from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation
from keras.optimizers import SGD
import keras #abd
 
# Generate dummy data
import numpy as np
 
x_train=np.random.random((1000,20))
y_train=keras.utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
x_test=np.random.random((100,20))
y_test=keras.utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)
 
model=Sequential()
 
# Dense(64) is a fully-connected Layer with 64 hidden units.
# in the first layer ,you must specify the expected input data shape;
# here,20-dimensional vectors.
 
model.add(Dense(64,activation='relu',input_dim=20))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))
 
sgd=SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
 
model.compile(loss='categorical_crossentropy', # 損失函數(shù)
    optimizer=sgd, # 
    metrics=['accuracy'] # 精確度，評(píng)估模型在訓(xùn)練和測(cè)試時(shí)的網(wǎng)絡(luò)性能的指標(biāo)。
    ) 
 
model.fit(x_train,y_train,epochs=20,batch_size=128) 
 
# batch_size 整數(shù)，指定進(jìn)行梯度下降時(shí)每個(gè)批次包含的樣本數(shù)訓(xùn)練時(shí)一個(gè)批次的樣本
# 會(huì)被計(jì)算一次梯度下降，使目標(biāo)函數(shù)進(jìn)行一步優(yōu)化
# epochs；訓(xùn)練20次，整數(shù)，訓(xùn)練終止時(shí)候的epoch值
score=model.evaluate(x_test,y_test,batch_size=128)
 
# 評(píng)估函數(shù) ，本函數(shù)返回一個(gè)測(cè)試誤差的標(biāo)量值（如果模型沒(méi)有其他評(píng)價(jià)指標(biāo)）。

import numpy as np
from keras.models import Sequential
from keras.layers import Dense,Dropout
 
x_train=np.random.random((1000,20))
y_train=keras.utils.to_categorical(np.random.randint(10,size=(1000,1)),num_classes=10)
x_test=np.random.random((100,20))
y_test=keras.utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)
 
model=Sequential()
model.add(Dense(64,input_dim=20,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))
 
model.compile(loss='binary_crossentropy',
    optimizer='rmsprop',
    metrics=['accuracy'])
 
model.fit(x_train,y_train,
   epoches=20,
   batch_size=128)
 
score=model.evaluate(x_test,y_test,batch_size=128)