使用Keras画神经网络准确性图教程

history=model.fit(x_train, y_train, batch_size=32, epochs=5, validation_split=0.25) #获取数据
 
#########画图
acc = history.history['acc']  #获取训练集准确性数据
val_acc = history.history['val_acc'] #获取验证集准确性数据
loss = history.history['loss']   #获取训练集错误值数据
val_loss = history.history['val_loss'] #获取验证集错误值数据
epochs = range(1,len(acc)+1)
plt.plot(epochs,acc,'bo',label='Trainning acc')  #以epochs为横坐标，以训练集准确性为纵坐标
plt.plot(epochs,val_acc,'b',label='Vaildation acc') #以epochs为横坐标，以验证集准确性为纵坐标
plt.legend() #绘制图例，即标明图中的线段代表何种含义
 
plt.figure() #创建一个新的图表
plt.plot(epochs,loss,'bo',label='Trainning loss')
plt.plot(epochs,val_loss,'b',label='Vaildation loss')
plt.legend() ##绘制图例，即标明图中的线段代表何种含义
 
plt.show() #显示所有图表

import keras
from keras.datasets import mnist
from keras.layers import Conv2D, MaxPool2D, Dense, Flatten,Dropout
from keras.models import Sequential
import matplotlib.pyplot as plt
 
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1)
x_test = x_test.reshape(-1, 28, 28, 1)
x_train = x_train / 255.
x_test = x_test / 255.
 
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)
 
model = Sequential()
model.add(Conv2D(20,(5,5),strides=(1,1),input_shape=(28,28,1),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Conv2D(64,(5,5),strides=(1,1),padding='valid',activation='relu',kernel_initializer='uniform'))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Flatten())
model.add(Dense(500,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10,activation='softmax'))
model.compile('sgd', loss='categorical_crossentropy', metrics=['accuracy']) #随机梯度下降
 
history=model.fit(x_train, y_train, batch_size=32, epochs=5, validation_split=0.25) #获取数据
 
#########画图
acc = history.history['acc']  #获取训练集准确性数据
val_acc = history.history['val_acc'] #获取验证集准确性数据
loss = history.history['loss']   #获取训练集错误值数据
val_loss = history.history['val_loss'] #获取验证集错误值数据
epochs = range(1,len(acc)+1)
plt.plot(epochs,acc,'bo',label='Trainning acc')  #以epochs为横坐标，以训练集准确性为纵坐标
plt.plot(epochs,val_acc,'b',label='Vaildation acc') #以epochs为横坐标，以验证集准确性为纵坐标
plt.legend() #绘制图例，即标明图中的线段代表何种含义
 
plt.figure() #创建一个新的图表
plt.plot(epochs,loss,'bo',label='Trainning loss')
plt.plot(epochs,val_loss,'b',label='Vaildation loss')
plt.legend() ##绘制图例，即标明图中的线段代表何种含义