Functions — github.com/aianaconda/TensorFlow2x_Engineering

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Functions413 in github.com/aianaconda/TensorFlow2x_Engineering_Implementation

⨍ Functions 413◇ Types & classes 46

Method__init__

(self ,intermediate_dim,original_dim,**kwargs)

code/代码8-5 以类的方式封装模型损失函数.py:60

Method__init__

(self, n_classes=80)

code/代码7-9 yolov3.py:28

Method__iter__

(self)

code/代码8-10 mydataset.py:25

Method__len__

(self)

code/代码8-10 mydataset.py:198

Method__next__

(self)

code/代码8-10 mydataset.py:28

Function_grad_fn

(yolo_v3, images_tensor, list_y_trues)

code/代码7-12 mainyolo.py:99

Function_initializer

(shape, dtype, partition_info)

code/代码5-5 序列特征工程.py:39

Method_map_func

(img, label)

code/代码8-10 mydataset.py:168

Function_parseone

Reading and handle image

code/代码4-11 将图片文件制作成Dataset数据集TFa.py:81

Function_parseone

Reading and handle image

code/代码9-2 finetune_resnet.py:51

Function_parseone

(A_fname, B_fname)

code/代码8-8 训练deblur.py:58

Function_parseone

Reading and handle image

code/代码4-10 将图片文件制作成Dataset数据集-TFa.py:81

Function_parseone

Reading and handle image

code/代码7-3 用ResNet识别橘子和苹果.py:86

Function_parseone

(A_fname, B_fname)

code/代码8-9 使用deblur模型.py:41

Function_parseone

Reading and handle image

code/代码4-12 在动态图里读取Dataset数据集.py:98

Function_random_rotated30

(image, label)

code/代码4-11 将图片文件制作成Dataset数据集TFa.py:67

Function_random_rotated30

(image, label)

code/代码4-10 将图片文件制作成Dataset数据集-TFa.py:67

Function_random_rotated30

(image, label)

code/代码7-3 用ResNet识别橘子和苹果.py:61

Function_random_rotated30

(image, label)

code/代码4-12 在动态图里读取Dataset数据集.py:74

Function_rotated

(image)

code/代码7-3 用ResNet识别橘子和苹果.py:63

Function_rotated

(image)

code/代码4-12 在动态图里读取Dataset数据集.py:76

Function_rotatedwrap

()

code/代码4-11 将图片文件制作成Dataset数据集TFa.py:68

Function_rotatedwrap

()

code/代码4-10 将图片文件制作成Dataset数据集-TFa.py:68

Function_rotatedwrap

()

code/代码7-3 用ResNet识别橘子和苹果.py:71

Function_rotatedwrap

()

code/代码4-12 在动态图里读取Dataset数据集.py:84

Method_set

(att, value, att_name)

code/代码8-10 mydataset.py:203

Methodafter_run

(self, run_context, run_values)

code/代码3-5 自定义hook.py.py:30

code/代码8-10 mydataset.py:79

Methodbefore_run

(self, run_context)

code/代码3-5 自定义hook.py.py:27

Methodbegin

(self)

code/代码3-5 自定义hook.py.py:24

Methodbuild

(self, input_shape)

code/代码3-12 keras回归模型2.py:23

Methodbuild

(self, input_shape)

code/代码6-3 keras注意力机制模型.py:47

Methodcall

(self, x)

code/代码8-4 在动态图中实现变分自编码.py:36

Methodcall

(self, z)

code/代码8-4 在动态图中实现变分自编码.py:58

Methodcall

(self, x)

code/代码8-3 将张量损失封装成损失函数.py:37

Methodcall

(self, z)

code/代码8-3 将张量损失封装成损失函数.py:56

Methodcall

(self, input_tensor, training=False)

code/代码7-8 yolohead.py:98

Methodcall

(self, input_tensor, training=False)

code/代码7-8 yolohead.py:127

Methodcall

(self, input_tensor, training=False)

code/代码7-8 yolohead.py:145

Methodcall

(self, x)

code/代码8-6 更合理的类封装方式.py:34

Methodcall

(self, activation)

code/代码8-6 更合理的类封装方式.py:44

Methodcall

(self, z)

code/代码8-6 更合理的类封装方式.py:63

Methodcall

(self, input_features)

code/代码8-6 更合理的类封装方式.py:77

Methodcall

(self, inputs)

code/代码3-12 keras回归模型2.py:33

Methodcall

(self, input_tensor)

code/代码9-4 ZSL_train.py:31

Methodcall

(self, x, y)

code/代码8-14 MINE.py:41

Methodcall

(self, x)

code/代码8-2 变分自编码模型的无标签训练.py:36

Methodcall

(self, z)

code/代码8-2 变分自编码模型的无标签训练.py:55

Methodcall

(self, input_tensor, training=False)

code/代码7-7 darknet53.py:129

Methodcall

(self, input_tensor, training=False)

code/代码7-7 darknet53.py:147

Methodcall

(self, input_tensor, training=False)

code/代码7-7 darknet53.py:171

Methodcall

(self, x)

code/代码8-1 用tf.keras实现变分自编码模型.py:36

Methodcall

(self, z)

code/代码8-1 用tf.keras实现变分自编码模型.py:48

Methodcall

(self, x)

code/代码6-3 keras注意力机制模型.py:18

Methodcall

(self, x)

code/代码6-3 keras注意力机制模型.py:75

Methodcall

(self, inputs, mask=None, training=None)

code/代码6-3 keras注意力机制模型.py:181

Methodcall

(self, x)

code/代码8-15 DIM.py:44

Methodcall

(self, x)

code/代码8-15 DIM.py:91

Methodcall

(self, x)

code/代码8-5 以类的方式封装模型损失函数.py:43

Methodcall

(self, z)

code/代码8-5 以类的方式封装模型损失函数.py:65

Methodcall

(self, x)

code/代码8-5 以类的方式封装模型损失函数.py:78

Methodcompute_mask

(self, inputs, mask=None)

code/代码6-3 keras注意力机制模型.py:137

Methodcompute_output_shape

(self, input_shape)

code/代码3-12 keras回归模型2.py:37

Methodcompute_output_shape

(self, input_shape)

code/代码6-3 keras注意力机制模型.py:33

Methodcompute_output_shape

(self, input_shape)

code/代码6-3 keras注意力机制模型.py:115

Methodcompute_output_shape

(self, input_shape)

code/代码6-3 keras注意力机制模型.py:140

Methoddataset

(self)

code/代码8-10 mydataset.py:71

Methoddropped_mask

()

code/代码6-3 keras注意力机制模型.py:184

Functioneval_input_fn

()

code/代码5-6 用wide_deep模型预测人口收入.py:221

Functioneval_input_fn

()

code/代码5-7 用梯度提升树模型预测人口收入.py:239

Functioneval_input_fn

(data,labels, batch_size)

code/代码3-4 为估算器添加钩子.py:28

Methodfrom_config

(cls, config)

code/代码3-12 keras回归模型2.py:48

Functiongen

()

code/代码6-1 NLP文本预处理.py:37

Functiongen

()

code/代码6-8 迁移训练BERT模型对中文分类.py:54

Methodget_config

(self)

code/代码3-12 keras回归模型2.py:42

Methodget_config

(self)

code/代码6-3 keras注意力机制模型.py:129

Methodget_height

(self, annotation_file)

code/代码7-4 annotation.py:189

Functionget_unique_labels

(files)

code/代码7-4 annotation.py:165

Methodget_variables

(self, layer_idx, suffix=None)

code/代码7-8 yolohead.py:57

Methodget_variables

(self, layer_idx, suffix=None)

code/代码7-7 darknet53.py:102

Methodget_width

(self, annotation_file)

code/代码7-4 annotation.py:183

Functiongetone

(dataset)

code/代码4-12 在动态图里读取Dataset数据集.py:140

Functiongradient_penalty

(f, real, fake=None)

code/代码8-11 AttGANmodels.py:82

Methoditerator

(self)

code/代码8-10 mydataset.py:75

Functionload_image

(image_path)

code/代码9-2 finetune_resnet.py:91

Functionload_image

(image_path)

code/代码9-3 feature_extractor.py:56

Functionmain

(argv=None)

code/代码6-3 用TextCNN模型进行文本分类.py:107

Functionmap_func_

(*args)

code/代码8-10 mydataset.py:106

Functionmy_model

(features, labels, mode, params)

code/代码3-3 用估算器框架训练一个回归模型.py:42

Functionmy_model

(features, labels, mode, params)

code/代码3-8 用估算器框架分布式训练ps.py:57

Functionmy_model

(features, labels, mode, params)

code/代码3-10 用估算器框架进行分布式训练worker.py:57

Functionmy_model

(features, labels, mode, params)

code/代码3-9 用估算器框架进行分布式训练chief.py:56

Functionmy_model

(features, labels, mode, params)

code/代码3-4 为估算器添加钩子.py:42

Functionmy_model

(features, labels, mode, params)

code/代码3-7 用估算器框架进行分布式训练.py:42

Functionmy_py_func

(X, W,b)

code/代码3-2 在静态图中使用动态图.py:26

Functionparse_csv

(value)

code/代码5-6 用wide_deep模型预测人口收入.py:146

Functionparse_csv

(value)

code/代码5-7 用梯度提升树模型预测人口收入.py:162

Functionparse_fn

(x)

code/代码4-6 interleave例子.py:15

Functionperceptual_loss

(y_true, y_pred,image_shape)

code/代码8-8 训练deblur.py:90

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