**更新**在实现@的人的答案后：我的代码看起来像这样：
from __future__ import absolute_import, division, print_function, unicode_literalsimport tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltimport osimport PIL as pilfrom tensorflow import feature_columnfrom tensorflow_core.python.platform.flags import FLAGSos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'def print_type(name , x): print(" {} type = {}".format(name, type(x)))def _bytes_feature(value): """返回一个从字符串/字节生成的bytes_list。""" if isinstance(value, type(tf.constant(0))): value = value.numpy() # BytesList不会从EagerTensor中解包字符串。 return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))def _float_feature(value): if not isinstance(value, np.ndarray): value = [value] """返回一个从浮点数/双精度浮点数生成的float_list。""" return tf.train.Feature(float_list=tf.train.FloatList(value=value))def _int64_feature(value): """返回一个从布尔值/枚举/整数/无符号整数生成的int64_list。""" if not isinstance(value, np.ndarray): value = [value] return tf.train.Feature(int64_list=tf.train.Int64List(value=value))def encode_example(arr, label): shape = arr.shape feature = { 'height': _int64_feature(shape[0]), 'width': _int64_feature(shape[1]), 'label': _int64_feature(label), 'image_raw': _bytes_feature(arr.flatten().tobytes()), } # print("编码类型 {}".format(type(feature['image_raw']))) return tf.train.Example(features=tf.train.Features(feature=feature))def decode_example(serialized_example): # 创建一个描述特征的字典。 image_feature_description = { 'height': tf.io.FixedLenFeature([], tf.int64), 'width': tf.io.FixedLenFeature([], tf.int64), 'label': tf.io.FixedLenFeature([], tf.int64), 'image_raw': tf.io.FixedLenFeature([], tf.string), } example = tf.io.parse_single_example(serialized_example, image_feature_description) return exampledef map_example(height, width, image_raw, label): # 假设小端解码，传递little_endian=False以进行大端解码 image_data = tf.io.decode_raw(image_raw, tf.uint8) image_data = tf.reshape(image_data, [height, width]) return image_data, labeldef make_dataset(partition): files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord") dataset = tf.data.TFRecordDataset(files) # dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size) dataset = dataset.map(decode_example) dataset = dataset.map( lambda x: map_example(x['height'], x['width'], x['image_raw'], x['label'])) # dataset = dataset.batch(batch_size=FLAGS.batch_size) return datasetdef write_examples_to_record_file(file_name, x , y): with tf.io.TFRecordWriter(file_name) as writer: for i in range(len(x)): tf_example = encode_example(x[i], y[i]) writer.write(tf_example.SerializeToString())mnist = tf.keras.datasets.mnist(x_train, y_train), (x_test, y_test) = mnist.load_data()x_train, y_train, x_test, y_test = x_train[:100], y_train[:100], x_test[:100], y_test[:100]# x_train, x_test = x_train.astype(np.int8), x_test.astype(np.int8)write_examples_to_record_file('images_train.tfrecord', x_train, y_train)# write_examples_to_record_file('images_test.tfrecord', x_test, y_test)train_dataset = make_dataset("train")# test_dataset = make_dataset("test")it = iter(train_dataset)r = next(it)print_type("r",r)(x,y) = rprint_type("X", x)print_type("Y", y)print("x 是" , x)print("y 是" , y)print("x 的形状是" , x.shape())print("y 的形状是" , y.shape())# print(next(it))# for r,label in next(it):# print(repr(r))# print("特征形状 = {}".format(r.shape.as_list()))# print("标签形状 = {}".format(label.shape.as_list()))# feature_column = [tf.feature_column.numeric_column(key='image', shape=(28,28))]# feature_layer = tf.keras.layers.DenseFeatures(feature_column)## it = iter(train_dataset)model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=[28,28]), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')])model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])model.fit(train_dataset, epochs=5)# model.evaluate(test_dataset, verbose=2)但现在错误不同，我认为这是最初问题错误的原因，即获取next()元组的x,y组件的形状时存在问题：
2019-11-19 11:11:54.540221: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] 无法加载动态库 'cudart64_100.dll'；dlerror: 未找到cudart64_100.dll2019-11-19 11:11:56.763955: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] 无法加载动态库 'nvcuda.dll'；dlerror: 未找到nvcuda.dll2019-11-19 11:11:56.764410: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] 调用cuInit失败：未知错误（303）2019-11-19 11:11:56.767167: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] 为主机szclu-dvcasa027检索CUDA诊断信息2019-11-19 11:11:56.767572: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] 主机名：szclu-dvcasa0272019-11-19 11:11:56.768026: I tensorflow/core/platform/cpu_feature_guard.cc:142] 您的CPU支持此TensorFlow二进制文件未编译使用的指令：AVX2WARNING:tensorflow:实体 <function decode_example at 0x000002A67F5EE438> 无法转换，将按原样执行。请向AutoGraph团队报告此问题。提交错误时，请将详细程度设置为10（在Linux上，`export AUTOGRAPH_VERBOSITY=10`）并附上完整输出。原因：没有名为 'tensorflow_core.estimator' 的模块WARNING:tensorflow:实体 <function make_dataset.<locals>.<lambda> at 0x000002A67F61C0D8> 无法转换，将按原样执行。请向AutoGraph团队报告此问题。提交错误时，请将详细程度设置为10（在Linux上，`export AUTOGRAPH_VERBOSITY=10`）并附上完整输出。原因：没有名为 'tensorflow_core.estimator' 的模块 r type = <class 'tuple'> X type = <class 'tensorflow.python.framework.ops.EagerTensor'> Y type = <class 'tensorflow.python.framework.ops.EagerTensor'>x 是 tf.Tensor([[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136 175 26 166 255 247 127 0 0 0 0] [ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253 225 172 253 242 195 64 0 0 0 0] [ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251 93 82 82 56 39 0 0 0 0 0] [ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119 25 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253 150 27 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252 253 187 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 253 249 64 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253 253 207 2 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253 250 182 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201 78 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]], shape=(28, 28), dtype=uint8)y 是 tf.Tensor(5, shape=(), dtype=int64)Traceback (most recent call last): File "<input>", line 1, in <module> File "C:\Users\me\.IntelliJIdea2019.3\config\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # 执行脚本 File "C:\Users\me\.IntelliJIdea2019.3\config\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:/data/projects/cludeeplearning/train_model_so.py", line 108, in <module> print("x 的形状是" , x.shape())TypeError: 'TensorShape' 对象不可调用=============================================================================我正在尝试使用tfrecords进行实验，因为最终我们将有大量数据存储在多个文件中，这些文件可能无法一次性加载到内存中。
这个概念验证的基本想法是从mnist数据集加载数据，将其保存为tf.example格式的tfrecrod文件，然后使用tfrecorddataset从tfrecord中加载数据，并训练模型。
这是我的代码样本：
from __future__ import absolute_import, division, print_function, unicode_literalsimport tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltimport osimport PIL as pilfrom tensorflow import feature_columnfrom tensorflow_core.python.platform.flags import FLAGSos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'def _bytes_feature(value): """返回一个从字符串/字节生成的bytes_list。""" if isinstance(value, type(tf.constant(0))): value = value.numpy() # BytesList不会从EagerTensor中解包字符串。 return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))def _float_feature(value): if not isinstance(value, np.ndarray): value = [value] """返回一个从浮点数/双精度浮点数生成的float_list。""" return tf.train.Feature(float_list=tf.train.FloatList(value=value))def _int64_feature(value): """返回一个从布尔值/枚举/整数/无符号整数生成的int64_list。""" if not isinstance(value, np.ndarray): value = [value] return tf.train.Feature(int64_list=tf.train.Int64List(value=value))def encode_example(arr, label): shape = arr.shape feature = { 'height': _int64_feature(shape[0]), 'width': _int64_feature(shape[1]), 'label': _int64_feature(label), 'image_raw': _bytes_feature(arr.flatten().tobytes()), } # print("编码类型 {}".format(type(feature['image_raw']))) return tf.train.Example(features=tf.train.Features(feature=feature))def decode_example(serialized_example): # 创建一个描述特征的字典。 image_feature_description = { 'height': tf.io.FixedLenFeature([], tf.int64), 'width': tf.io.FixedLenFeature([], tf.int64), 'label': tf.io.FixedLenFeature([], tf.int64), 'image_raw': tf.io.FixedLenFeature([], tf.string), } example = tf.io.parse_single_example(serialized_example, image_feature_description) return exampledef map_example(height, width, image_raw, label): image_data = np.frombuffer(image_raw.numpy(), dtype=np.dtype('int64')) image_data = tf.reshape(image_data, [height.numpy(), width.numpy()]) # image_data.set_shape([28,28]) label = tf.constant([label.numpy()], tf.int64) return image_data, labeldef make_dataset(partition): files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord") dataset = tf.data.TFRecordDataset(files) # dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size) dataset = dataset.map(decode_example) dataset = dataset.map(lambda x: tf.py_function(func=map_example, inp=[x['height'], x['width'], x['image_raw'], x['label']], Tout=(tf.int64, tf.int64))) # dataset = dataset.batch(batch_size=FLAGS.batch_size) return datasetdef write_examples_to_record_file(file_name, x , y): with tf.io.TFRecordWriter(file_name) as writer: for i in range(len(x)): tf_example = encode_example(x[i], y[i]) writer.write(tf_example.SerializeToString())mnist = tf.keras.datasets.mnist(x_train, y_train), (x_test, y_test) = mnist.load_data()x_train, y_train, x_test, y_test = x_train[:100], y_train[:100], x_test[:100], y_test[:100]x_train, x_test = x_train / 255.0, x_test / 255.0write_examples_to_record_file('images_train.tfrecord', x_train, y_train)# write_examples_to_record_file('images_test.tfrecord', x_test, y_test)train_dataset = make_dataset("train")# test_dataset = make_dataset("test")for r in train_dataset.take(1): print(r[0].shape.as_list()) print(r[1])# feature_column = [tf.feature_column.numeric_column(key='image', shape=(28,28))]# feature_layer = tf.keras.layers.DenseFeatures(feature_column)#model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=[28,28,1]), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax')])model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])model.fit(train_dataset, epochs=5)# model.evaluate(test_dataset, verbose=2)在我开始训练之前，我遇到了以下错误，您知道为什么它不起作用吗？
2019-11-19 06:31:21.067987: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] 无法加载动态库 'cudart64_100.dll'；dlerror: 未找到cudart64_100.dll2019-11-19 06:31:23.315270: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] 无法加载动态库 'nvcuda.dll'；dlerror: 未找到nvcuda.dll2019-11-19 06:31:23.315617: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] 调用cuInit失败：未知错误（303）2019-11-19 06:31:23.320751: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] 为主机szclu-dvcasa027检索CUDA诊断信息2019-11-19 06:31:23.321132: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] 主机名：szclu-dvcasa0272019-11-19 06:31:23.321927: I tensorflow/core/platform/cpu_feature_guard.cc:142] 您的CPU支持此TensorFlow二进制文件未编译使用的指令：AVX2WARNING:tensorflow:实体 <function decode_example at 0x00000250EE94A1F8> 无法转换，将按原样执行。请向AutoGraph团队报告此问题。提交错误时，请将详细程度设置为10（在Linux上，`export AUTOGRAPH_VERBOSITY=10`）并附上完整输出。原因：没有名为 'tensorflow_core.estimator' 的模块WARNING:tensorflow:实体 <function make_dataset.<locals>.<lambda> at 0x00000250EE94A438> 无法转换，将按原样执行。请向AutoGraph团队报告此问题。提交错误时，请将详细程度设置为10（在Linux上，`export AUTOGRAPH_VERBOSITY=10`）并附上完整输出。原因：没有名为 'tensorflow_core.estimator' 的模块特征形状 = [28, 28]标签形状 = [1]Epoch 1/5Traceback (most recent call last): File "C:/data/projects/cludeeplearning/train_model.py", line 110, in <module> model.fit(train_dataset, epochs=5) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 728, in fit 1/Unknown - 0s 13ms/step 1/Unknown - 0s 13ms/step use_multiprocessing=use_multiprocessing) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 324, in fit total_epochs=epochs) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 123, in run_one_epoch batch_outs = execution_function(iterator) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 86, in execution_function distributed_function(input_fn)) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 457, in __call__ result = self._call(*args, **kwds) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 503, in _call self._initialize(args, kwds, add_initializers_to=initializer_map) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 408, in _initialize *args, **kwds)) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 1848, in _get_concrete_function_internal_garbage_collected graph_function, _, _ = self._maybe_define_function(args, kwargs) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 2150, in _maybe_define_function graph_function = self._create_graph_function(args, kwargs) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\function.py", line 2041, in _create_graph_function capture_by_value=self._capture_by_value), File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 358, in wrapped_fn return weak_wrapped_fn().__wrapped__(*args, **kwds) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 66, in distributed_function model, input_iterator, mode) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 112, in _prepare_feed_values inputs, targets, sample_weights = _get_input_from_iterator(inputs) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 149, in _get_input_from_iterator distribution_strategy_context.get_strategy(), x, y, sample_weights) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 308, in validate_distributed_dataset_inputs x_values_list = validate_per_replica_inputs(distribution_strategy, x) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 356, in validate_per_replica_inputs validate_all_tensor_shapes(x, x_values) File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\keras\distribute\distributed_training_utils.py", line 373, in validate_all_tensor_shapes x_shape = x_values[0].shape.as_list() File "C:\Users\me\AppData\Local\Continuum\anaconda3\envs\deeplearning\lib\site-packages\tensorflow_core\python\framework\tensor_shape.py", line 1171, in as_list raise ValueError("as_list() 未在未知TensorShape上定义。")ValueError: as_list() 未在未知TensorShape上定义。Process finished with exit code 1任何帮助都将不胜感激。谢谢
回答：
在没有一个自包含的示例来测试的情况下，很难确定问题所在，但我认为问题可能来自于使用tf.py_func，这可能会丢失张量的形状信息。不过，您在这里并不需要它，我认为您可以将map_example和make_dataset定义为等效的如下内容：
def map_example(height, width, image_raw, label): image_data = tf.io.decode_raw(image_raw, tf.uint8) image_data = tf.reshape(image_data, [1, height, width]) return image_data, labeldef make_dataset(partition): files = tf.data.Dataset.list_files("images_" + partition + "*.tfrecord") dataset = tf.data.TFRecordDataset(files) # dataset = dataset.shuffle(buffer_size=FLAGS.shuffle_buffer_size) dataset = dataset.map(decode_example) dataset = dataset.map( lambda x: map_example(x['height'], x['width'], x['image_raw'], x['label'])) # dataset = dataset.batch(batch_size=FLAGS.batch_size) return dataset