reluLayermaxPooling2dLayer(2,'Stride',2)convolution2dLayer(3,32,'Padding','same')batchNormalizationLayer reluLayer flattenLayer% 扁平化层fullyConnectedLayer(10)% 全连接层softmaxLayer% Softmax层classificationLayer];% 分类层 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16...
[xTrain,yTrain,xTest,yTest]=digitTrain4DArrayData; 1. 3. 构建深度学习模型 使用layerGraph函数构建一个简单的卷积神经网络(CNN)模型。可以通过如下代码实现: layers=[imageInputLayer([28281])convolution2dLayer(3,8,'Padding','same')batchNormalizationLayer reluLayermaxPooling2dLayer(2,'Stride',2)flatt...
<:( 由于array类型是NumPy中的默认类型,因此即使你将矩阵作为参数传入,某些函数也可能返回一个 array类型。这种情况不应该发生在NumPy函数中(如果发生了,那就是一个bug),但是基于NumPy的第三方代码可能不像NumPy那样遵守类型保存。:) A*B是矩阵乘法,因此线性代数更方便(对于Python >= 3.5 的版本,普通数组与@运算...
y = x.flatten(1) turn array into vector (note that this forces a copy) 1:10 arange(1.,11.)or r_[1.:11.]or r_[1:10:10j] mat(arange(1.,11.))or r_[1.:11.,'r'] create an increasing vectorsee note 'RANGES' 0:9 arange(10.)or r_[:10.]or r_[:9:10j] mat(arange(...
dz_dy = np.gradient(zi) # Calculate the normal vectors nx = -dz_dx / np.sqrt(dz_dx**2 + dz_dy**2 + 1) ny = -dz_dy / np.sqrt(dz_dx**2 + dz_dy**2 + 1) nz = np.ones_like(nx) normals = np.column_stack((nx.flatten(), ny.flatten(), nz.flatten())) return ...
% 定义 x 变量 , % 从 0 开始 , 每次递增 0.1 , 到 2 * pi 结束 % 坐标系中 x 点的个...
y = x.flatten(1) turn array into vector (note that this forces a copy) 1:10 arange(1.,11.) or r_[1.:11.] or r_[1:10:10j] mat(arange(1.,11.)) or r_[1.:11.,'r'] create an increasing vector see note 'RANGES' 0:9 arange(10.) or r_[:10.] or r_[:...
y=x(: ) y = x.flatten() 将数组转换为向量(请注意,这会强制复制) 1:10 arange(1.,11.)或r_[1.:11.]或 r_[1:10:10j] 创建一个增加的向量,步长为默认值1(参见备注) 0:9 arange(10.)或 r_[:10.]或 r_[:9:10j] 创建一个增加的向量,步长为默认值1(参见注释范围) [1:10]' arange(...
flatten_conn_matrices get_wh_subjects ISC qcfc ttest CanlabCore/@canlab_dataset add_vars bars canlab_dataset concatenate get_descriptives get_var glm glm_multilevel histogram list_variables mediation plot_var print_summary read_from_excel reliability replace_values scatte...
convolution2dLayer([3 3],8,"Name","conv_2") batchNormalizationLayer("Name","batchnorm_2") reluLayer("Name","relu_2") flattenLayer("Name","flatten") fullyConnectedLayer(100,"Name","fc") lstmLayer(100,"Name","lstm","OutputMode","last")]; net = addLayers(net,tempNet); te...