importtensorflowastfw=tf.constant(1.)x=tf.constant(2.)y=x*wwithtf.GradientTape()astape:tape.watch([w])y2=x*wgrad1=tape.gradient(y,[w])grad1withtf.Gr...

importtensorflowastfy=tf.constant([1,2,3,0,2])y=tf.one_hot(y,depth=4)#max_label=3种y=tf.cast(y,dtype=tf.float32)out=tf.random.normal([5,4])outloss1=tf.reduce_mea...

sigmoid  out'=sigmoid(out)#把输出值压缩在0-1importtensorflowastfa=tf.linspace(-6.,6,10)atf.sigmoid(a)x=tf.random.normal([1,28,28])*5tf.reduce_min(x),tf.reduc...

out=f(X@W+b)out=relut(X@W+b)    importtensorflowastfx=tf.random.normal([4,784])net=tf.keras.layers.Dense(512)out=net(x)out.shapenet.kernel.s...

classNeuralNetWork:def__init__(self):'''初始化网络，设置输入层，中间层，输出层的节点数'''passdeffit(self):'''根据训练数据，不断更新神经网络层之间的链路权重'''passdefevaluate(self):'''输入新数据，网络给出对新数据的判断结果'''p...

#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...

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...