importosbase_dir='/Users/apple/Documents/cat-and-dog'train_cats_dir=os.path.join(base_dir,'training_set/cats')train_dogs_dir=os.path.join(base_dir,'training_set...

fromkerasimportlayersfromkerasimportmodelsmodel=models.Sequential()#首层接收2维输入model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)))model.add(...

importpandasaspddata_path='/Users/chenyi/Documents/housing.csv'housing=pd.read_csv(data_path)housing.info()housing.head()housing.describe()housing.hist(bins=50,...

importpandasaspddf=pd.read_json('/Users/chenyi/Documents/News_Category_Dataset.json',lines=True)df.head()categories=df.groupby('category')print("totalcategories...

fromkeras.datasetsimportimdb#num_words表示加载影评时，确保影评里面的单词使用频率保持在前1万位，于是有些很少见的生僻词在数据加载时会舍弃掉(train_data,train_labels),(test_data,test_labels)=imdb.load_data(num_wor...

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...

importnumpyasnpimporttensorflowastfimportmatplotlib.pyplotasplt#定义RNN的参数。HIDDEN_SIZE=30#LSTM中隐藏节点的个数。NUM_LAYERS=2#LSTM的层数。TIMESTEPS=10#循环神经网络的训练序列长度。TRAINING_ST...

importnumpyasnp#1.定义RNN的参数。X=[1,2]state=[0.0,0.0]w_cell_state=np.asarray([[0.1,0.2],[0.3,0.4]])w_cell_input=np.asarray([0.5,0.6])b_cell=np.asarray([0.1,-0.1])w_...

              importtensorflowastfINPUT_NODE=784OUTPUT_NODE=10IMAGE_SIZE=28NUM_CHANNELS=1N...

                          &nbs...

                          &nbs...