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Functions35 in github.com/Sunwc0/vegetable_fruit_imageRe

⨍ Functions 35◇ Types & classes 0
↓ 12 callersFunctionadd_and_check_final
(name, net)
model/aliyun_train.py:150
↓ 12 callersFunctionadd_and_check_final
(name, net)
model/inception_resnet_v2.py:149
↓ 2 callersFunctiondecode_from_tfrecord
(filequeuelist, rows=299, cols=299)
TFrecord/tfrecord.py:85
↓ 2 callersFunctiongenerate_filenamequeue
(filequeuelist)
model/aliyun_train.py:382
↓ 2 callersFunctiongenerate_filenamequeue
(filequeuelist)
model/new_train.py:30
↓ 2 callersFunctiongenerate_filenamequeue
(filequeuelist)
TFrecord/tfrecord.py:106
↓ 2 callersFunctionget_batch
(filename_queue,batch_size)
model/aliyun_train.py:374
↓ 2 callersFunctionget_batch
(filename_queue,batch_size)
model/new_train.py:21
↓ 1 callersFunction_byte_feature
(value)
TFrecord/tfrecord.py:24
↓ 1 callersFunction_int64_feature
(value)
TFrecord/tfrecord.py:19
↓ 1 callersFunction_make_example
(label, image)
TFrecord/tfrecord.py:28
↓ 1 callersFunctionblock8
Builds the 8x8 resnet block.
model/aliyun_train.py:86
↓ 1 callersFunctionblock8
Builds the 8x8 resnet block.
model/inception_resnet_v2.py:85
↓ 1 callersFunctiondecode_from_tfrecord
(filequeuelist, rows=299, cols=299)
model/aliyun_train.py:361
↓ 1 callersFunctiondecode_from_tfrecord
(filequeuelist, rows=299, cols=299)
model/new_train.py:8
↓ 1 callersFunctiondistort_color
(image,color_ordering = 0)
TFrecord/transform.py:16
↓ 1 callersFunctionencode_to_tfrecord
(file_path)
TFrecord/tfrecord.py:35
↓ 1 callersFunctionget_batch
(filename_queue)
TFrecord/tfrecord.py:98
↓ 1 callersFunctioninception_resnet_v2
Creates the Inception Resnet V2 model. Args: inputs: 输入为一个4维张量 a 4-D tensor of size [batch_size, height, width, 3]. Dimension batch_size m
model/aliyun_train.py:288
↓ 1 callersFunctioninception_resnet_v2_base
Inception model from http://arxiv.org/abs/1602.07261. Constructs an Inception Resnet v2 network from inputs to the given final endpoint. This met
model/aliyun_train.py:112
↓ 1 callersFunctioninception_resnet_v2_base
Inception model from http://arxiv.org/abs/1602.07261. Constructs an Inception Resnet v2 network from inputs to the given final endpoint. This met
model/inception_resnet_v2.py:111
↓ 1 callersFunctionmain
()
TFrecord/tfrecord.py:147
↓ 1 callersFunctionpre_detail_image
(image,height,witdh,bbox)
TFrecord/transform.py:26
↓ 1 callersFunctionresize_image
()
TFrecord/transform.py:41
↓ 1 callersFunctionvalidate_img
()
validate.py:29
Functionblock17
Builds the 17x17 resnet block.
model/aliyun_train.py:60
Functionblock17
Builds the 17x17 resnet block.
model/inception_resnet_v2.py:59
Functionblock35
Builds the 35x35 resnet block.
model/aliyun_train.py:34
Functionblock35
Builds the 35x35 resnet block.
model/inception_resnet_v2.py:33
Functiondecode_batch
(filename, batch_size)
TFrecord/tfrecord.py:111
Functionfeature
(file_name)
TFrecord/tfrecord.py:67
Functioninception_resnet_v2
Creates the Inception Resnet V2 model. Args: inputs: 输入为一个4维张量 a 4-D tensor of size [batch_size, height, width, 3]. Dimension batch_size m
model/inception_resnet_v2.py:287
Functioninception_resnet_v2_arg_scope
Returns the scope with the default parameters for inception_resnet_v2. Args: weight_decay: the weight decay for weights variables. batch_nor
model/inception_resnet_v2.py:362
Functionmain
(_)
model/aliyun_train.py:388
Functionmain
()
TFrecord/transform.py:56