Functions — github.com/aianaconda/TensorFlow2x_Engineering

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Functions413 in github.com/aianaconda/TensorFlow2x_Engineering_Implementation

⨍ Functions 413◇ Types & classes 46

↓ 1 callersFunctionmasked_routing_iter

(caps_uhat, seqLen, iter_num)

code/代码6-4 用带有动态路由算法的RNN模型对新闻进行分类.py:76

↓ 1 callersFunctionmkMask

(input_tensor, maxLen)

code/代码6-4 用带有动态路由算法的RNN模型对新闻进行分类.py:54

↓ 1 callersFunctionmoving_average

(a, w=10)

code/代码3-6 将估算器模型转为静态图模型.py:33

↓ 1 callersFunctionmoving_average

(a, w=10)

code/代码2-2 用动态图训练一个具有保存检查点功能的回归模型.py:91

↓ 1 callersFunctionmoving_average

(a, w=10)

code/代码2-1 用静态图训练一个具有保存检查点功能的回归模型.py:54

↓ 1 callersFunctionmoving_average

(a, w=10)

code/代码3-2 在静态图中使用动态图.py:61

↓ 1 callersFunctionnms_boxes

(boxes, nms_threshold=0.3, obj_threshold=0.3)

code/代码7-6 box.py:63

↓ 1 callersFunctionparse_func

(path, *label)

code/代码8-10 mydataset.py:100

↓ 1 callersFunctionpostprocess_ouput

(yolos, anchors, net_size, image_h, image_w, obj_thresh=0.5, nms_thresh=0.5)

code/代码7-9 yolov3.py:81

↓ 1 callersFunctionpre_train_epoch

(sess, iterator,datatensor)

code/代码8-8 训练deblur.py:119

↓ 1 callersFunctionpremain

(argv)

code/代码5-6 用wide_deep模型预测人口收入.py:245

↓ 1 callersFunctionpremain

(argv)

code/代码5-7 用梯度提升树模型预测人口收入.py:271

↓ 1 callersFunctionread_data

(file_queue)

code/代码4-4 将Excel文件制作成内存对象数据集.py:12

↓ 1 callersFunctionread_item_index_to_entity_id_file

()

code/代码5-8 preprocess.py:18

↓ 1 callersFunctionres_block

(input, filters, kernel_size=(3, 3), strides=(1, 1), use_dropout=False)

code/代码8-7 deblurmodel.py:21

↓ 1 callersMethodreset

(self, feed_dict={})

code/代码8-10 mydataset.py:44

↓ 1 callersFunctionresize_image

(image, boxes, desired_w, desired_h)

code/代码7-4 annotation.py:76

↓ 1 callersFunctionrouting_masked

(in_x, xLen, out_caps_dim, out_caps_num, iter_num=3, dropout=None, is_train=Fa

code/代码6-4 用带有动态路由算法的RNN模型对新闻进行分类.py:112

↓ 1 callersFunctionsampling

(args)

code/代码8-5 以类的方式封装模型损失函数.py:53

↓ 1 callersFunctionsamplingfun

(z_mean, z_log_var)

code/代码8-1 用tf.keras实现变分自编码模型.py:54

↓ 1 callersFunctionsave_all_weights

(d, g, epoch_number, current_loss)

code/代码8-8 训练deblur.py:32

↓ 1 callersFunctionshared_routing_uhat

(caps, #输入 shape(b_sz, maxlen, caps_dim) out_caps_num, #输出胶囊个数

code/代码6-4 用带有动态路由算法的RNN模型对新闻进行分类.py:63

↓ 1 callersFunctionshowbatch

(onebatch_element,datasetname)

code/代码4-8 将内存数据转成DataSet数据集.py:53

↓ 1 callersFunctionshowimg

(index,label,img,ntop)

code/代码9-2 finetune_resnet.py:104

↓ 1 callersFunctionshowimg

(index,label,img,ntop)

code/代码4-3 将图片制作成内存对象数据集.py:77

↓ 1 callersFunctionshowimg

(index,label,img,ntop)

code/代码4-12 在动态图里读取Dataset数据集.py:131

↓ 1 callersFunctionshowone

(one_element,datasetname)

code/代码4-6 interleave例子.py:35

↓ 1 callersFunctionshowresult

(subplot,title,thisimg)

code/代码4-11 将图片文件制作成Dataset数据集TFa.py:108

↓ 1 callersFunctionshowresult

(subplot,title,thisimg)

code/代码9-2 finetune_resnet.py:98

↓ 1 callersFunctionshowresult

(subplot,title,thisimg)

code/代码4-10 将图片文件制作成Dataset数据集-TFa.py:108

↓ 1 callersFunctionshowresult

(subplot,title,thisimg)

code/代码7-3 用ResNet识别橘子和苹果.py:114

↓ 1 callersFunctionshowresult

(subplot,title,thisimg)

code/代码4-3 将图片制作成内存对象数据集.py:70

↓ 1 callersFunctionshowresult

(subplot,title,thisimg)

code/代码4-12 在动态图里读取Dataset数据集.py:125

↓ 1 callersFunctionsinkhorn_loss

Parameters ---------- x : 输入A y : 输入B epsilon :缩放参数 n : A/B中元素个数 niter : 迭代次数 Return: 返回结果和sinkhorn距离

code/代码9-1 Sinkhorn.py:18

↓ 1 callersFunctiontest_categorical_cols_to_embedding

()

code/代码5-3 将离散文本特征列转化为one-hot编码与词向量.py:97

↓ 1 callersFunctiontest_categorical_cols_to_hash_bucket

()

code/代码5-3 将离散文本特征列转化为one-hot编码与词向量.py:12

↓ 1 callersFunctiontest_categorical_cols_to_onehot

()

code/代码5-3 将离散文本特征列转化为one-hot编码与词向量.py:73

↓ 1 callersFunctiontest_column_order

()

code/代码5-1 用feature_column模块处理连续值特征列.py:62

↓ 1 callersFunctiontest_crossed

()

code/代码5-4 根据特征列生成交叉列.py:12

↓ 1 callersFunctiontest_numeric_cols_to_bucketized

()

code/代码5-2 将连续值特征列转化成离散值特征列.py:10

↓ 1 callersFunctiontest_numeric_cols_to_identity

()

code/代码5-2 将连续值特征列转化成离散值特征列.py:27

↓ 1 callersFunctiontest_one_column

()

code/代码5-1 用feature_column模块处理连续值特征列.py:12

↓ 1 callersFunctiontest_order

()

code/代码5-3 将离散文本特征列转化为one-hot编码与词向量.py:119

↓ 1 callersFunctiontest_placeholder_column

()

code/代码5-1 用feature_column模块处理连续值特征列.py:25

↓ 1 callersFunctiontest_reshaping

()

code/代码5-1 用feature_column模块处理连续值特征列.py:47

↓ 1 callersFunctiontest_with_1d_sparse_tensor

()

code/代码5-3 将离散文本特征列转化为one-hot编码与词向量.py:35

↓ 1 callersMethodthiscall

(self, y, M)

code/代码8-15 DIM.py:95

↓ 1 callersFunctiontopk_eval

(sess, model, user_list, train_record, test_record, item_set, k_list)

code/代码5-10 train.py:89

↓ 1 callersFunctiontrain

(args, data, show_loss, show_topk)

code/代码5-10 train.py:13

↓ 1 callersFunctiontrain

()

code/代码6-3 用TextCNN模型进行文本分类.py:18

↓ 1 callersFunctiontrain_input_fn

(train_data, batch_size)

code/代码3-8 用估算器框架分布式训练ps.py:37

↓ 1 callersFunctiontrain_input_fn

(train_data, batch_size)

code/代码3-6 将估算器模型转为静态图模型.py:23

↓ 1 callersFunctiontrain_input_fn

(train_data, batch_size)

code/代码3-10 用估算器框架进行分布式训练worker.py:37

↓ 1 callersFunctiontrain_input_fn

(train_data, batch_size)

code/代码3-9 用估算器框架进行分布式训练chief.py:36

↓ 1 callersFunctiontrain_input_fn

(train_data, batch_size)

code/代码3-4 为估算器添加钩子.py:22

↓ 1 callersFunctiontrain_input_fn

(train_data, batch_size)

code/代码3-7 用估算器框架进行分布式训练.py:22

↓ 1 callersMethodtrain_kge

(self, sess, feed_dict)

code/代码5-9 MKRmodel.py:160

↓ 1 callersMethodtrain_rs

(self, sess, feed_dict)

code/代码5-9 MKRmodel.py:157

↓ 1 callersFunctiontrain_step

(x_batch, y_batch)

code/代码6-3 用TextCNN模型进行文本分类.py:80

↓ 1 callersFunctiontrainmain

(argv)

code/代码5-6 用wide_deep模型预测人口收入.py:205

↓ 1 callersFunctiontrainmain

(argv)

code/代码5-7 用梯度提升树模型预测人口收入.py:222

↓ 1 callersFunctionwh_scale_tensor

(true_box_wh, anchors, image_size)

code/代码7-11 yololoss.py:70

FunctionParsingAtt

(lines)

code/代码9-4 ZSL_train.py:60

FunctionParsingClass

(lines)

code/代码9-4 ZSL_train.py:71

FunctionParsingClass

(lines)

code/代码9-5 ZSL_test.py:13

Method__call__

(self, inputs)

code/代码5-9 MKRmodel.py:23

Method__del__

(self)

code/代码8-10 mydataset.py:21

Method__init__

(self, weight_file)

code/代码7-10 weights.py:12

Method__init__

(self)

code/代码5-6 用wide_deep模型预测人口收入.py:191

Method__init__

(self ,intermediate_dim,original_dim,**kwargs)

code/代码8-4 在动态图中实现变分自编码.py:53

Method__init__

(self, x, y, w, h, c = None, classes = None)

code/代码7-6 box.py:24

Method__init__

(self ,intermediate_dim,original_dim,**kwargs)

code/代码8-3 将张量损失封装成损失函数.py:51

Method__init__

( self, sequence_length, num_classes, vocab_size, embedding_size, filter_sizes, num_filters, l2_re

code/代码6-2 TextCNN模型.py:15

Method__init__

(self, n_classes=80)

code/代码7-8 yolohead.py:16

Method__init__

(self, filters, layer_idx, name="")

code/代码7-8 yolohead.py:118

Method__init__

(self, filters, layer_idx, name="")

code/代码7-8 yolohead.py:136

Method__init__

(self ,intermediate_dim,latent_dim, **kwargs)

code/代码8-6 更合理的类封装方式.py:30

Method__init__

(self ,intermediate_dim,original_dim,**kwargs)

code/代码8-6 更合理的类封装方式.py:58

Method__init__

(self, intermediate_dim, original_dim,latent_dim)

code/代码8-6 更合理的类封装方式.py:71

Method__init__

(self, output_dim, **kwargs)

code/代码3-12 keras回归模型2.py:18

Method__init__

(self, w, h, jitter)

code/代码7-4 annotation.py:16

Method__init__

(self)

code/代码7-4 annotation.py:176

Method__init__

(self, filename)

code/代码7-4 annotation.py:229

Method__init__

(self, dim, name=None)

code/代码5-9 MKRmodel.py:13

Method__init__

(self)

code/代码9-4 ZSL_train.py:21

Method__init__

(self)

code/代码8-10 mydataset.py:13

Method__init__

(self, data_dir, atts, img_resize, batch_size, shuffle=True, repeat=-1, sess=None, mode='tr

code/代码8-10 mydataset.py:138

Method__init__

(self)

code/代码5-7 用梯度提升树模型预测人口收入.py:208

Method__init__

(self)

code/代码8-14 MINE.py:35

Method__init__

(self, loss)

code/代码3-5 自定义hook.py.py:21

Method__init__

(self ,intermediate_dim,original_dim,**kwargs)

code/代码8-2 变分自编码模型的无标签训练.py:50

Method__init__

(self, ann_fnames, img_dir,labels, batch_size, anchors, net_size=416, jitt

code/代码7-5 generator.py:20

Method__init__

(self, filters, layer_idx, name="")

code/代码7-7 darknet53.py:121

Method__init__

(self, filters, layer_idx, name="")

code/代码7-7 darknet53.py:138

Method__init__

(self, filters, layer_idx, name="")

code/代码7-7 darknet53.py:157

Method__init__

(self ,intermediate_dim,original_dim,**kwargs)

code/代码8-1 用tf.keras实现变分自编码模型.py:43

Method__init__

(self, size=None, mode='sum', **kwargs)

code/代码6-3 keras注意力机制模型.py:13

Method__init__

(self,drop_rate,target_rate, **kwargs)

code/代码6-3 keras注意力机制模型.py:122

Method__init__

(self, alpha=0.5, beta=1.0, gamma=0.1, **kwargs)

code/代码8-15 DIM.py:58

Method__init__

(self ,intermediate_dim,latent_dim, **kwargs)

code/代码8-5 以类的方式封装模型损失函数.py:37

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