python - Lambda 层 TensorFlow 的多个输入

问题描述

我正在尝试在 keras/tensorflow 的 lambda 层中制作一个非常简单的加法器示例（将两个缩放器张量相加）。这是我的最小示例：

import tensorflow as tf
from tensorflow import keras

def adder(x1,x2):
  return tf.math.add(x1,x2)

我可以对我的加法器函数进行快速测试：

# Check that the adder function works well for two scaler tensors
scaler1 = tf.convert_to_tensor(21.1)
scaler2 = tf.convert_to_tensor(12.0)
print(adder(scaler1,scaler2))

得到正确的输出：

tf.Tensor(33.1, shape=(), dtype=float32)

所以我现在定义了一个带有一个 lambda 层的多输入 keras 网络，它试图将两个输入相加：

inpAdder1 = keras.layers.Input((1), name="Input1")
inpAdder2 = keras.layers.Input((1), name="Input2")
doAdd = keras.layers.Lambda(lambda x: adder(x[0],x[1]),name="LambdaLayer")([inpAdder1,inpAdder2])

modelAdder = keras.Model([inpAdder1,inpAdder2],doAdd)

网络摘要显示正确：

Model: "model_12"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
Input1 (InputLayer)             [(None, 1)]          0                                            
__________________________________________________________________________________________________
Input2 (InputLayer)             [(None, 1)]          0                                            
__________________________________________________________________________________________________
LambdaLayer (Lambda)            (None, 1)            0           Input1[0][0]                     
                                                                 Input2[0][0]                     
==================================================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0

但是，如果我尝试使用相同的缩放器张量调用一个简单的预测，我会收到一个错误，我不确定如何调试：

modelAdder.predict([scaler1, scaler2])

错误回溯：

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<command-4319124675560319> in <module>
     16 print(adder(scaler1,scaler2))
     17 
---> 18 modelAdder.predict([scaler1, scaler2])

/databricks/python/lib/python3.8/site-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1718                         '. Consider setting it to AutoShardPolicy.DATA.')
   1719 
-> 1720       data_handler = data_adapter.get_data_handler(
   1721           x=x,
   1722           batch_size=batch_size,

/databricks/python/lib/python3.8/site-packages/keras/engine/data_adapter.py in get_data_handler(*args, **kwargs)
   1381   if getattr(kwargs["model"], "_cluster_coordinator", None):
   1382     return _ClusterCoordinatorDataHandler(*args, **kwargs)
-> 1383   return DataHandler(*args, **kwargs)
   1384 
   1385 

/databricks/python/lib/python3.8/site-packages/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)
   1136 
   1137     adapter_cls = select_data_adapter(x, y)
-> 1138     self._adapter = adapter_cls(
   1139         x,
   1140         y,

/databricks/python/lib/python3.8/site-packages/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
    238     inputs = pack_x_y_sample_weight(x, y, sample_weights)
    239 
--> 240     num_samples = set(int(i.shape[0]) for i in tf.nest.flatten(inputs)).pop()
    241     _check_data_cardinality(inputs)
    242 

/databricks/python/lib/python3.8/site-packages/keras/engine/data_adapter.py in <genexpr>(.0)
    238     inputs = pack_x_y_sample_weight(x, y, sample_weights)
    239 
--> 240     num_samples = set(int(i.shape[0]) for i in tf.nest.flatten(inputs)).pop()
    241     _check_data_cardinality(inputs)
    242 

/databricks/python/lib/python3.8/site-packages/tensorflow/python/framework/tensor_shape.py in __getitem__(self, key)
    894       else:
    895         if self._v2_behavior:
--> 896           return self._dims[key].value
    897         else:
    898           return self._dims[key]

IndexError: list index out of range

我有一种感觉，我在这里遗漏了一些简单的东西，但我尝试查看类似的问题（例如：如何将两个输入放入 tensorflow lambda 层），但找不到我做错了什么。

标签： pythontensorflowkeras

python - Lambda 层 TensorFlow 的多个输入

问题描述

解决方案

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