Functions — github.com/aianaconda/TensorFlow2x_Engineering

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Functions413 in github.com/aianaconda/TensorFlow2x_Engineering_Implementation

⨍ Functions 413◇ Types & classes 46

↓ 1 callersMethod__init__

(self ,intermediate_dim,latent_dim, **kwargs)

code/代码8-4 在动态图中实现变分自编码.py:30

↓ 1 callersMethod__init__

(self ,intermediate_dim,latent_dim, **kwargs)

code/代码8-3 将张量损失封装成损失函数.py:31

↓ 1 callersMethod__init__

(self, args, n_users, n_items, n_entities, n_relations)

code/代码5-9 MKRmodel.py:42

↓ 1 callersMethod__init__

(self ,intermediate_dim,latent_dim, **kwargs)

code/代码8-2 变分自编码模型的无标签训练.py:30

↓ 1 callersMethod__init__

(self ,intermediate_dim,latent_dim, **kwargs)

code/代码8-1 用tf.keras实现变分自编码模型.py:30

↓ 1 callersMethod__init__

(self, **kwargs)

code/代码8-15 DIM.py:32

↓ 1 callersFunction_activate_probs

(objectness, classes, obj_thresh=0.3)

code/代码7-9 yolov3.py:134

↓ 1 callersFunction_assign_box

(yolo, box_index, box, label)

code/代码7-5 generator.py:115

↓ 1 callersMethod_build_high_layers

(self, args)

code/代码5-9 MKRmodel.py:99

↓ 1 callersMethod_build_inputs

(self)

code/代码5-9 MKRmodel.py:60

↓ 1 callersMethod_build_loss

(self, args)

code/代码5-9 MKRmodel.py:137

↓ 1 callersMethod_build_low_layers

(self, args)

code/代码5-9 MKRmodel.py:69

↓ 1 callersMethod_build_train

(self, args)

code/代码5-9 MKRmodel.py:153

↓ 1 callersMethod_bulid

(self, dataset, sess=None)

code/代码8-10 mydataset.py:51

↓ 1 callersMethod_compute_target_mask

(self, inputs, mask=None)

code/代码6-3 keras注意力机制模型.py:143

↓ 1 callersFunction_create_augment_pipeline

()

code/代码7-4 annotation.py:102

↓ 1 callersFunction_create_empty_xy

(net_size, n_classes, n_boxes=3)

code/代码7-5 generator.py:76

↓ 1 callersFunction_create_mesh_anchor

(anchors, batch_size, grid_h, grid_w, n_box)

code/代码7-11 yololoss.py:27

↓ 1 callersFunction_create_mesh_xy

(batch_size, grid_h, grid_w, n_box)

code/代码7-11 yololoss.py:10

↓ 1 callersFunction_decode_coords

(netout, row, col, b, anchors)

code/代码7-9 yolov3.py:123

↓ 1 callersFunction_distorted_image

(image,size,ch=1,shuffleflag = False,cropflag = False, brightnessflag=False,contrastflag

code/代码4-11 将图片文件制作成Dataset数据集TFa.py:42

↓ 1 callersFunction_distorted_image

(image,size,ch=1,shuffleflag = False,cropflag = False, brightnessflag=False,contrastflag

code/代码4-10 将图片文件制作成Dataset数据集-TFa.py:42

↓ 1 callersFunction_distorted_image

(image,size,ch=1,shuffleflag = False,cropflag = False, brightnessflag=False,contrastflag

code/代码7-3 用ResNet识别橘子和苹果.py:43

↓ 1 callersFunction_distorted_image

(image,size,ch=1,shuffleflag = False,cropflag = False, brightnessflag=False,contrastflag

code/代码4-12 在动态图里读取Dataset数据集.py:49

↓ 1 callersFunction_encode_box

(yolo, original_box, anchor_box, net_w, net_h)

code/代码7-5 generator.py:86

↓ 1 callersFunction_find_match_anchor

(box, anchor_boxes)

code/代码7-5 generator.py:105

↓ 1 callersMethod_get

(self)

code/代码7-5 generator.py:51

↓ 1 callersMethod_init_vars

(self)

code/代码7-8 yolohead.py:68

↓ 1 callersMethod_init_vars

(self)

code/代码7-7 darknet53.py:113

↓ 1 callersMethod_init_vars

(self)

code/代码7-9 yolov3.py:62

↓ 1 callersFunction_interpolate

(a, b=None)

code/代码8-11 AttGANmodels.py:83

↓ 1 callersMethod_load_1d_var

(self, variable)

code/代码7-10 weights.py:63

↓ 1 callersMethod_load_4d_var

(self, variable)

code/代码7-10 weights.py:68

↓ 1 callersFunction_loop_train

(model,optimizer, generator,grad)

code/代码7-12 mainyolo.py:76

↓ 1 callersFunction_loop_validation

(model, generator)

code/代码7-12 mainyolo.py:58

↓ 1 callersFunction_norm_image

(image,size,ch=1,flattenflag = False)

code/代码4-11 将图片文件制作成Dataset数据集TFa.py:60

↓ 1 callersFunction_norm_image

(image,size,ch=1,flattenflag = False)

code/代码9-2 finetune_resnet.py:42

↓ 1 callersFunction_norm_image

(image,size,ch=1,flattenflag = False)

code/代码4-10 将图片文件制作成Dataset数据集-TFa.py:60

↓ 1 callersFunction_norm_image

(image,size,ch=1,flattenflag = False)

code/代码7-3 用ResNet识别橘子和苹果.py:129

↓ 1 callersFunction_norm_image

(image,size,ch=1,flattenflag = False)

code/代码4-12 在动态图里读取Dataset数据集.py:67

↓ 1 callersMethod_parse_args

(self, n_users, n_items, n_entities, n_relations)

code/代码5-9 MKRmodel.py:50

↓ 1 callersFunction_set_scale_factor

()

code/代码7-6 box.py:90

↓ 1 callersMethod_skip

(self, i)

code/代码7-10 weights.py:48

↓ 1 callersFunction_squash

(in_caps, axes)

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:104

↓ 1 callersMethodadd_object

(self, x1, y1, x2, y2, name, code)

code/代码7-4 annotation.py:235

↓ 1 callersFunctionadjust_pred_tensor

(y_pred)

code/代码7-11 yololoss.py:16

↓ 1 callersFunctionboxes_to_array

(bound_boxes)

code/代码7-6 box.py:53

↓ 1 callersMethodbuild_mode

(self)

code/代码6-2 TextCNN模型.py:88

↓ 1 callersMethodcall

(self, stage3_in, stage4_in, stage5_in, training=False)

code/代码7-8 yolohead.py:42

↓ 1 callersMethodcall

(self, input_tensor, training=False)

code/代码7-7 darknet53.py:61

↓ 1 callersMethodcheck_attribute_conflict

(att_batch, att_name, att_names)

code/代码8-10 mydataset.py:202

↓ 1 callersFunctionconf_delta_tensor

(y_true, y_pred, anchors, ignore_thresh)

code/代码7-11 yololoss.py:33

↓ 1 callersFunctionconvert_kg

()

code/代码5-8 preprocess.py:84

↓ 1 callersFunctionconvert_rating

()

code/代码5-8 preprocess.py:30

↓ 1 callersFunctioncorrect_yolo_boxes

(boxes, image_h, image_w)

code/代码7-6 box.py:12

↓ 1 callersFunctioncounter

(start=0, scope=None)

code/代码8-12 trainattgan.py:216

↓ 1 callersFunctioncreate_anchor_boxes

(anchors)

code/代码7-5 generator.py:126

↓ 1 callersFunctiondataset_split

(rating_np)

code/代码5-11 data_loader.py:33

↓ 1 callersFunctiondecode_netout

(netout, anchors, obj_thresh, net_size, nb_box=3)

code/代码7-9 yolov3.py:95

↓ 1 callersMethoddetect

(self, image, anchors, net_size=416)

code/代码7-9 yolov3.py:66

↓ 1 callersFunctiondiscriminator_model

(image_shape)

code/代码8-7 deblurmodel.py:87

↓ 1 callersFunctiondisk_image_batch_dataset

(img_paths, batch_size, labels=None, filter=None,drop_remainder=True, map_func=No

code/代码8-10 mydataset.py:87

↓ 1 callersFunctiondraw_boxes

(image, boxes, labels, probs, class_labels, obj_thresh=0.0, desired_size=None)

code/代码7-6 box.py:88

↓ 1 callersFunctioneval_input_fn

(data,labels, batch_size)

code/代码3-3 用估算器框架训练一个回归模型.py:28

↓ 1 callersFunctioneval_input_fn

(data,labels, batch_size)

code/代码3-8 用估算器框架分布式训练ps.py:43

↓ 1 callersFunctioneval_input_fn

(data,labels, batch_size)

code/代码3-10 用估算器框架进行分布式训练worker.py:43

↓ 1 callersFunctioneval_input_fn

(data,labels, batch_size)

code/代码3-9 用估算器框架进行分布式训练chief.py:42

↓ 1 callersFunctioneval_input_fn

(data,labels, batch_size)

code/代码3-7 用估算器框架进行分布式训练.py:28

↓ 1 callersFunctionexport_model

导出模型. 参数: model: 估算器对象 model_type: 要导出的模型类型，可选值有 "wide"、 "deep" 或 "wide_deep" export_dir: 导出模型的路径.

code/代码5-6 用wide_deep模型预测人口收入.py:167

↓ 1 callersFunctionexport_model

导出模型. 参数: model: 估算器对象 model_type: 要导出的模型类型，可选值有 "wide"、 "deep" 或 "wide_deep" export_dir: 导出模型的路径.

code/代码5-7 用梯度提升树模型预测人口收入.py:182

↓ 1 callersFunctionfind_match_box

(centroid_box, centroid_boxes)

code/代码7-6 box.py:186

↓ 1 callersFunctiong_containing_d_multiple_outputs

(generator, discriminator,image_shape)

code/代码8-7 deblurmodel.py:121

↓ 1 callersFunctiongen_sequence

(id_df, seq_length, seq_cols)

code/代码5-12 预测飞机发动机的剩余使用寿命-TF2.py:96

↓ 1 callersFunctionget_attributes

(url,function=None)

code/代码9-5 ZSL_test.py:17

↓ 1 callersFunctionget_batches

(image,label,input_w,input_h,channels,batch_size)

code/代码4-3 将图片制作成内存对象数据集.py:41

↓ 1 callersMethodget_boxes

(self, annotation_file)

code/代码7-4 annotation.py:203

↓ 1 callersFunctionget_feed_dict_for_kge

(model, kg, start, end)

code/代码5-10 train.py:81

↓ 1 callersMethodget_fname

(self, annotation_file)

code/代码7-4 annotation.py:179

↓ 1 callersMethodget_score

(self)

code/代码7-6 box.py:36

↓ 1 callersMethodget_scores

(self, sess, feed_dict)

code/代码5-9 MKRmodel.py:170

↓ 1 callersFunctiongetone

(dataset)

code/代码4-6 interleave例子.py:27

↓ 1 callersFunctionimmerge

(images, row, col)

code/代码8-12 trainattgan.py:237

↓ 1 callersFunctionimwrite

Save an [-1.0, 1.0] image.

code/代码8-13 testattgan.py:47

↓ 1 callersFunctionimwrite

(image, path)

code/代码8-12 trainattgan.py:262

↓ 1 callersMethodiou

(self, bound_box)

code/代码7-6 box.py:39

↓ 1 callersMethodload_darknet_params

(self, weights_file, skip_detect_layer=False)

code/代码7-9 yolov3.py:38

↓ 1 callersFunctionload_data

(args)

code/代码5-11 data_loader.py:11

↓ 1 callersFunctionload_kg

(args)

code/代码5-11 data_loader.py:53

↓ 1 callersFunctionload_rating

(args)

code/代码5-11 data_loader.py:19

↓ 1 callersFunctionload_sample

递归读取文件。只支持一级。返回文件名、数值标签、数值对应的标签名

code/代码9-2 finetune_resnet.py:20

↓ 1 callersFunctionload_sample

递归读取文件。只支持一级。返回文件名、数值标签、数值对应的标签名

code/代码7-3 用ResNet识别橘子和苹果.py:21

↓ 1 callersFunctionload_sample

递归读取文件。只支持一级。返回文件名、数值标签、数值对应的标签名

code/代码9-3 feature_extractor.py:19

↓ 1 callersFunctionload_sample

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code/代码4-3 将图片制作成内存对象数据集.py:15

↓ 1 callersFunctionload_sample

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code/代码4-5 将图片文件制作成TFRecord数据集.py:17

↓ 1 callersFunctionlossCalculator

(y_true, y_pred, anchors,image_size)

code/代码7-11 yololoss.py:108

↓ 1 callersFunctionloss_class_tensor

(object_mask, pred_box_class, true_box_class, class_scale)

code/代码7-11 yololoss.py:93

↓ 1 callersFunctionloss_conf_tensor

(object_mask, pred_box_conf, true_box_conf, obj_scale, noobj_scale, conf_delta)

code/代码7-11 yololoss.py:86

↓ 1 callersFunctionloss_coord_tensor

(object_mask, pred_box, true_box, wh_scale, xywh_scale)

code/代码7-11 yololoss.py:81

↓ 1 callersFunctionmakeTFRec

(filenames,labels)

code/代码4-5 将图片文件制作成TFRecord数据集.py:44

↓ 1 callersFunctionmake_jitter_on_image

(image, boxes)

code/代码7-4 annotation.py:32

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