6.keras-基於CNN網絡的Mnist數據集分類

keras-基於CNN網絡的Mnist數據集分類

1.數據的載入和預處理

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,Adam
from keras.regularizers import l2
from keras.utils.vis_utils import plot_model
from matplotlib import pyplot as plt

import os

import tensorflow as tf

# 載入數據
(x_train,y_train),(x_test,y_test) = mnist.load_data()

# 預處理
# 將（60000，28，28）轉化爲（-1,28,28,1），最後1是圖片深度

x_train = x_train.reshape(-1,28,28,1)/255.0
x_test= x_test.reshape(-1,28,28,1)/255.0
# 將輸出轉化爲one_hot編碼
y_train = np_utils.to_categorical(y_train,num_classes=10)
y_test = np_utils.to_categorical(y_test,num_classes=10)

2.建立網絡打印訓練數據# 建立網絡

model = Sequential([ 
    # 建立卷積層提取特徵，對卷積核進行正則化 Conv2D(input_shape=(28,28,1),filters=32,kernel_size=5,strides=1,padding='same',activation='relu',kernel_regularizer=l2(0.01)), 
　　 # 池化層，對特徵的篩選
　　 MaxPool2D(pool_size=(2,2),strides=2,padding='same'), Flatten(),
　　
　　 Dense(units=128,input_dim=784,bias_initializer='one',activation='tanh'),

    Dropout(0.2),
    Dense(units=10,bias_initializer='one',activation='softmax')
])

# 編譯 # 自定義優化器
sgd = SGD(lr=0.1) adma = Adam(lr=0.001) # 運用交叉熵
model.compile(optimizer=adma, loss='categorical_crossentropy', # 獲得訓練過程當中的準確率
              metrics=['accuracy']) model.fit(x_train,y_train,batch_size=32,epochs=10,validation_split=0.2) # 評估模型
loss,acc = model.evaluate(x_test,y_test,) print('\ntest loss',loss) print('test acc',acc) 

out:

Epoch 1/10

32/48000 [..............................] - ETA: 10:18 - loss: 2.7563 - acc: 0.1562
96/48000 [..............................] - ETA: 3:53 - loss: 2.6141 - acc: 0.1354

......

......

Epoch 10/10

45952/48000 [===========================>..] - ETA: 0s - loss: 0.0664 - acc: 0.9905
47616/48000 [============================>.] - ETA: 0s - loss: 0.0663 - acc: 0.9908
48000/48000 [==============================] - 2s 37us/step - loss: 0.0663 - acc: 0.9910 - val_loss: 0.0149 - val_acc: 0.9884

 

32/10000 [..............................] - ETA: 4s
3360/10000 [=========>....................] - ETA: 0s
5824/10000 [================>.............] - ETA: 0s
8512/10000 [========================>.....] - ETA: 0s
10000/10000 [==============================] - 0s 20us/step

test loss 0.015059704356454312test acc 0.988