4.keras-交叉熵的介绍和应用

keras-交叉熵的介绍和应用

1.载入数据以及预处理

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import *
from keras.optimizers import SGD

import os

import tensorflow as tf

# 载入数据
(x_train,y_train),(x_test,y_test) = mnist.load_data()

# 预处理
# 将（60000，28，28）转化为（600000，784），好输入展开层
x_train = x_train.reshape(x_train.shape[0],-1)/255.0
x_test= x_test.reshape(x_test.shape[0],-1)/255.0
# 将输出转化为one_hot编码
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

2.创建网络打印训练结果

# 创建网络
model = Sequential([
    # 输入784输出10个
    Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
])
# 编译
# 自定义优化器
sgd = SGD(lr=0.1)
model.compile(optimizer=sgd,
　　　　　　　　# 运用交叉熵
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train,y_train,batch_size=32,epochs=10,validation_split=0.2)

# 评估模型
loss,acc = model.evaluate(x_test,y_test,)
print('
test loss',loss)
print('test acc',acc)

out:

Epoch 1/10

32/48000 [..............................] - ETA: 2:43 - loss: 2.2593 - acc: 0.1562
1792/48000 [>.............................] - ETA: 4s - loss: 1.2642 - acc: 0.6579

......

......

Epoch 10/10

47456/48000 [============================>.] - ETA: 0s - loss: 0.2712 - acc: 0.9241
48000/48000 [==============================] - 2s 41us/step - loss: 0.2716 - acc: 0.9240 - val_loss: 0.2748 - val_acc: 0.9240

32/10000 [..............................] - ETA: 0s
2976/10000 [=======>......................] - ETA: 0s
6656/10000 [==================>...........] - ETA: 0s
10000/10000 [==============================] - 0s 17us/step

test loss 0.2802182431191206
test acc 0.9205