《TensorFlow+keras深度学习人工智能实践应用》代码实践记录

the codes of the book can be downloaded on website: http://www.drmaster.com.tw/download/example/MP21710_example.zip

1. 全连接mnist数字识别

import matplotlib.pyplot as plt
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
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()
def plot_images_labels_prediction(images,labels,prediction,
                                  idx,num=10):
    fig=plt.gcf()
    fig.set_size_inches(12,14)
    if num>25:
        num=25
    for i in range(0,num):
        ax=plt.subplot(5,5,1+i)
        ax.imshow(images[idx],cmap='binary')
        title='label='+str(labels[idx])
        if(len(prediction)>0):
            title+=",prediction="+str(prediction[idx])
        ax.set_title(title,fontsize=10)
        ax.set_xticks([])
        ax.set_yticks([])
        idx+=1
    plt.show()
from keras.datasets import mnist
import numpy as py
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
import pandas as pd
(x_train_image,y_train_label),(x_test_image,y_test_label)=mnist.load_data()
x_Train=x_train_image.reshape(60000,784).astype('float32')
x_Test=x_test_image.reshape(10000,784).astype('float32')
x_Train_normalize=x_Train/255
x_Test_normalize=x_Test/255
y_Train_OneHot=np_utils.to_categorical(y_train_label)
y_Test_OneHot=np_utils.to_categorical(y_test_label)
model=Sequential()
model.add(Dense(units=1000,input_dim=784,kernel_initializer='normal',activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=1000,kernel_initializer='normal',activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=10,kernel_initializer='normal',activation='softmax'))
print(model.summary())
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
train_history=model.fit(x=x_Train_normalize,y=y_Train_OneHot,validation_split=0.2,
                        epochs=10,batch_size=200,verbose=2)
show_train_history(train_history,'acc','val_acc')
show_train_history(train_history,'loss','val_loss')
scores=model.evaluate(x_Test_normalize,y_Test_OneHot)
print()
print('accuracy=',scores[1])
prediction=model.predict_classes(x_Test)
plot_images_labels_prediction(x_test_image,y_test_label,
                              prediction,idx=340)
# pd.crosstab(y_test_label,prediction,rownames=['label'],
#             colnames=['predict'])

2. cnn on mnist

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()
def plot_images_labels_prediction(images,labels,prediction,
                                  idx,num=10):
    fig=plt.gcf()
    fig.set_size_inches(12,14)
    if num>25:
        num=25
    for i in range(0,num):
        ax=plt.subplot(5,5,1+i)
        ax.imshow(images[idx],cmap='binary')
        title='label='+str(labels[idx])
        if(len(prediction)>0):
            title+=",prediction="+str(prediction[idx])
        ax.set_title(title,fontsize=10)
        ax.set_xticks([])
        ax.set_yticks([])
        idx+=1
    plt.show()
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
import numpy as np
import pandas as pd
(x_Train,y_Train),(x_Test,y_Test)=mnist.load_data()
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')
x_Train4D_normalize=x_Train4D/255
x_Test4D_normalize=x_Test4D/255
y_TrainOneHot=np_utils.to_categorical(y_Train)
y_TestOneHot=np_utils.to_categorical(y_Test)
model=Sequential()
model.add(Conv2D(filters=16,kernel_size=(5,5),padding='same',
                 input_shape=(28,28,1),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=36,kernel_size=(5,5),padding='same',
                 activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))
print(model.summary())
model.compile(loss='categorical_crossentropy',
              optimizer='adam',metrics=['accuracy'])
train_history=model.fit(x=x_Train4D_normalize,
                        y=y_TrainOneHot,validation_split=0.2,
                        epochs=10,batch_size=300,verbose=2)
show_train_history(train_history,'acc','val_acc')
show_train_history(train_history,'loss','val_loss')
scores = model.evaluate(x_Test4D_normalize , y_TestOneHot)
print(scores[1])
prediction=model.predict_classes(x_Test4D_normalize)
pd.crosstab(y_Test,prediction,rownames=['label'],
            colnames=['predict'])

3. cnn on cifar10

import matplotlib.pyplot as plt
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
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()
def plot_images_labels_prediction(images,labels,prediction,
                                  idx,num=10):
    fig=plt.gcf()
    fig.set_size_inches(12,14)
    if num>25:
        num=25
    for i in range(0,num):
        ax=plt.subplot(5,5,1+i)
        ax.imshow(images[idx],cmap='binary')
        title='label='+str(labels[idx])
        if(len(prediction)>0):
            title+=",prediction="+str(prediction[idx])
        ax.set_title(title,fontsize=10)
        ax.set_xticks([])
        ax.set_yticks([])
        idx+=1
    plt.show()
from keras.datasets import cifar10
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout,Activation,Flatten
from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D
import numpy as np
(x_img_train,y_label_train),(x_img_test,y_label_test)=cifar10.load_data()
x_img_train_normalize=x_img_train.astype('float32')/255.0
x_img_test_normalize=x_img_test.astype('float32')/255.0
y_label_train_OneHot=np_utils.to_categorical(y_label_train)
y_label_test_OneHot=np_utils.to_categorical(y_label_test)
model=Sequential()
model.add(Conv2D(filters=32,kernel_size=(3,3),
                 input_shape=(32,32,3),
                 activation='relu',
                 padding='same'))
model.add(Dropout(rate=0.25))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=64,kernel_size=(3,3),
                 activation='relu',padding='same'))
model.add(Dropout(rate=0.25))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dropout(rate=0.25))
model.add(Dense(1024,activation='relu'))
model.add(Dropout(rate=0.25))
model.add(Dense(10,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',
              metrics=['accuracy'])
train_history=model.fit(x_img_train_normalize,y_label_train_OneHot,
                        validation_split=0.2,
                        epochs=10,batch_size=128,verbose=1)
show_train_history(train_history,'acc','val_acc')
show_train_history(train_history,'loss','val_loss')
scores=model.evaluate(x_img_train_normalize,y_label_train_OneHot,verbose=0)
print(scores[1])