#绘制步调函数图像importmatplotlib.pyplotaspltx=[1,2,3,4]y=[0,1,2,3]plt.step(x,y)plt.show() importnumpyasnpimportpylabaspltfrommatplotlibimportpylab#设置simoid函数计算流程d...

importnumpyasnp#构建一个含有一个常数12的0维张量x=np.array(12)print(x)#ndim表示张量的维度print(x.ndim)x1=np.array([11,12,13])print(x1)print(x1.ndim)x2=np.array([[11,12,13],[14,15,16]...

importnumpyasnpfrommatplotlibimportcmimportmatplotlib.pyplotaspltfrommpl_toolkits.mplot3dimportAxes3Dfrommatplotlib.tickerimportLinearLocator,FormatStrFormatter...

fromkeras.datasetsimportmnist(train_images,train_labels),(test_images,test_labels)=mnist.load_data()print(train_images.shape)print(train_labels) print(test...

importjava.text.*;publicclassNumberFormatDemo01{publicstaticvoidmain(Stringargs[]){NumberFormatnf=null;//声明一个NumberFormat对象nf=NumberFormat.getInstance();//得到默认的...

importtensorflowastffromtensorflowimportkerasfromtensorflow.kerasimportdatasetsimportos#donotprintirrelevantinformation#os.environ['TF_CPP_MIN_LOG_LEVEL']='2'#x...

importtensorflowastffromtensorflowimportkeras#train:60k|test:10k(x,y),(x_test,y_test)=keras.datasets.mnist.load_data()x.shapey.shape#0纯黑、255纯白x.min(),x.max(),x....

importtensorflowastfa=tf.random.normal([3,3])amask=a>0mask#为True元素，即>0的元素的索引indices=tf.where(mask)indices#取回>0的值tf.gather_nd(a,indices)A=tf.ones([3,3])...

importtensorflowastfa=tf.range(10)a#a中小于2的元素值为2tf.maximum(a,2)#a中大于8的元素值为8tf.minimum(a,8)#a中的元素值限制在[2,8]区间内tf.clip_by_value(a,2,8)a=a-5atf.nn.relu(a)tf.maximum(...

importtensorflowastfa=tf.reshape(tf.range(9),[3,3])atf.pad(a,[[0,0],[0,0]])tf.pad(a,[[1,0,],[0,0]])tf.pad(a,[[1,1],[0,0]])tf.pad(a,[[1,1],[1,0]])tf.pad(a,[[1,1]...

importtensorflowastfa=tf.random.shuffle(tf.range(5))atf.sort(a,direction='DESCENDING')#返回索引tf.argsort(a,direction='DESCENDING')idx=tf.argsort(a,direction='DESCE...

importtensorflowastfa=tf.ones([2,2])atf.norm(a)tf.sqrt(tf.reduce_sum(tf.square(a)))a=tf.ones([4,28,28,3])a.shapetf.norm(a)tf.sqrt(tf.reduce_sum(tf.square(a)))b=...

importtensorflowastf#6个班级的学生分数情况a=tf.ones([4,35,8])b=tf.ones([2,35,8])c=tf.concat([a,b],axis=0)c.shape#3个学生学生补考a=tf.ones([4,32,8])b=tf.ones([4,3,8])tf.concat([a...

手写数字识别流程MNIST手写数字集7000*10张图片60k张图片训练，10k张图片测试每张图片是28*28，如果是彩色图片是28*28*30-255表示图片的灰度值，0表示纯白，255表示纯黑打平28*28的矩阵，得到28*28=784的向量对于b张图片得到[b,784];然后对于b张图片可以给定编码把上述的普通编...

importtensorflowastfb=tf.fill([2,2],2.)a=tf.ones([2,2])a+ba-ba*ba/bb//ab%atf.math.log(a)#只有以e为底的logtf.exp(a)tf.math.log(8.)/tf.math.log(2.)#以2为底tf.math.log(100....