Python （已解决）Tensorflow Federated | tff.learning.from_keras_model（），具有密集特征层和多输入的模型

我正在尝试联合具有多个输入的keras模型。 这些输入中有些是分类的，有些是数字的，所以我有一些DenseFeature层来嵌入这些值

问题是，使用tff.learning.from_keras_model（）期望作为输入\u spec一个只包含2个元素（x，y）的字典，但我有多个输入，然后我必须在模型中区分这些输入，才能正确地使用feature_columns函数和DenseFeature层执行嵌入

如果模型只接受“x”作为输入，而没有正确的列名称，那么如何处理单个要素列

多谢各位

以下是代码和错误：

def create_keras_model():
  l = tf.keras.layers

  # handling numerical columns 
  for header in numerical_column_names:
    feature_columns.append(feature_column.numeric_column(header))

  # handling the categorical feature  
  pickup = feature_column.categorical_column_with_vocabulary_list(
      'pickup_location_id', [i for i in range(number_of_locations)])
  #pickup_one_hot = feature_column.indicator_column(pickup)
  #feature_columns.append(pickup_one_hot)

  pickup_embedding = feature_column.embedding_column(pickup, dimension=64)
  #feature_columns.append(pickup_embedding)


  feature_inputs = {
    'pickup_week_day_sin': tf.keras.Input((1,), name='pickup_week_day_sin'),
    'pickup_week_day_cos': tf.keras.Input((1,), name='pickup_week_day_cos'),
    'pickup_hour_sin': tf.keras.Input((1,), name='pickup_hour_sin'),
    'pickup_hour_cos': tf.keras.Input((1,), name='pickup_hour_cos'),
    'pickup_month_sin': tf.keras.Input((1,), name='pickup_month_sin'),
    'pickup_month_cos': tf.keras.Input((1,), name='pickup_month_cos'),
  }
  numerical_features = l.DenseFeatures(feature_columns)(feature_inputs)#{'x': a}

  location_input = {
      'pickup_location_id': tf.keras.Input((1,), dtype=tf.dtypes.int32, name='pickup_location_id'),
  }
  categorical_features = l.DenseFeatures(pickup_embedding)(location_input)#{'x': a}
  #i = l.Input(shape=(64+6,))

  #embedded_lookup_feature = tf.feature_column.numeric_column('x', shape=(784))
  conca = l.Concatenate()([categorical_features, numerical_features])

  dense = l.Dense(128, activation='relu')(conca)
  dense_1 = l.Dense(128, activation='relu')(dense)
  dense_2 = layers.Dense(number_of_locations, kernel_initializer='zeros')(dense_1)
  output = l.Softmax()(dense_2)

  inputs = list(feature_inputs.values()) + list(location_input.values())
  return tf.keras.Model(inputs=inputs, outputs=output)

---

---

调用时出错：

ValueError: The top-level structure in `dummy_batch` or `input_spec` must contain exactly two elements, as it must contain type information for both inputs to and predictions from the model.

---

预处理的\u示例\u dataset.element\u规范：

OrderedDict([('pickup_location_id',
              TensorSpec(shape=(None,), dtype=tf.int32, name=None)),
             ('pickup_hour_sin',
              TensorSpec(shape=(None,), dtype=tf.float32, name=None)),
             ('pickup_hour_cos',
              TensorSpec(shape=(None,), dtype=tf.float32, name=None)),
             ('pickup_week_day_sin',
              TensorSpec(shape=(None,), dtype=tf.float32, name=None)),
             ('pickup_week_day_cos',
              TensorSpec(shape=(None,), dtype=tf.float32, name=None)),
             ('pickup_month_sin',
              TensorSpec(shape=(None,), dtype=tf.float32, name=None)),
             ('pickup_month_cos',
              TensorSpec(shape=(None,), dtype=tf.float32, name=None)),
             ('y', TensorSpec(shape=(None,), dtype=tf.int32, name=None))])

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我在GitHub上的联邦成员学习库中找到了答案：

方法是将OrderedICT的“x”值设置为OrderedICT本身，并使用我们想要作为输入的列的名称作为键

这里给出了一个具体的例子：

其中定义了输入规范：

 input_spec = collections.OrderedDict(
        x=collections.OrderedDict(
            a=tf.TensorSpec(shape=[None, 1], dtype=tf.float32),
            b=tf.TensorSpec(shape=[1, 1], dtype=tf.float32)),
        y=tf.TensorSpec(shape=[None, 1], dtype=tf.float32))
    model = model_examples.build_multiple_inputs_keras_model()

---

在定义为以下内容的模型中使用：

def build_multiple_inputs_keras_model():
  """Builds a test model with two inputs."""
  l = tf.keras.layers
  a = l.Input((1,), name='a')
  b = l.Input((1,), name='b')
  # Each input has a single, independent dense layer, which are combined into
  # a final dense layer.
  output = l.Dense(1)(
      l.concatenate([
          l.Dense(1)(a),
          l.Dense(1)(b),
      ]))
  return tf.keras.Model(inputs={'a': a, 'b': b}, outputs=[output])

---

在实现您的答案时，我遇到了这样一个错误：“AttributeError:Tensor.op在启用了急切执行时没有意义。”您是否也有同样的问题？我使用TF2.1Btw，我使用TF2.2。如果你有同样的问题，请告诉我。如果是，如何解决？谢谢

def build_multiple_inputs_keras_model():
  """Builds a test model with two inputs."""
  l = tf.keras.layers
  a = l.Input((1,), name='a')
  b = l.Input((1,), name='b')
  # Each input has a single, independent dense layer, which are combined into
  # a final dense layer.
  output = l.Dense(1)(
      l.concatenate([
          l.Dense(1)(a),
          l.Dense(1)(b),
      ]))
  return tf.keras.Model(inputs={'a': a, 'b': b}, outputs=[output])