這是我的源代碼
#使用dropout進一步改進神經網路
from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense ,Activation
from keras.optimizers import SGD
from keras.utils import np_utils
np.random.seed(1671) #重復性設定
#網路和訓練
NB_EPOCH =250
BATCH_SIZE=128
VERBOSE=1
NB_CLASSES=10
OPTIMIZER=SGD()
N_HIDDEN=128
VALIDATION_SPLIT=0.2
DROPOUT=0.3
#資料：混合并劃分訓練集和測驗集
(X_train,y_train),(X_test,y_test)=mnist.load_data()
#X_train是60000行的28*28的資料，可以變形為60000*784
RESHAPED=784
#
X_train=X_train.reshape(60000,RESHAPED)
X_test=X_test.reshape(10000,RESHAPED)
X_train=X_train.astype('float32')
X_test=X_test.astype('float32')
#歸一化
X_train/=255
X_test/=255
#將類向量轉換成二值類別矩陣
Y_train=np_utils.to_categorical(y_train,NB_CLASSES)
Y_test=np_utils.to_categorical(y_test,NB_CLASSES)
#N_HIDDEN個隱藏層，10個輸出
model=Sequential()
model.add(Dense(N_HIDDEN,input_shape=(RESHAPED,)))
model.add(Activation('relu'))
model.add(Dropout(DROPOUT))
model.add(Dense(N_HIDDEN))
model.add(Activation('relu'))
model.add(Dropout(DROPOUT))
model.add(Dense(NB_CLASSES))
model.add(Activation('softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=OPTIMIZER,
metrics=['accuracy'])
history=model.fit(X_train,Y_train,
batch_size=BATCH_SIZE,epochs=NB_EPOCH,
verbose=VERBOSE,validation_split=VALIDATION_SPLIT)
score=model.evaluate(X_test,Y_test,verbose=VERBOSE)
print("Test score:",score[0])
print('Test accuracy:',score[1])
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