我有一个应用程序，需要使用 tf.data 设置管道。我所拥有的数据存储在Matlab中创建的.mat文件中，包含三个变量“s_matrix”，它是一个224x224x3双精度数组，一个“框架”，它是1024x1复数双精度，最后是一个数字标签。管道将这样加载，以便我可以将数据提供给 model.fit 函数。到目前为止，我一直在使用的用于加载和处理数据的代码添加在下面，但我不断收到几个类型错误和意外的字节错误。

更新2：进行一些修改

通过包含 Giorgos 建议的更改以及更改数据集生成器并使用 tf.data.Dataset.from_generator 函数来更新代码。有一些明显的改进，但现在的问题是只有两个输入之一被传递。

# Define the shape of the input image
input_shape = (224, 224, 3)

# Define the shape of the complex vector after conversion
complex_shape = (1024, 2, 1)

# Define a function to load and preprocess each sample
def load_and_preprocess_sample(sample_path):
    # Load the sample from the mat file
    sample = scipy.io.loadmat(sample_path)
    matrix = sample['s_matrix']
    complex_vector = sample['frame']
    label = sample['numeric_label']

    # Preprocess the matrix, complex vector, and label as needed
    real = tf.reshape(tf.math.real(complex_vector), [1024, 1])
    imag = tf.reshape(tf.math.imag(complex_vector), [1024, 1])
    signal_tensor = tf.concat([real, imag], axis=-1)
    signal_tensor = tf.reshape(signal_tensor, [1024, 2, 1])
    signal = signal_tensor
    # Normalize the matrix values between 0 and 1
    matrix = matrix / 255.0

    return matrix, signal, label


# Define a generator function to generate the samples
def sample_generator(file_paths):
    for file_path in file_paths:
        #yield load_and_preprocess_sample(file_path)
        matrix, complex_vector, label = load_and_preprocess_sample(file_path)
        yield (matrix, complex_vector), label

# Modify the create_dataset() function to use from_generator
def create_dataset(file_paths):
    dataset = tf.data.Dataset.from_generator(
        generator=lambda: sample_generator(file_paths),
        output_signature=(
            tf.TensorSpec(shape=input_shape, dtype=tf.float32),
            tf.TensorSpec(shape=complex_shape, dtype=tf.float32),
            tf.TensorSpec(shape=(1,), dtype=tf.float32)
        )
    )

    dataset = dataset.shuffle(buffer_size=len(file_paths))
    dataset = dataset.batch(batch_size)
    dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)

    return dataset

# Get a list of all file paths in the data folder
file_paths = [os.path.join(data_path, f) for f in os.listdir(data_path) if f.endswith('.mat')]

# Split file paths into training and validation sets
train_file_paths = file_paths[:-num_val_samples]
val_file_paths = file_paths[-num_val_samples:]

生成的数据和模型调用如下：

# Create datasets for training and validation sets
train_dataset = create_dataset(train_file_paths)
val_dataset = create_dataset(val_file_paths)
...
...
...
model = tf.keras.Model(inputs=[input1, input2], outputs=output)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train your model
model.fit(train_dataset,
          epochs=5,
          steps_per_epoch=num_train_samples // batch_size,
          validation_data=val_dataset,
          validation_steps=num_val_samples // batch_size)

当前的错误输出共享如下：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[7], line 90
     87 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
     89 # Train your model
---> 90 model.fit(train_dataset,
     91           epochs=5,
     92           steps_per_epoch=num_train_samples // batch_size,
     93           validation_data=val_dataset,
     94           validation_steps=num_val_samples // batch_size)

File ~\miniconda3\envs\tf2\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File ~\AppData\Local\Temp\__autograph_generated_filea4_9b7hv.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator)
     13 try:
     14     do_return = True
---> 15     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16 except:
     17     do_return = False

ValueError: in user code:

    File "C:\Users\Admin\miniconda3\envs\tf2\lib\site-packages\keras\engine\training.py", line 1160, in train_function  *
        return step_function(self, iterator)
    File "C:\Users\Admin\miniconda3\envs\tf2\lib\site-packages\keras\engine\training.py", line 1146, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\Admin\miniconda3\envs\tf2\lib\site-packages\keras\engine\training.py", line 1135, in run_step  **
        outputs = model.train_step(data)
    File "C:\Users\Admin\miniconda3\envs\tf2\lib\site-packages\keras\engine\training.py", line 993, in train_step
        y_pred = self(x, training=True)
    File "C:\Users\Admin\miniconda3\envs\tf2\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "C:\Users\Admin\miniconda3\envs\tf2\lib\site-packages\keras\engine\input_spec.py", line 216, in assert_input_compatibility
        raise ValueError(

    ValueError: Layer "model" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 224, 224, 3) dtype=float32>]