keras框架的CNN手写数字识别MNIST

参考：林大贵.TensorFlow+Keras深度学习人工智能实践应用[M].北京：清华大学出版社,2018.

首先在命令行中写入 activate tensorflow和jupyter notebook，运行如下代码。当然，事先准备好MNIST数据集。

# coding: utf-8

# In[4]:


from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
np.random.seed(10)


# In[5]:


(x_train,y_train),(x_test,y_test)=mnist.load_data()


# In[6]:


x_train4d = x_train.reshape(x_train.shape[0],28,28,1).astype('float32')
x_test4d = x_test.reshape(x_test.shape[0],28,28,1).astype('float32')


# In[7]:


x_train4d_normalize = x_train4d/255
x_test4d_normalize = x_test4d/255


# In[8]:


y_train_oneHot = np_utils.to_categorical(y_train)
y_test_oneHot = np_utils.to_categorical(y_test)


# In[9]:


from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D


# In[10]:


model = Sequential()


# In[11]:


model.add(Conv2D(filters = 16,
                 kernel_size = (5,5),
                 padding = 'same',
                 input_shape = (28,28,1),
                 activation = ('relu')
                ))


# In[12]:


model.add(MaxPooling2D(pool_size=(2,2)))


# In[13]:


model.add(Conv2D(filters = 36,
                kernel_size = (5,5),
                padding = 'same',
                activation = 'relu'))


# In[14]:


model.add(MaxPooling2D(pool_size=(2,2)))


# In[15]:


model.add(Dropout(0,255))


# In[16]:


model.add(Flatten())


# In[17]:


model.add(Dense(128,activation = 'relu'))


# In[18]:


model.add(Dropout(0.5))


# In[19]:


model.add(Dense(10,activation = 'sigmoid'))


# In[20]:


print(model.summary())


# In[21]:


model.compile(loss='categorical_crossentropy',
             optimizer = 'adam',
             metrics = ['accuracy'])


# In[22]:


train_history = model.fit(x = x_train4d_normalize,
                         y = y_train_oneHot,
                         validation_split = 0.2,
                         epochs = 10,
                         batch_size = 300,
                         verbose = 2)


# In[23]:


import matplotlib.pyplot as plt
def show_train_history(train_history,train,validation):
    plt.plot(train_history.history[train])
    plt.plot(train_history.history[validation])
    plt.title('Train_History')
    plt.ylabel(train)
    plt.xlabel('Epoch')
    plt.legend(['train','validation'], loc = 'upper left')
    plt.show()


# In[24]:


show_train_history(train_history,'acc','val_acc')


# In[25]:


show_train_history(train_history,'loss','val_loss')


# In[26]:


scores = model.evaluate(x_test4d_normalize,y_test_oneHot)
scores[1]


# In[27]:


def plot_image_labels_prediction(images,labels,prediction,idx,num=10):
    fig = plt.gcf()
    fig.set_size_inches(12,24)
    if num>50 : num = 50
    for i in range(0,num):
        ax = plt.subplot(10,5,1+i)
        ax.imshow(images[idx],cmap='binary')
        title = "lable="+str(labels[idx])
        if len(prediction)>0:
            title+=",predict="+str(prediction[idx])
        ax.set_title(title,fontsize=10)
        ax.set_xticks([]);ax.set_yticks([])
        idx+=1
    plt.show()                                  


# In[28]:


prediction = model.predict_classes(x_test4d_normalize)


# In[29]:


plot_image_labels_prediction(x_test,
                             y_test,
                             prediction,
                             0,
                            50)


# In[30]:


import pandas as pd


# In[31]:


pd.crosstab(y_test,
            prediction,
           rownames=['label'],
           colnames=['predict'])


# In[ ]:

卷积神经网络简介：https://www.cnblogs.com/bai2018/p/10413889.html

 产生如下神经网络：

其中加入的dropout层用于避免过度拟合。在每次迭代时，随机舍弃一部分的训练样本。

训练效果较好。

林大贵.TensorFlow+Keras深度学习人工智能实践应用[M].北京：清华大学出版社,2018.