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我正在使用 tensorflow v2.7.0 并尝试使用不规则张量创建 ML 模型。

问题是 tf.linalg.diag、tf.matmul 和 tf.linalg.det 不适用于不规则张量。我通过在 numpy 中转换参差不齐的张量并将其转换回参差不齐的张量找到了一种解决方法，但是在全局模型中应用图层时它不起作用。

以下代码正在运行

import tensorflow as tf

class LRDet(tf.keras.layers.Layer):
    
    def __init__(self,numItems,rank=10):
        super(LRDet,self).__init__()
        self.numItems = numItems
        self.rank = rank
    
    def build(self,input_shape):
        V_init = tf.random_normal_initializer(mean=0.0,stddev=0.01)
        D_init = tf.random_normal_initializer(mean=1.0,stddev=0.01)
        self.V = tf.Variable(name='V',initial_value=V_init(shape=(self.numItems, self.rank)),trainable=True)
        self.D = tf.Variable(name='D',initial_value=D_init(shape=(self.numItems,)),trainable=True)
    
    def call(self,inputs):
        batch_size = inputs.nrows()
        subV = tf.gather(self.V,inputs)
        subD = tf.square(tf.gather(self.D,inputs,batch_dims=0))#tf.linalg.diag(tf.square(tf.gather(D,Xrag,batch_dims=0)))
        subD = tf.ragged.constant([tf.linalg.diag(subD[i]).numpy() for i in tf.range(batch_size)])
        K = tf.ragged.constant([tf.matmul(subV[i],subV[i],transpose_b=True).numpy() for i in tf.range(batch_size)])
        K = tf.add(K,subD)
        res = tf.ragged.constant([tf.linalg.det(K[i].to_tensor()).numpy() for i in tf.range(batch_size)])
        return res
        

numItems = 10
rank = 3
detX = LRDet(numItems,rank)
X = [[1,2],[3],[4,5,6]]
Xrag = tf.ragged.constant(X)
_ = detX(Xrag)

但是一旦我在更全局的模型中使用了这一层，我就会遇到以下错误

OperatorNotAllowedInGraphError：调用层“lr_det_10”（类型 LRDet）时遇到异常。

    in user code:
    
        File "<ipython-input-57-6b073a14386e>", line 18, in call  *
            subD = tf.ragged.constant([tf.linalg.diag(subD[i]).numpy() for i in tf.range(batch_size)])
    
        OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.

我尝试使用 tf.map_fn 而不是 .numpy() 的列表理解，但没有成功。

任何帮助将不胜感激。

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1 回答 1

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这是一个与tf.map_fn;一起运行的选项 然而，由于最近的一个关于、参差不齐的张量和 GPU 的错误，它目前只能在 CPU 上运行：tf.map_fn

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # do not access GPU
import tensorflow as tf

class LRDet(tf.keras.layers.Layer):
    
    def __init__(self,numItems,rank=10):
        super(LRDet,self).__init__()
        self.numItems = numItems
        self.rank = rank
    
    def build(self,input_shape):
        V_init = tf.random_normal_initializer(mean=0.0,stddev=0.01)
        D_init = tf.random_normal_initializer(mean=1.0,stddev=0.01)
        self.V = tf.Variable(name='V',initial_value=V_init(shape=(self.numItems, self.rank)),trainable=True)
        self.D = tf.Variable(name='D',initial_value=D_init(shape=(self.numItems,)),trainable=True)

    def call(self,inputs):
        batch_size = inputs.nrows()
        subV = tf.gather(self.V,inputs)
    
        subD = tf.square(tf.gather(self.D, inputs, batch_dims=0))
        subD = tf.map_fn(self.diag, subD, fn_output_signature=tf.RaggedTensorSpec(shape=[1, None, None],
                                                                    dtype=tf.type_spec_from_value(subD).dtype,
                                                                    ragged_rank=2,
                                                                    row_splits_dtype=tf.type_spec_from_value(subD).row_splits_dtype))
        subD = tf.squeeze(subD, 1)


        K = tf.map_fn(self.matmul, subV, fn_output_signature=tf.RaggedTensorSpec(shape=[1, None, None],
                                                                    dtype=tf.type_spec_from_value(subV).dtype,
                                                                    ragged_rank=2,
                                                                    row_splits_dtype=tf.type_spec_from_value(subV).row_splits_dtype))
        K = tf.squeeze(K, 1)
        K = tf.add(K,subD)

        res = tf.map_fn(self.det, K, tf.TensorSpec(shape=(), dtype=tf.float32, name=None))

        return res
        

    def diag(self, x):
      return tf.ragged.stack(tf.linalg.diag(x))

    def matmul(self, x):
      return tf.ragged.stack(tf.matmul(x, x,transpose_b=True))

    def det(self, x):
      return tf.linalg.det(x.to_tensor())


numItems = 10
rank = 3
input = tf.keras.layers.Input(shape=(None,), ragged=True, dtype=tf.int32)
detX = LRDet(numItems,rank)
output = detX(input)
model = tf.keras.Model(input, output)

X = [[1,2],[3],[4,5,6]]
Xrag = tf.ragged.constant(X)
y = tf.random.normal((3, 1))
model.compile(loss='mse', optimizer='adam')
model.fit(Xrag, y, batch_size=1, epochs=1)
于 2021-12-20T11:07:34.280 回答