MNIST-完整记录

Author： Alpha
发布时间：August 11, 2021
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## 声明

因为mnist的数据集是公开的，为了避免把keggle 的测试集搞到训练里面去

我们使用kaggle和keras提供的训练集训练，用他们的测试数据集测试！

如果你想在kaggle MNIST中达到1.00

这很简单，去网上把mnist的所有数据集下载，带到模型里面去训练，达到过拟合后，去预测kaggle的训练数据，就可以达到1.00了！

但是这对学习来说没有多少意义.....

## 数据集准备

1.keras中自带，内置的60000个训练数据和10000个测试数据

2.kaggle - MNIST [https://www.kaggle.com/c/digit-recognizer/data](https://www.kaggle.com/c/digit-recognizer/data "https://www.kaggle.com/c/digit-recognizer/data") 42000个训练数据和28000个测试数据

注：kaggle中的测试数据没有标签

## 模型的准备

在这里分第一期和第二期来准备

### 密集全连接层

![密集全连接层](http://blog.a152.top/usr/uploads/2021/08/1106269163.png "密集全连接层")

### 带有卷积池化的连接层-CNN

![请输入图片描述](http://blog.a152.top/usr/uploads/2021/08/603253698.png)

## 用代码实现模型

<div class="tab-container post_tab box-shadow-wrap-lg">
<ul class="nav no-padder b-b scroll-hide" role="tablist">
<li class='nav-item active' role="presentation"><a class='nav-link active' style="" data-toggle="tab" 
aria-controls='tabs-f5b1f702697dfe80fc7e470cde97cc53430' role="tab" data-target='#tabs-f5b1f702697dfe80fc7e470cde97cc53430'>全连接层</a></li><li class='nav-item ' role="presentation"><a class='nav-link ' style="" data-toggle="tab" 
aria-controls='tabs-6851594a020e91a7545658a6132d2af0141' role="tab" data-target='#tabs-6851594a020e91a7545658a6132d2af0141'>CNN</a></li>
</ul>
<div class="tab-content no-border">
<div role="tabpanel" id='tabs-f5b1f702697dfe80fc7e470cde97cc53430' class="tab-pane fade active in">
            
下面的模型经过进一步修改，图片可能与它不符

```python
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(2048, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dropout(0.1))
network.add(layers.Dense(512, activation='relu'))
network.add(layers.Dropout(0.1))
network.add(layers.Dense(512, activation='relu'))
network.add(layers.Dropout(0.1))
network.add(layers.Dense(512, activation='relu'))
network.add(layers.Dense(10, activation='softmax'))
#network.summary()
```

</div><div role="tabpanel" id='tabs-6851594a020e91a7545658a6132d2af0141' class="tab-pane fade  ">
            还在写作中...</div>
</div>
</div>

## 数据预处理

<div class="panel panel-default collapse-panel box-shadow-wrap-lg"><div class="panel-heading panel-collapse" data-toggle="collapse" data-target="#collapse-d9bf65ad217dcacc707b25eadea620d252" aria-expanded="true"><div class="accordion-toggle"><span style="">keras内置的数据集</span>
<i class="pull-right fontello icon-fw fontello-angle-right"></i>
</div>
</div>
<div class="panel-body collapse-panel-body">
<div id="collapse-d9bf65ad217dcacc707b25eadea620d252" class="collapse collapse-content"><p></p>

### 加载数据集

```python
from keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
```

### 处理训练数据和测试数据图像

```python
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255
print(train_images.shape)
```

经过上面处理，我们已经处理成模型input一样的形状了
接下来是处理训练标签和测试标签

```python
from keras.utils import to_categorical

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
```

<div class="panel panel-default collapse-panel box-shadow-wrap-lg"><div class="panel-heading panel-collapse" data-toggle="collapse" data-target="#collapse-a88174858c4d272ba09897f1cf6511dc70" aria-expanded="true"><div class="accordion-toggle"><span style="">kaggle下载的数据集</span>
<i class="pull-right fontello icon-fw fontello-angle-right"></i>
</div>
</div>
<div class="panel-body collapse-panel-body">
<div id="collapse-a88174858c4d272ba09897f1cf6511dc70" class="collapse collapse-content"><p></p>

### 读取数据集 处理训练图像

```python
df = pd.read_csv('train.csv')
data = df.drop(['label'],axis=1)
train_images = data.values.reshape(42000,28*28)
train_images = train_images.astype('float32') / 255
train_images.shape
```

### 处理训练标签

```python
label = df['label']
from keras.utils import to_categorical
train_labels = label
train_labels = to_categorical(label)
train_labels.shape
```

### 合并两个数据集

```python
train_data = np.concatenate([train_images,ktrain_images],axis=0)
train_label = np.concatenate([train_labels,ktrain_labels])
print(train_data.shape)
print(train_label.shape)
```

</div><div role="tabpanel" id='tabs-d323c61c36e5464aa2e178d42ffa5ea0351' class="tab-pane fade  ">
            内容 2</div>
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</div>

## 开始训练！

```python
network.compile(optimizer='sgd',
                loss='categorical_crossentropy',
                metrics=['accuracy'])
network.fit(train_data, train_label, epochs=50, batch_size=5000)
```

在50个epoch后，将会在这些数据集达到过拟合

学习方法可以用多个，比如rmsprop

学习完成后，记得保存模型

```python
network.save('mnistdnn814.h5')
```

用keras的测试集进行检测

```python
test_loss, test_acc = network.evaluate(ktest_images, ktest_labels)
print('test_acc:', test_acc)
```

预测kaggle的测试数据集

```python
test_data = pd.read_csv('test.csv')
test_data = test_data.values.reshape(28000,28*28)
test_data = test_data / 255
result = model.predict(test_data)
result = np.argmax(result, axis=1)
r = {"ImageId": [i+1 for i in range(28000)],
                   "Label": result}
r = pd.DataFrame(r)
r.to_csv('result.csv',index=False)
print("成功了！结果保存在result.csv!")
```

</div><div role="tabpanel" id='tabs-0a5d3eb05c130a754c75c5c5f1fec17721' class="tab-pane fade  ">
            内容 2</div>
</div>
</div>

<div class="panel panel-default  box-shadow-wrap-lg goal-panel">
    <div class="panel-heading">
        完成进度
    </div>
    <div class="list-group">
   <div class="list-group-item">            <p class="goal_name"> 还在进行中 ：</p>
            <div class="progress-striped active m-b-sm progress" value="dynamic" type="danger">
                <div class="progress-bar progress-bar-warning" role="progressbar" aria-valuenow="97" aria-valuemin="0"
                 aria-valuemax="100" style="width: 50%;"><span> 50% </span></div>
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Last modification：August 26, 2021

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MNIST-完整记录

Alpha • 2021 年 08 月 11 日

## 声明

因为mnist的数据集是公开的，为了避免把keggle 的测试集搞到训练里面去

我们使用kaggle和keras提供的训练集训练，用他们的测试数据集测试！

如果你想在kaggle MNIST中达到1.00

这很简单，去网上把mnist的所有数据集下载，带到模型里面去训练，达到过拟合后，去预测kaggle的训练数据，就可以达到1.00了！

但是这对学习来说没有多少意义.....

## 数据集准备

1.keras中自带，内置的60000个训练数据和10000个测试数据

注：kaggle中的测试数据没有标签

## 模型的准备

在这里分第一期和第二期来准备

### 密集全连接层

![密集全连接层](http://blog.a152.top/usr/uploads/2021/08/1106269163.png "密集全连接层")

### 带有卷积池化的连接层-CNN

![请输入图片描述](http://blog.a152.top/usr/uploads/2021/08/603253698.png)

## 用代码实现模型

</div><div role="tabpanel" id='tabs-6851594a020e91a7545658a6132d2af0141' class="tab-pane fade  ">
            还在写作中...</div>
</div>
</div>

## 数据预处理

### 加载数据集

```python
from keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
```

### 处理训练数据和测试数据图像

```python
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype('float32') / 255

test_images = test_images.reshape((10000, 28 * 28))
test_images = test_images.astype('float32') / 255
print(train_images.shape)
```

经过上面处理，我们已经处理成模型input一样的形状了
接下来是处理训练标签和测试标签

```python
from keras.utils import to_categorical

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
```

### 读取数据集 处理训练图像

```python
df = pd.read_csv('train.csv')
data = df.drop(['label'],axis=1)
train_images = data.values.reshape(42000,28*28)
train_images = train_images.astype('float32') / 255
train_images.shape
```

### 处理训练标签

```python
label = df['label']
from keras.utils import to_categorical
train_labels = label
train_labels = to_categorical(label)
train_labels.shape
```

### 合并两个数据集

```python
train_data = np.concatenate([train_images,ktrain_images],axis=0)
train_label = np.concatenate([train_labels,ktrain_labels])
print(train_data.shape)
print(train_label.shape)
```

</div><div role="tabpanel" id='tabs-d323c61c36e5464aa2e178d42ffa5ea0351' class="tab-pane fade  ">
            内容 2</div>
</div>
</div>

## 开始训练！

```python
network.compile(optimizer='sgd',
                loss='categorical_crossentropy',
                metrics=['accuracy'])
network.fit(train_data, train_label, epochs=50, batch_size=5000)
```

在50个epoch后，将会在这些数据集达到过拟合

学习方法可以用多个，比如rmsprop

学习完成后，记得保存模型

```python
network.save('mnistdnn814.h5')
```

用keras的测试集进行检测

```python
test_loss, test_acc = network.evaluate(ktest_images, ktest_labels)
print('test_acc:', test_acc)
```

预测kaggle的测试数据集

</div><div role="tabpanel" id='tabs-0a5d3eb05c130a754c75c5c5f1fec17721' class="tab-pane fade  ">
            内容 2</div>
</div>
</div>

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MNIST-完整记录

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