我觉得我错过了什么小东西，但似乎不能弄清楚它是什么。我试图在cassava数据集上训练一个非常简单的模型，但当我调用fit函数时，输入名称与预期名称不匹配。我尝试命名输入层以匹配模型，但tf坚持将_input附加到层名，导致冲突。我敢肯定这是tfds的一个相当典型的用例，而且一定是一些微不足道的东西。
错误：

ValueError: Missing data for input "flatten_input". You passed a data dictionary with keys ['image', 'image/filename', 'label']. Expected the following keys: ['flatten_input']

我从一个github项目中借用了查看代码，这个代码确实可以工作，因为我可以查看加载的数据。

# tensorflow 2.x core api
import logging
from mlflow.models import infer_signature
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras as K

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

#############################################################################################################
from matplotlib import pyplot as plt

def plot(examples, predictions=None):
  # Get the images, labels, and optionally predictions
  images = examples['image']
  labels = examples['label']
  batch_size = len(images)
  if predictions is None:
    predictions = batch_size * [None]

  # Configure the layout of the grid
  x = np.ceil(np.sqrt(batch_size))
  y = np.ceil(batch_size / x)
  fig = plt.figure(figsize=(x * 6, y * 7))

  for i, (image, label, prediction) in enumerate(zip(images, labels, predictions)):
    # Render the image
    ax = fig.add_subplot(int(x), int(y), i+1)
    ax.imshow(image, aspect='auto')
    ax.grid(False)
    ax.set_xticks([])
    ax.set_yticks([])

    # Display the label and optionally prediction
    x_label = 'Label: ' + name_map[class_names[label]]
    if prediction is not None:
      x_label = 'Prediction: ' + name_map[class_names[prediction]] + '\n' + x_label
      ax.xaxis.label.set_color('green' if label == prediction else 'red')
    ax.set_xlabel(x_label)

  plt.show()
  
  
# dataset, info = tfds.load('cassava', with_info=True)
dataset, info = tfds.load("cassava", shuffle_files=True, with_info=True)
print("INFO:\n", info)

# Extend the cassava dataset classes with 'unknown'
class_names = info.features['label'].names + ['unknown']

# Map the class names to human readable names
name_map = dict(
    cmd='Mosaic Disease',
    cbb='Bacterial Blight',
    cgm='Green Mite',
    cbsd='Brown Streak Disease',
    healthy='Healthy',
    unknown='Unknown')

print(len(class_names), 'classes:')
print(class_names)
print([name_map[name] for name in class_names])

def preprocess_fn(data):
  image = data['image']

  # Normalize [0, 255] to [0, 1]
  image = tf.cast(image, tf.float32)
  image = image / 255.

  # Resize the images to 224 x 224
  image = tf.image.resize(image, (224, 224))

  data['image'] = image
  return data

def create_model(type="default", n_classes=6):
    if type == "something":
        pass
    else:
        model = K.Sequential()
# naming the below layer with name argument still appends the _input to the actually name
        model.add(K.layers.Flatten(input_shape=(244, 244, 3)))
        model.add(K.layers.Dense(512, activation="relu"))
        model.add(K.layers.Dense(256, activation="relu"))
        model.add(K.layers.Dense(128, activation="relu"))
        model.add(K.layers.Dense(64, activation="relu"))
        model.add(K.layers.Dense(n_classes, activation="softmax"))
        model.compile(loss='sparse_categorical_crossentropy', optimizer=K.optimizers.Adam(0.01), metrics=['accuracy'])
        return model

# batch = dataset['validation'].map(preprocess_fn).batch(25).as_numpy_iterator()
# examples = next(batch)
# plot(examples)
print(tf.__version__)

model = create_model()
model.fit(dataset["train"], epochs=5)