the simplest Convolutional Neural Network. Fig. 6. shows its architecture: 2 dimensional image input is fed into two sets of convolutional layer and pooling layer, then it is fed into fully connected layer and softmax classifier. The following three properties of convolution enable LeNet-5 to better recognize images than Multilayer fully-connected perceptrons:
- 3D properties of neurons: a convolutional layer is organized by width, height and depth. Neurons in each layer are connected to only a small region in previous layer. This region is called receptive field.
- Local connection: CNN utilizes local space correlation by connecting local neurons. This design guarantees learned filter has strong response to local input features. Stacking many such layers leads non-linear filter becomes more and more global. This allows the network to first obtain good representation for a small parts of input, then combine them to represent larger region.
- Sharing weights: In CNN, computation is iterated with shared parameters (weights and bias) to form afeature map. This means all neurons in the same depth of output respond to the same feature. This allows detecting a feature regardless of its position in the input, and enables a property of translation equivariance.
- Sharing weights: In CNN, computation is iterated with shared parameters (weights and bias) to form afeature map. This means all neurons in the same depth of output respond to the same feature. This allows detecting a feature regardless of its position in the input, and enables a property of translation equivariance.
For more details of Convolutional Neural Network, please refer to [Stanford open course](http://cs231n.github.io/convolutional-networks/) and [Image Classification](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md)chapter.
For more details of Convolutional Neural Network, please refer to [Stanford open course](http://cs231n.github.io/convolutional-networks/) and [Image Classification](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md)tutorial.
In fact, tang function is just a rescaled version of sigmoid function. It is obtained by magnifying the value of sigmoid function and moving it downwards by 1.
In fact, tanh function is just a rescaled version of sigmoid function. It is obtained by magnifying the value of sigmoid function and moving it downwards by 1.
- ReLU activation function： $ f(x) = max(0, x) $
...
...
@@ -138,7 +138,7 @@ Users can randomly generate 10 images with the following script (Refer to Fig. 1
### Provide Data for PaddlePaddle
We use python interface to convey data to system. `mnist_provider.py` shows a complete example for MNIST data.
We use python interface to provide data to system. `mnist_provider.py` shows a complete example for MNIST data.
```python
# Define a py data provider
...
...
@@ -192,7 +192,7 @@ Set training related parameters.
- batch_size: use 128 samples in each training step.
- learning_rate: rating of iteration, related to the rate of convergence.
- learning_method: Optimizer `MomentumOptimizer` for training. The parameter 0.9 indicates momentum keeps 0.9 of previous speed.
- learning_method: use optimizer `MomentumOptimizer` for training. The parameter 0.9 indicates momentum keeps 0.9 of previous speed.
- regularization: A method to prevent overfitting. Here L2 regularization is used.
```python
...
...
@@ -238,7 +238,7 @@ def softmax_regression(img):
#### MultiLayer Perceptron
One simple fully connected layer with softmax activation function outputs classification result.
The following code implements a Multilayer Perceptron with two fully connected hidden layers and ReLU activation function. Output layer has Softmax activation function.
The following is the LeNet-5 network architecture. 2D input image is first fed into two sets of convolutional layer and pooling layer, and it is fed to fully connected layer, and another fully connected layer with softmax activation.
The following is the LeNet-5 network architecture. 2D input image is first fed into two sets of convolutional layer and pooling layer, and it is fed into fully connected layer, and another fully connected layer with softmax activation.
config=mnist_model.py # Select network in mnist_model.py
...
...
@@ -347,13 +347,13 @@ Best pass is 00013, testing Avgcost is 0.484447
The classification accuracy is 90.01%
```
From the evaluation results, the best step for softmax regression model is pass-00013, where classification accuracy is 90.01%, and the last pass-00099 has accuracy of 89.3%. From Fig. 7, we also see that the best accuracy may not appear in the last pass. A explanation is that during training, the model may already arrive at local optimum, and it just swings around nearby in the following passes, or it gets lower local optimum.
From the evaluation results, the best pass for softmax regression model is pass-00013, where classification accuracy is 90.01%, and the last pass-00099 has accuracy of 89.3%. From Fig. 7, we also see that the best accuracy may not appear in the last pass. An explanation is that during training, the model may already arrive at local optimum, and it just swings around nearby in the following passes, or it gets lower local optimum.
@@ -363,7 +363,7 @@ Best pass is 00085, testing Avgcost is 0.164746
The classification accuracy is 94.95%
```
From the evaluation results, the final training accuracy is 94.95%. It has significant improvement comparing with softmax regression model. The reason is that softmax regression is simple, and it cannot fit complex data, but Multilayer perceptron with hidden layers has stronger fitting capacity.
From the evaluation results, the final training accuracy is 94.95%. It has significant improvement comparing with softmax regression model. The reason is that softmax regression is simple, and it cannot fit complex data, but Multilayer Perceptron with hidden layers has stronger fitting capacity.
### Training results for Convolutional Neural Network
...
...
@@ -409,7 +409,7 @@ Actual Number: 0
From the result, this classifier recognizes the digit on the third image as digit 0 with near to 100% probability, and the ground truth is actually consistent.
## Conclusion
Softmax regression, Multilayer perceptron and Convolutional Neural Network in this chapter are the most basic Deep Learning models. More sophisticated models in the following chapters are derived from them. Therefore, these models are very helpful for the future learning. At the same time, we observed that when evolving from the simplest softmax regression to slightly complex Convolutional Neural Network, recognition accuracy on MNIST data set has large improvement, due to Convolutional layers' local connections and parameter sharing. When learning new models in the future, we hope readers to understand the key ideas for a new model to improve over an old one. Moreover, this chapter introduced basic flow of PaddlePaddle model design, starting from dataprovider, model layer construction, to final training and prediction. By becoming familiar with this flow, readers can use specific data and define specific network models, and complete training and prediction for their tasks.
Softmax regression, Multilayer Perceptron and Convolutional Neural Network in this tutorial are the most basic Deep Learning models. More sophisticated models in the following tutorials are derived from them. Therefore, these models are very helpful for the future learning. At the same time, we observed that when evolving from the simplest softmax regression to slightly complex Convolutional Neural Network, recognition accuracy on MNIST data set has large improvement, due to Convolutional layers' local connections and parameter sharing. When learning new models in the future, we hope readers to understand the key ideas for a new model to improve over an old one. Moreover, this tutorial introduced basic flow of PaddlePaddle model design, starting from dataprovider, model layer construction, to final training and prediction. By becoming familiar with this flow, readers can use specific data, define specific network models, and complete training and prediction for their tasks.
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<divid="markdown"style='display:none'>
# Recognize Digits
Source code of this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits) For the first-time use, please refer to PaddlePaddle [installation instructions](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html).
## Introduction
When we learn programming, the first program is usually printing “Hello World.” In Machine Learning, or Deep Learning, this is handwritten digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains handwritten digits and corresponding labels (Fig. 1). An image is a 28x28 matrix, and a label corresponds to one of the 10 digits from 0 to 9. Each image is normalized in size and centered.
MNIST dataset is made from [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and Special Database 1 (SD-1). Since SD-3 is labeled by staffs in U.S. Census Bureau, while SD-1 is labeled by high school students in U.S., SD-3 is cleaner and easier to recognize than SD-1 is. Yann LeCun et al. used half of samples from each of SD-1 and SD-3 for 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 annotators of training set and test set are not completely overlapped.
Yann LeCun, one of the founders of Deep Learning, had huge contribution on handwritten character recognition in early dates, and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for handwritten characters. CNN is now a critical key for Deep Learning. From Yann LeCun’s first proposal of LeNet, to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification) tutorial.) CNN achieved a series of impressive results in Image Classification tasks.
Many algorithms are tested on MNIST. In 1998, LeCun experimented single layer linear classifier, MLP (Multilayer Perceptron) and Multilayer CNN LeNet. These algorithms constantly reduced test error from 12% to 0.7% \[[1](#References)\]. Since then, researchers worked on many algorithms such as k-NN (K-Nearest Neighbors) \[[2](#References)\], Support Vector Machine (SVM) \[[3](#References)\], Neural Networks \[[4-7](#References)\] and Boosting \[[8](#References)\], and applied various preprocessing methods, such as distortion removal, noise removal and blurring, to increase recognition accuracy.
In this tutorial, we start from simple softmax regression model, and guide readers to introduction of handwritten character recognition, and step-by-step improvement of models.
## Model Overview
Before introducing classification algorithms and training procedure, we provide some definitions:
- $X$ is input: Input is $28\times28$ MNIST image. It is flattened to $784$ dimensional vector. $X=\left ( x_0, x_1, \dots, x_{783} \right )$.
- $Y$ is output: Output of classifier is 10 class digits from 0 to 9. $Y=\left ( y_0, y_1, \dots, y_9 \right )$. Each dimension $y_i$ represents a probability that the image belongs to $i$.
- $L$ is ground truth label: $L=\left ( l_0, l_1, \dots, l_9 \right )$ It is also 10 dimensional, but only one dimension is 1 and others are all 0.
### Softmax Regression
The simplest softmax regression model is to feed input into fully connected layers, and directly use softmax for multiclass classification \[[9](#References)\].
Input $X$ is multiplied with weights $W$, added by bias $b$, and activated.
$$ y_i = softmax(\sum_j W_{i,j}x_j + b_i) $$
where $ softmax(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}} $
For a $N$ class classification problem with $N$ output nodes, a $N$ dimensional vector is normalized to $N$ real values in [0, 1], each representing the probability of the sample to belong to the class. Here $y_i$ is the prediction probability that an image is digit $i$.
In classification problem, we usually use cross entropy loss function:
Softmax regression model uses the simplest two layer neural network, i.e. it only contains input layer and output layer, so that it's regression ability is limited. To achieve better recognition effect, we consider adding several hidden layers \[[10](#References)\] between the input layer and the output layer.
1. After the first hidden layer, we get $ H_1 = \phi(W_1X + b_1) $, where $\phi$ is activation function. Some common ones are sigmoid, tanh and ReLU.
2. After the second hidden layer, we get $ H_2 = \phi(W_2H_1 + b_2) $.
3. Finally, after output layer, we get $Y=softmax(W_3H_2 + b_3)$, the last classification result vector.
Fig. 3. is Multilayer Perceptron network, with weights in black, and bias in red. +1 indicates bias is 1.
Convolutional layer is the core of Convolutional Neural Network. The parameters in this layer are composed of a set of filters, or kernels. In forward step, each kernel moves horizontally and vertically, and compute dot product of the kernel and the input on corresponding positions, then add bias and apply activation function. The result is 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 is a dynamic graph of convolutional layer, where depths are not shown for simplicity. Input is $W_1=5,H_1=5,D_1=3$. In fact, this is a common representation for colored images. The width and height of a colored image corresponds to $W_1$ and $H_1$, and the 3 color channels for RGB corresponds to $D_1$. The parameters of convolutional layers are $K=2,F=3,S=2,P=1$. $K$ is the number of kernels. Here, $Filter W_0$ and $Filter W_1$ are two convolution kernels. $F$ is kernel size. $W0$ and $W1$ are both $3\times3$ matrix in all depths. $S$ is stride. Kernels moves leftwards or downwards by 2 units each time. $P$ is padding, the extension for the input.
Pooling layer performs downsampling. The main functionality is to reduce computation by reducing network parameters. It also prevents over-fitting to some extent. Usually, a pooling layer is added after a convolutional layer. Pooling layer includes max pooling, average pooling, etc. Max pooling uses rectangles to divide input layer into several parts, and compute maximum value in each part as output (Fig. 5.)
[LeNet-5](http://yann.lecun.com/exdb/lenet/) is one of the simplest Convolutional Neural Network. Fig. 6. shows its architecture: 2 dimensional image input is fed into two sets of convolutional layer and pooling layer, then it is fed into fully connected layer and softmax classifier. The following three properties of convolution enable LeNet-5 to better recognize images than Multilayer fully-connected perceptrons:
- 3D properties of neurons: a convolutional layer is organized by width, height and depth. Neurons in each layer are connected to only a small region in previous layer. This region is called receptive field.
- Local connection: CNN utilizes local space correlation by connecting local neurons. This design guarantees learned filter has strong response to local input features. Stacking many such layers leads non-linear filter becomes more and more global. This allows the network to first obtain good representation for a small parts of input, then combine them to represent larger region.
- Sharing weights: In CNN, computation is iterated with shared parameters (weights and bias) to form a feature map. This means all neurons in the same depth of output respond to the same feature. This allows detecting a feature regardless of its position in the input, and enables a property of translation equivariance.
For more details of Convolutional Neural Network, please refer to [Stanford open course]( http://cs231n.github.io/convolutional-networks/ ) and [Image Classification](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md) tutorial.
In fact, tanh function is just a rescaled version of sigmoid function. It is obtained by magnifying the value of sigmoid function and moving it downwards by 1.
- ReLU activation function： $ f(x) = max(0, x) $
For more information, please refer to [Activation functions in Wikipedia](https://en.wikipedia.org/wiki/Activation_function).
## Data Preparation
### Data and Download
Execute the following command to download [MNIST](http://yann.lecun.com/exdb/mnist/) dataset and unzip, then put paths of training set and test set to train.list and test.list respectively for PaddlePaddle to read.
```bash
./data/get_mnist_data.sh
```
`gzip` downloaded data. The following files can be found in `data/raw_data`:
In model configuration, define data reading from `dataprovider` by `define_py_data_sources2`. If this configuration is used for prediction, data definition is not necessary.
```python
if not is_predict:
data_dir = './data/'
define_py_data_sources2(
train_list=data_dir + 'train.list',
test_list=data_dir + 'test.list',
module='mnist_provider',
obj='process')
```
### Algorithm Configuration
Set training related parameters.
- batch_size: use 128 samples in each training step.
- learning_rate: rating of iteration, related to the rate of convergence.
- learning_method: use optimizer `MomentumOptimizer` for training. The parameter 0.9 indicates momentum keeps 0.9 of previous speed.
- regularization: A method to prevent overfitting. Here L2 regularization is used.
```python
settings(
batch_size=128,
learning_rate=0.1 / 128.0,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * 128))
```
### Model Architecture
#### Overview
First get data by `data_layer`, and get classification result by classifier. Here we provided three different classifiers. In training, we compute loss function, which is usually cross entropy for classification problem. In prediction, we can directly output results.
The following code implements a Multilayer Perceptron with two fully connected hidden layers and ReLU activation function. Output layer has Softmax activation function.
The following is the LeNet-5 network architecture. 2D input image is first fed into two sets of convolutional layer and pooling layer, and it is fed into fully connected layer, and another fully connected layer with softmax activation.
From the evaluation results, the best pass for softmax regression model is pass-00013, where classification accuracy is 90.01%, and the last pass-00099 has accuracy of 89.3%. From Fig. 7, we also see that the best accuracy may not appear in the last pass. An explanation is that during training, the model may already arrive at local optimum, and it just swings around nearby in the following passes, or it gets lower local optimum.
From the evaluation results, the final training accuracy is 94.95%. It has significant improvement comparing with softmax regression model. The reason is that softmax regression is simple, and it cannot fit complex data, but Multilayer Perceptron with hidden layers has stronger fitting capacity.
### Training results for Convolutional Neural Network
From the evaluation result, the best accuracy of Convolutional Neural Network is 99.20%. This means, for image problem, Convolutional Neural Network has better recognition effect than fully connected network. This should be related to the local connection and parameter sharing of convolutional layers. Also, in Fig. 9, Convolutional Neural Network achieves good effect in early steps, which indicates that it is fast to converge.
## Application Model
### Prediction Commands and Results
Script `predict.py` can make prediction for trained models. For example, in softmax regression:
- -m sets model parameters, here the best trained model is used for prediction
Follow to instruction to input image ID for prediction. The classifier can output probabilities for each digit, predicted results with the highest probability, and ground truth label.
From the result, this classifier recognizes the digit on the third image as digit 0 with near to 100% probability, and the ground truth is actually consistent.
## Conclusion
Softmax regression, Multilayer Perceptron and Convolutional Neural Network in this tutorial are the most basic Deep Learning models. More sophisticated models in the following tutorials are derived from them. Therefore, these models are very helpful for the future learning. At the same time, we observed that when evolving from the simplest softmax regression to slightly complex Convolutional Neural Network, recognition accuracy on MNIST data set has large improvement, due to Convolutional layers' local connections and parameter sharing. When learning new models in the future, we hope readers to understand the key ideas for a new model to improve over an old one. Moreover, this tutorial introduced basic flow of PaddlePaddle model design, starting from dataprovider, model layer construction, to final training and prediction. By becoming familiar with this flow, readers can use specific data, define specific network models, and complete training and prediction for their tasks.
## References
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