我试图通过使用参数矩阵来隔离一些用户特定的参数，其中每个数组将学习该用户特定的参数。
我想使用用户ID为矩阵建立索引，并将参数连接到其他功能。
最后，有一些完全连接的层才能获得理想的结果。
但是，我在代码的最后一行不断收到此错误。
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-93de3591ccf0> in <module>
20 # combined = tf.keras.layers.Concatenate(axis=-1)([le_param, le])
21
---> 22 net = tf.keras.layers.Dense(128)(combined)
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
793 # framework.
794 if build_graph and base_layer_utils.needs_keras_history(inputs):
--> 795 base_layer_utils.create_keras_history(inputs)
796
797 # Clear eager losses on top level model call.
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)
182 keras_tensors: The Tensors found that came from a Keras Layer.
183 """
--> 184 _, created_layers = _create_keras_history_helper(tensors, set(), [])
185 return created_layers
186
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
229 constants[i] = backend.function([], op_input)([])
230 processed_ops, created_layers = _create_keras_history_helper(
--> 231 layer_inputs, processed_ops, created_layers)
232 name = op.name
233 node_def = op.node_def.SerializeToString()
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
229 constants[i] = backend.function([], op_input)([])
230 processed_ops, created_layers = _create_keras_history_helper(
--> 231 layer_inputs, processed_ops, created_layers)
232 name = op.name
233 node_def = op.node_def.SerializeToString()
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
227 else:
228 with ops.init_scope():
--> 229 constants[i] = backend.function([], op_input)([])
230 processed_ops, created_layers = _create_keras_history_helper(
231 layer_inputs, processed_ops, created_layers)
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py in __call__(self, inputs)
3746 return nest.pack_sequence_as(
3747 self._outputs_structure,
-> 3748 [x._numpy() for x in outputs], # pylint: disable=protected-access
3749 expand_composites=True)
3750
~/anaconda3/envs/tam-env/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py in <listcomp>(.0)
3746 return nest.pack_sequence_as(
3747 self._outputs_structure,
-> 3748 [x._numpy() for x in outputs], # pylint: disable=protected-access
3749 expand_composites=True)
3750
ValueError: Cannot convert a Tensor of dtype resource to a NumPy array.
重现该错误的代码：
import tensorflow as tf
num_uids = 50
input_uid = tf.keras.layers.Input(shape=(1,), dtype=tf.int32)
params = tf.Variable(tf.random.normal((num_uids, 9)), trainable=True)
param = tf.gather_nd(params, input_uid)
input_shared_features = tf.keras.layers.Input(shape=(128,), dtype=tf.float32)
combined = tf.concat([param, input_shared_features], axis=-1)
net = tf.keras.layers.Dense(128)(combined)
我尝试了几件事：
奇怪的是，如果我指定项目数并将Input替换为tf.Variable，代码将按预期工作：
import tensorflow as tf
num_uids = 50
input_uid = tf.Variable(tf.ones((32, 1), dtype=tf.int32))
params = tf.Variable(tf.random.normal((num_uids, 9)), trainable=True)
param = tf.gather_nd(params, input_uid)
input_shared_features = tf.Variable(tf.ones((32, 128), dtype=tf.float32))
combined = tf.concat([param, input_shared_features], axis=-1)
net = tf.keras.layers.Dense(128)(combined)
我正在将Tensorflow 2.1与Python 3.6.10一起使用
答案 0 :(得分：2)
当我尝试在TensorFlow 2.x中使用TensorFlow表查找（tf.lookup.StaticHashTable）时遇到了类似的问题。我最终通过将其保存在Custom Keras Layer中来解决了该问题。相同的解决方案似乎也可以解决该问题，至少在问题中提到的版本之前都是如此。 （我尝试使用TensorFlow 2.0、2.1和2.2，并且在所有这些版本中都可以使用。）
import tensorflow as tf
num_uids = 50
input_uid = tf.keras.Input(shape=(1,), dtype=tf.int32)
input_shared_features = tf.keras.layers.Input(shape=(128,), dtype=tf.float32)
class CustomLayer(tf.keras.layers.Layer):
def __init__(self,num_uids):
super(CustomLayer, self).__init__(trainable=True,dtype=tf.int64)
self.num_uids = num_uids
def build(self,input_shape):
self.params = tf.Variable(tf.random.normal((num_uids, 9)), trainable=True)
self.built=True
def call(self, input_uid,input_shared_features):
param = tf.gather_nd(self.params, input_uid)
combined = tf.concat([param, input_shared_features], axis=-1)
return combined
def get_config(self):
config = super(CustomLayer, self).get_config()
config.update({'num_uids': self.num_uids})
return config
combined = CustomLayer(num_uids)(input_uid,input_shared_features)
net = tf.keras.layers.Dense(128)(combined)
model = tf.keras.Model(inputs={'input_uid':input_uid,'input_shared_features':input_shared_features},outputs=net)
model.summary()
以下是模型摘要：
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 128)] 0
__________________________________________________________________________________________________
custom_layer (CustomLayer) (None, 137) 450 input_1[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 128) 17664 custom_layer[0][0]
==================================================================================================
Total params: 18,114
Trainable params: 18,114
Non-trainable params: 0
有关更多信息，请参阅tf.keras.layers.Layer documentation。
如果要参考表查找问题和解决方案，请参见以下链接：
答案 1 :(得分：1)
尽管Jithin Jees的答案非常清楚，但下面显示的是使用Concatenate操作的变通办法，
import tensorflow as tf
num_uids = 50
#input_uid = tf.keras.layers.Input(shape=(1,), dtype=tf.int32, batch_size = 32)
#input_uid = tf.keras.layers.Input(shape=(1,), dtype=tf.int32)
#params = tf.Variable(tf.random.normal((num_uids, 9)), trainable=True)
#param = tf.gather_nd(params, input_uid)
indices = tf.keras.layers.Input(name='indices', shape=(), dtype='int32')
params = tf.Variable(params)
class GatherLayer(tf.keras.layers.Layer):
def call(self, indices, params):
return tf.gather(params, indices)
output = GatherLayer()(indices, params)
#input_shared_features = tf.keras.layers.Input(shape=(128,), dtype=tf.float32, batch_size = 32)
input_shared_features = tf.keras.layers.Input(shape=(128,), dtype=tf.float32)
combined = tf.concat([output, input_shared_features], axis=-1)
net = tf.keras.layers.Dense(128)(combined)
有关更多详细信息，请参阅此Github Issue。