TensorFlow Keras训练报错：不支持字符串转浮点型的解决过程

问题场景 #

在Google Colab中基于Roboflow数据集训练目标检测模型，执行以下训练代码时出现类型转换错误：

batch_size = 16
num_epochs = 10

train_dataset = create_dataset(TRAINING_TFRecord, batch_size)

model.fit(train_dataset, epochs=num_epochs)

报错详情 #

Epoch 1/10
--------------------------------------------------------------------------- UnimplementedError                        Traceback (most recent call last) <ipython-input-60-99c88cc37d2a> in <cell line: 8>() 6 train_dataset = create_dataset(TRAINING_TFRecord, batch_size) 7  8 model.fit(train_dataset, epochs=num_epochs)

1 frames /usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 51   try: 52     ctx.ensure_initialized() 53     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, 54                                         inputs, attrs, num_outputs) 55   except core._NotOkStatusException as e:

UnimplementedError: Graph execution error:

Detected at node Cast defined at (most recent call last): File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main

File "/usr/lib/python3.10/runpy.py", line 86, in _run_code

File "/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py", line 37, in <module>

File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance

File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py", line 619, in start

File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py", line 195, in start

File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever

File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once

File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run

File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 685, in <lambda>

File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 738, in _run_callback

File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 825, in inner

File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 786, in run

File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 361, in process_one

File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper

File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell

File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper

File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 539, in execute_request

File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper

File "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py", line 302, in do_execute

File "/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py", line 539, in run_cell

File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 2975, in run_cell

File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3030, in _run_cell

File "/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner

File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async

File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes

File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code

File "<ipython-input-52-99c88cc37d2a>", line 8, in <cell line: 8>

File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1807, in fit

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1401, in train_function

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1384, in step_function

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1373, in run_step

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1151, in train_step

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1209, in compute_loss

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/compile_utils.py", line 275, in __call__

File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/compile_utils.py", line 860, in match_dtype_and_rank

2 root error(s) found.   (0) UNIMPLEMENTED:  Cast string to float is not supported [[{{node Cast}}]]   (1) CANCELLED:  Function was cancelled before it was started 0 successful operations. 0 derived errors ignored. [Op:__inference_train_function_5403]

相关代码实现 #

TFRecord解析函数 #

def parse_tfrecord_fn(example):
    feature_description = {
        'image/encoded': tf.io.FixedLenFeature([], tf.string),
    'image/filename': tf.io.FixedLenFeature([], tf.string),
    'image/format': tf.io.FixedLenFeature([], tf.string),
    'image/height': tf.io.FixedLenFeature([], tf.int64),
    'image/width': tf.io.FixedLenFeature([], tf.int64),
    'image/object/bbox/xmax': tf.io.FixedLenFeature([], tf.float32),
    'image/object/bbox/xmin': tf.io.FixedLenFeature([], tf.float32),
    'image/object/bbox/ymax': tf.io.FixedLenFeature([], tf.float32),
    'image/object/bbox/ymin': tf.io.FixedLenFeature([], tf.float32),
    'image/object/class/label': tf.io.FixedLenFeature([], tf.int64),
    'image/object/class/text': tf.io.FixedLenFeature([], tf.string )
    }
    example = tf.io.parse_single_example(example, feature_description)
    image = tf.io.decode_jpeg(example['image/encoded'])
    label = example['image/object/class/label']
    filename = example['image/filename']
    format = example['image/object/class/label']
    xmin = example['image/object/bbox/xmin']
    ymin = example['image/object/bbox/ymin']
    xmax = example['image/object/bbox/xmax']
    ymax = example['image/object/bbox/xmax']
    text = example['image/object/class/text']

    return image, label, text

数据集创建函数 #

def create_dataset(tfrecord_file, batch_size):
    dataset = tf.data.TFRecordDataset(tfrecord_file)
    dataset = dataset.map(parse_tfrecord_fn)
    dataset = dataset.batch(batch_size)
    # 可添加其他数据转换操作
    return dataset

模型定义函数 #

from tensorflow.keras import layers, models
def create_cnn_model(input_shape, num_classes):
    model = models.Sequential()

    model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
    model.add(layers.MaxPooling2D((2, 2)))

    model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))

    model.add(layers.Conv2D(64, (3, 3), activation='relu'))

    model.add(layers.Flatten())

    model.add(layers.Dense(128, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid')) # 用于二分类
    #model.add(layers.Dense(num_classes, activation='softmax')) # 用于多分类

    model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',  # 当标签为整数类型时使用
              metrics=['accuracy'])

    return model

错误原因及修复方案 #

错误原因 #

模型使用sparse_categorical_crossentropy损失函数，要求标签必须为整数类型，但实际传入的是字符串类型标签，导致TensorFlow尝试将字符串转为浮点数时触发不支持的操作错误。

修复后的解析函数 #

通过建立标签字符串到整数的映射，使用tf.py_function将字符串标签转换为整数（注：修复了原代码中参数传递错误，实际应传入字符串类型的image/object/class/text）：

label_mapping = {"black stain": 1, "decayed tooth": 2}
def convert_label(label_string):
    return label_mapping[label_string.numpy().decode('utf-8')]

def parse_tfrecord_fn(example):
    feature_description = {
        'image/encoded': tf.io.FixedLenFeature([], tf.string),
    'image/filename': tf.io.FixedLenFeature([], tf.string),
    'image/format': tf.io.FixedLenFeature([], tf.string),
    'image/height': tf.io.FixedLenFeature([], tf.int64),
    'image/width': tf.io.FixedLenFeature([], tf.int64),
    'image/object/bbox/xmax': tf.io.FixedLenFeature([], tf.float32),
    'image/object/bbox/xmin': tf.io.FixedLenFeature([], tf.float32),
    'image/object/bbox/ymax': tf.io.FixedLenFeature([], tf.float32),
    'image/object/bbox/ymin': tf.io.FixedLenFeature([], tf.float32),
    'image/object/class/label': tf.io.FixedLenFeature([], tf.int64),
    'image/object/class/text': tf.io.FixedLenFeature([], tf.string ) # 需要将字符串转为整数
    }
    example = tf.io.parse_single_example(example, feature_description)
    image = tf.io.decode_jpeg(example['image/encoded']),
    label = tf.py_function(convert_label, [example['image/object/class/text']], tf.int64),
    filename = example['image/filename']
    format = example['image/object/class/label']
    xmin = example['image/object/bbox/xmin']
    ymin = example['image/object/bbox/ymin']
    xmax = example['image/object/bbox/xmax']
    ymax = example['image/object/bbox/xmax']
    text = example['image/object/class/text']

    return image, label

内容的提问来源于stack exchange，提问作者Staddl