from keras.layers.normalization import BatchNormalization

from keras.models import Sequential

from keras.layers.core import Dense,Dropout,Activation

from keras.optimizers import SGD,Adam

import numpy as np

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"]=''

def fizzbuzz(start,end):

    x_train,y_train=[],[]

    for i in range(start,end+1):

        num = i

        tmp=[0]*10

        j=0

        while num :

            tmp[j] = num & 1#这位是1吗

            num = num>>1#右移一位

            j+=1

        x_train.append(tmp)

        if i % 3 == 0 and i % 5 ==0:

            y_train.append([0,0,0,1])

        elif i % 3 == 0:

            y_train.append([0,1,0,0])

        elif i % 5 == 0:

            y_train.append([0,0,1,0])

        else :

            y_train.append([1,0,0,0])

    return np.array(x_train),np.array(y_train)

x_train,y_train = fizzbuzz(101,1000) #打标记函数

x_test,y_test = fizzbuzz(1,100)

model = Sequential()

model.add(Dense(input_dim=10,output_dim=100))#100个neuron(hidden layer)

model.add(Activation('relu'))

model.add(Dense(output_dim=4))#4种情况

model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

model.fit(x_train,y_train,batch_size=20,nb_epoch=100)

result = model.evaluate(x_test,y_test,batch_size=1000)

print('Acc：',result[1])