模型训练后使用 TF model.predict() 会引发未知形状错误

Question

背景：我的数据形状为（batch_size、ghost_dim、data_length），我正在学习训练基于 MultiHeadAttention 的模型。我使用 model()、model.predict() 或 model.predict_on_batch() 函数输入一批相同维度的数据，以在拟合模型后返回预测数组，并且每个数据都出现类似的形状相关错误。

投掷：

2 frames
/usr/local/lib/python3.10/dist-packages/keras/engine/training.py in tf__predict_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 "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 2169, in predict_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 2155, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 2143, in run_step  **
        outputs = model.predict_step(data)
    File "/usr/local/lib/python3.10/dist-packages/keras/engine/training.py", line 2111, in predict_step
        return self(x, training=False)
    File "/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py", line 70, in error_handler
        raise e.with_traceback(filtered_tb) from None

    **ValueError: Exception encountered when calling layer 'layer_normalization_1' (type LayerNormalization).
    
    Cannot take the length of shape with unknown rank.
    
    Call arguments received by layer 'layer_normalization_1' (type LayerNormalization):
      • inputs=tf.Tensor(shape=<unknown>, dtype=float16)**

如果我直接使用 model() 函数调用，则 MultiHeadAttention 层中的输入形状会出现类似的错误，输入看起来是 1 维，而不是我输入的原始单批数据，即：

x_predict = np.random.normal(size=(1, 1, 512)) 

predictions = self.model(x_predict)
predictions = self.model.predict_on_batch(x_predict)

不确定所有这些 numpy 数组形状错误的根源是什么。有任何帮助/想法值得赞赏吗？

Answer 1

问题在于指定输入层形状中的批次：

decoder_inputs = layers.Input(batch_input_shape=shape, dtype=tensorflow.float16)

那么你的模型将始终假设你的训练和测试数据将由定义的batch_size = 32形成。所以batch_size = 1的x_predict会给出错误。我建议从输入层中删除batch_size：

decoder_inputs = layers.Input(shape=(ghost_dim, embed_dim), dtype=tensorflow.float16)

或者，如果您可以对 x_predict 数据进行采样，使其始终具有 (32, 1, 512) 的形状。