keras第二课--MNIST手写数字识别

Alpha

November 20, 2020

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2645 words

首页 python 编程技术

12.5由于jupyter问题重新安装了anaconda

蛋没关系啊，唉俩小时以后，他就装好了

```python
from keras.datasets import mnist
(train_images,train_labels),(test_images,test_labels) = mnist.load_data()
```

第一步，加载mnist数据集

---

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

第二步，构建网络框架，keras作为最快上手的，基本上一开始直接相信教程中的选择。

该网络层有俩个层，他们是全连接的神经层

relu函数作为一开始介绍到的激活函数

softmax作为一个10个概率值的恒等函数，他的输出就是识别的结果

---

```python
network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])
```

第三步：编译步骤（相信即可）：确定损失函数，优化器等，暂不做具体介绍

---

```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
```

第四步：准备图像数据，对图像数据进行一个预处理

---

```python
from keras.utils import to_categorical
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
```

第五步：准备标签，准备开始拟合！

---

```
network.fit(train_images, train_labels, epochs=5, batch_size=128)
```

进行拟合

---

完整代码：

```python
from keras.datasets import mnist
(train_images,train_labels),(test_images,test_labels) = mnist.load_data()
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu',input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))

network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])

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

from keras.utils import to_categorical
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network.fit(train_images, train_labels, epochs=5, batch_size=128)
```

执行结果（你可能会不一样）

```python
Epoch 1/5
60000/60000 [==============================] - 6s 97us/step - loss: 0.2550 - acc: 0.9263
Epoch 2/5
60000/60000 [==============================] - 7s 116us/step - loss: 0.1043 - acc: 0.9696
Epoch 3/5
60000/60000 [==============================] - 8s 131us/step - loss: 0.0694 - acc: 0.9782
Epoch 4/5
60000/60000 [==============================] - 6s 95us/step - loss: 0.0503 - acc: 0.9853
Epoch 5/5
60000/60000 [==============================] - 6s 97us/step - loss: 0.0374 - acc: 0.9883
```

Last modification：December 5, 2020

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keras第二课--MNIST手写数字识别

Alpha • 2020 年 11 月 20 日

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