我目前正在TensorFlow（和Keras）中使用RNN生成运动对象数据。我的RNN模型定义如下：
if tf.test.is_gpu_available():
my_gru = tf.keras.layers.CuDNNGRU
else:
import functools
my_gru = functools.partial(
tf.keras.layers.GRU, recurrent_activation='sigmoid')
def build_model(internal_units, batch_size):
model = tf.keras.Sequential([
my_gru(internal_units, return_sequences=True, recurrent_initializer='glorot_uniform', stateful=True, batch_input_shape=[batch_size, None, 3]),
tf.keras.layers.Dense(3)
])
return model
INTERNAL_UNITS = 1024
model = build_model(internal_units=INTERNAL_UNITS, batch_size=BATCH_SIZE)
我得到了该模型的以下摘要，这些摘要很好：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
cu_dnngru_2 (CuDNNGRU) (64, None, 1024) 3161088
_________________________________________________________________
dense_4 (Dense) (64, None, 3) 3075
=================================================================
Total params: 3,164,163
Trainable params: 3,164,163
Non-trainable params: 0
_________________________________________________________________
当我尝试在训练之前对模型进行单个预测时，只是为了查看它是否正常工作并且输出具有正确的形状，就会出现错误。
这就是我运行模型的方式...
for input_example_batch, target_example_batch in dataset.take(1):
example_batch_predictions = model(input_example_batch)
print(example_batch_predictions.shape, "# (batch_size, sequence_length, n_features)")
...这是我得到的错误...
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-52-dd1ea240c0d1> in <module>()
1 for input_example_batch, target_example_batch in dataset.take(1):
----> 2 example_batch_predictions = model(input_example_batch)
3 print(example_batch_predictions.shape, "# (batch_size, sequence_length, n_features)")
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
755 if not in_deferred_mode:
756 self._in_call = True
--> 757 outputs = self.call(inputs, *args, **kwargs)
758 self._in_call = False
759 if outputs is None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py in call(self, inputs, training, mask)
227 def call(self, inputs, training=None, mask=None):
228 if self._is_graph_network:
--> 229 return super(Sequential, self).call(inputs, training=training, mask=mask)
230
231 outputs, _ = self._call_and_compute_mask(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in call(self, inputs, training, mask)
843 outputs, _ = self._run_internal_graph(inputs,
844 training=training,
--> 845 mask=masks)
846 return outputs
847
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _run_internal_graph(self, inputs, training, mask)
1029 computed_tensor, **kwargs)
1030 else:
-> 1031 output_tensors = layer.call(computed_tensor, **kwargs)
1032 if hasattr(layer, 'compute_mask'):
1033 output_masks = layer.compute_mask(computed_tensor,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py in call(self, inputs, mask, training, initial_state)
107 # Reverse time axis.
108 inputs = K.reverse(inputs, 1)
--> 109 output, states = self._process_batch(inputs, initial_state)
110
111 if self.stateful:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py in _process_batch(self, inputs, initial_state)
297 params=params,
298 is_training=True,
--> 299 rnn_mode='gru')
300
301 if self.stateful or self.return_state:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_cudnn_rnn_ops.py in cudnn_rnn(input, input_h, input_c, params, rnn_mode, input_mode, direction, dropout, seed, seed2, is_training, name)
142 input_mode=input_mode, direction=direction, dropout=dropout,
143 seed=seed, seed2=seed2, is_training=is_training, name=name,
--> 144 ctx=_ctx)
145 except _core._NotOkStatusException as e:
146 if name is not None:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_cudnn_rnn_ops.py in cudnn_rnn_eager_fallback(input, input_h, input_c, params, rnn_mode, input_mode, direction, dropout, seed, seed2, is_training, name, ctx)
184 "is_training", is_training)
185 _result = _execute.execute(b"CudnnRNN", 4, inputs=_inputs_flat,
--> 186 attrs=_attrs, ctx=_ctx, name=name)
187 _execute.record_gradient(
188 "CudnnRNN", _inputs_flat, _attrs, _result, name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
64 else:
65 message = e.message
---> 66 six.raise_from(core._status_to_exception(e.code, message), None)
67 # pylint: enable=protected-access
68 return tensors
/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: cannot compute CudnnRNN as input #1(zero-based) was expected to be a double tensor but is a float tensor [Op:CudnnRNN]
我使用的输入（input_example_batch）如下：
tf.Tensor(
[[[ 1.04234461 4.66466794 -4.32528214]
[ 1.04244826 4.49530966 -4.42294239]
[ 1.04256889 4.47099585 -4.51911731]
...
[ 1.05549699 -0.64915764 0.67451403]
[ 1.05559893 -0.7788313 0.66355975]
[ 1.05570257 -0.88502956 0.65223413]]
[[-0.83849063 -0.47476892 -0.22299478]
[-0.8383632 -0.41943403 -0.22782209]
[-0.83830883 -0.41635987 -0.24527468]
...
[-0.82015615 0.05482504 0.681198 ]
[-0.8200627 -0.04187127 0.69233796]
[-0.81995906 -0.13493448 0.66615907]]
[[-1.27264128 0.12441285 -0.48032767]
[-1.27256143 0.29544794 -0.714081 ]
[-1.27247477 0.46871879 -0.97197089]
...
[-1.25435437 3.47832158 0.72724314]
[-1.25427451 3.90674772 0.63255355]
[-1.25418956 4.28934093 0.49219015]]
...
[[ 1.01251773 1.16012535 -2.9933152 ]
[ 1.01263666 1.16906836 -2.99554319]
[ 1.0127505 0.99943062 -2.95135471]
...
[ 1.02650943 -0.42362607 0.46805359]
[ 1.02661308 -0.49377282 0.42702143]
[ 1.02666575 -0.49544963 0.42739276]]
[[-1.34049978 -0.01029126 0.5430626 ]
[-1.34038934 -0.04690171 0.52078269]
[-1.34029419 0.07857991 0.52895199]
...
[-1.3289751 1.94152097 1.03489148]
[-1.32888165 1.51113855 0.94372954]
[-1.32878311 1.16208164 0.76511898]]
[[ 0.57716585 -1.38779448 0.0469634 ]
[ 0.57728308 -1.44536512 -0.01542033]
[ 0.57734255 -1.49846425 -0.02581762]
...
[ 0.58594146 -0.63183055 -0.03621491]
[ 0.58603491 -0.60136842 -0.04419854]
[ 0.58613855 -0.63015374 -0.07204842]]], shape=(64, 100, 3), dtype=float64)
所有这些都在Google Colab环境中运行。
我已经试图强迫它使用普通的GRU层，这会导致几乎相同的错误。但是，与普通的GRU层相比，一个显着的区别是它声称期望的是浮动张量而不是双张量（恰好相反，这是我复制到本文中的错误）。
我的整个代码基本上与example given on the TensorFlow website相同。我对模型进行了少许修改，并将输入数据更改为具有3个特征，而不是具有与字母较长一样多的特征。原始的TensorFlow示例对我来说很好用。
请原谅冗长的帖子，并询问更多信息。我试图做到彻底，但是你永远都不知道。感谢您的帮助。
编辑：
Here，您可以在Google Colab上找到一个简短的示例Notebook，可用于自己重现错误。
答案 0 :(得分：0)
因此，我找到了一个似乎对我有用的解决方案。当我调用model.input时，得到以下输出，表明GRU层要求其输入为浮点张量。
<DeferredTensor 'gru_input' shape=(64, ?, 3) dtype=float32>
使用线路
x = tf.cast(x, dtype=tf.float32)
（TensorFlow documentation）我可以将输入数据转换为浮点张量。使用此浮动张量，模型可以正常工作。
我希望这对遇到相同问题的人有所帮助。根据{{3}} float64，兼容性是TensorFlow的一个已知问题。