用于解析固定长度输入特性的配置。若要将稀疏输入视为密集输入,请提供default_value;否则,对于任何缺少此特性的示例,解析函数都将失败。
([seq_length], tf.int64), | 548 "input_ids": tf.FixedLenFeature...], tf.int64), | 549 "input_mask": tf.FixedLenFeature([seq_length...), | 550 "segment_ids": tf.FixedLenFeature([seq_length], tf.int64...| 551 "label_ids": tf.FixedLenFeature([], tf.int64),..."is_real_example": tf.FixedLenFeature([], tf.int64), | 552
': tf.FixedLenFeature([], tf.string), 'image/channels': tf.FixedLenFeature([], tf.int64),...'image/format': tf.FixedLenFeature([], tf.string), 'image/filename': tf.FixedLenFeature...([], tf.string), 'image/id': tf.FixedLenFeature([], tf.string), 'image/encoded...': tf.FixedLenFeature([], tf.string), 'image/extra': tf.FixedLenFeature([], tf.string),...'image/class/label': tf.FixedLenFeature([], tf.int64), 'image/class/text': tf.FixedLenFeature
def _parse_function(tfrecord_serialized): features={'label': tf.FixedLenFeature([], tf.int64),...'shape': tf.FixedLenFeature([], tf.string), 'image': tf.FixedLenFeature([], tf.string)}
Tensorflow提供了三种解析函数: 1、tf.FixedLenFeature(shape,dtype,default_value):解析定长特征,shape:输入数据形状、dtype:输入数据类型...、default_value:默认值; 代码如下: def read_demo(filepath): # 定义schema schema = { 'user_id': tf.FixedLenFeature...([], tf.int64), 'city_id': tf.FixedLenFeature([], tf.int64), 'app_type': tf.FixedLenFeature...([], tf.int64), 'viewed_pois': tf.VarLenFeature(tf.int64), 'avg_paid': tf.FixedLenFeature...([], tf.float32, default_value=0.0), 'comment': tf.FixedLenFeature([], tf.string, default_value
record): features = tf.parse_single_example( record, features={ 'image': tf.FixedLenFeature...([], tf.string), 'label': tf.FixedLenFeature([], tf.int64), 'height': tf.FixedLenFeature...([], tf.int64), 'width': tf.FixedLenFeature([], tf.int64), 'channels': tf.FixedLenFeature
reader.read(queue) features = tf.parse_single_example( serialized_example, features={ 'image_raw': tf.FixedLenFeature...([], tf.string), 'label_raw': tf.FixedLenFeature([], tf.string), }) image = tf.decode_raw(features...reader.read(file_name_queue) features = tf.parse_single_example(serialized_example, features={ 'data': tf.FixedLenFeature...([256,256], tf.float32), ### 'label': tf.FixedLenFeature([], tf.int64), 'id': tf.FixedLenFeature([]
serialized_example, features={ 'label': tf.FixedLenFeature...([], tf.int64), 'img_raw' : tf.FixedLenFeature([], tf.string),...'img_width': tf.FixedLenFeature([], tf.int64),...'img_height': tf.FixedLenFeature([], tf.int64), }) #取出包含image和label
由tf完成 keys_to_features = { 'image/encoded': tf.FixedLenFeature((), tf.string, default_value...=''), 'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),...'image/width': tf.FixedLenFeature((), tf.int64, default_value=0), 'image/height': tf.FixedLenFeature...((), tf.int64, default_value=0), 'image/label': tf.FixedLenFeature((), tf.int64, default_value
tf.parse_single_example(serialized_example, features={ 'image' : tf.FixedLenFeature...([], tf.string), 'label0': tf.FixedLenFeature([], tf.int64), 'label1': tf.FixedLenFeature([], tf.int64...), 'label2': tf.FixedLenFeature([], tf.int64), 'label3': tf.FixedLenFeature([], tf.int64), }) # 获取图片数据...([], tf.string), 'label0': tf.FixedLenFeature([], tf.int64), 'label1': tf.FixedLenFeature([], tf.int64...), 'label2': tf.FixedLenFeature([], tf.int64), 'label3': tf.FixedLenFeature([], tf.int64), }) # 获取图片数据
serialized_example = reader.read(queue)features = tf.parse_single_example(serialized_example,features={'image_raw': tf.FixedLenFeature...([], tf.string),'label_raw': tf.FixedLenFeature([], tf.string),})image = tf.decode_raw(features['image_raw...reader.read(file_name_queue)features = tf.parse_single_example(serialized_example, features={'data': tf.FixedLenFeature...([256,256], tf.float32),'label': tf.FixedLenFeature([], tf.int64),'id': tf.FixedLenFeature([], tf.int64
fm_feat_shape self.labels = _labels def parser(self, record): keys_to_features = { 'fm_feat_indices': tf.FixedLenFeature...([2], tf.int64), # (batch_size,2) 'labels': tf.FixedLenFeature([], tf.string), # (batch_size,) } parsed...在这个过程中会用到两个函数,tf.FixedLenFeature()和 tf.VarLenFeature(),前者是取固定长度的特征的,后者是针对不定长的特征的,关于这两个函数的具体使用情况可以参照官方文档...但是需要注意的一个地方是,这两个函数都有一个参数是shape,除了字符串类型的特征在取的时候用tf.FixedLenFeature()不用指定要取的特征的shape,其余类型的特征在取的时候要标明取得shape...labels_str = labels.tostring() tfrecord中对于变长数据和定长数据的处理 对于定长数据,可以把它转化成int,float,byte三种类型之一,然后存储,在读取的时候使用tf.FixedLenFeature
76 77 def _extract_fn(self, tfrecord): 78 feautres = { 79 'file_name': tf.FixedLenFeature...([], tf.string), 80 'img': tf.FixedLenFeature([], tf.string), 81 'label': tf.FixedLenFeature
Tensorflow提供了三种解析函数: tf.FixedLenFeature(shape,dtype,default_value):解析定长特征,shape:输入数据形状、dtype:输入数据类型、default_value...、default_value:默认值; 代码如下: def read_demo(filepath): # 定义schema schema = { 'user_id': tf.FixedLenFeature...([], tf.int64), 'city_id': tf.FixedLenFeature([], tf.int64), 'app_type': tf.FixedLenFeature...([], tf.int64), 'viewed_pois': tf.VarLenFeature(tf.int64), 'avg_paid': tf.FixedLenFeature...([], tf.float32, default_value=0.0), 'comment': tf.FixedLenFeature([], tf.string, default_value
#制作时期 tf.train.Feature(int64_list=tf.train.Int64List(value=[1.0])) #解码时期 tf.FixedLenFeature([],tf.int64...) # 返回 1.0 tf.FixedLenFeature([1],tf.int64) # 返回 [1.0] #对于之前的制作代码,这两种解码策略都是可以的,只不过返回的不同....#制作时期 tf.train.Feature(int64_list=tf.train.Int64List(value=[1.0, 2.0])) #解码时期 tf.FixedLenFeature([2],...[1.0, 2.0] #对于bytes,制作时期 tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytestring])) #解码时期 tf.FixedLenFeature...([],tf.string) tf.FixedLenFeature([1],tf.string) # 如果在制作过程中, value 的长度是变化的话,解码的时候是需要用tf.VarLenFeature
filename_queue) #返回文件名和文件 features = tf.parse_single_example(serialized_example, features={ 'label': tf.FixedLenFeature...([], tf.int64), 'img_raw' : tf.FixedLenFeature([], tf.string), }) #取出包含image和label的feature对象 image =...filename_queue) #返回文件名和文件 features = tf.parse_single_example(serialized_example, features={ 'label': tf.FixedLenFeature...([], tf.int64), 'img_raw' : tf.FixedLenFeature([], tf.string), }) #取出包含image和label的feature对象 image...([180], tf.float32), 'b': tf.FixedLenFeature([2], tf.int64), 'c': tf.FixedLenFeature([],tf.string)
一种方法是tf.FixedLenFeature, # 这种方法解析的结果为一个Tensor。...'image_raw': tf.FixedLenFeature([], tf.string), 'pixels': tf.FixedLenFeature([], tf.int64),
解析读入的一个样例 features = tf.parse_single_example( record, features={ 'feat1': tf.FixedLenFeature...([], tf.int64), 'feat2': tf.FixedLenFeature([], tf.int64), }) return features...parser(record): features = tf.parse_single_example( record, features={ 'image': tf.FixedLenFeature...([], tf.string), 'label': tf.FixedLenFeature([], tf.int64), 'height': tf.FixedLenFeature...([], tf.int64), 'width': tf.FixedLenFeature([], tf.int64), 'channels': tf.FixedLenFeature
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