Question

我正在用LSTM训练Tensorflow模型以进行预测性维护。对于每个实例，我创建一个矩阵（50,4），其中50是历史序列的长度，而4是每个记录的特征数，因此为了训练模型，我使用例如（55048，50，4）张量和（55048，1）作为标签。当我在计算机上使用Jupyter进行训练时，它可以运行（非常慢，但是可以），但是在Colab上，我会收到此错误：

Exception has occured: ConnectionRefusedError [Errno 111] Connection refused

我与您分享一些代码。我知道这很长：


Training data shape is (55048, 50, 4)
Labels shape is (55048, 1)
WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 50, 100)           42000     
_________________________________________________________________
dense (Dense)                (None, 50, 1)             101       
=================================================================
Total params: 42,101
Trainable params: 42,101
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run  **
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:543 train_step  **
        self.compiled_metrics.update_state(y, y_pred, sample_weight)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:406 update_state
        metric_obj.update_state(y_t, y_p)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
        update_op = update_state_fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:2083 update_state
        label_weights=label_weights)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:351 update_confusion_matrix_variables
        y_pred.shape.assert_is_compatible_with(y_true.shape)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 50) and (None, 1) are incompatible

为什么它在Jupyter中起作用而在Colab中不起作用？感谢您的关注。

Answer 1

就我而言，我卸载了tensorflow，然后安装了tensorflow-gpu，问题就解决了

Answer 2

我已经在将运行时设置为GPU。如果我将最后一层不是一个节点的密集层（用于二进制分类），而是一个节点的LSTM层作为最后一层，则它可以工作。也许是因为LSTM和Dense不应该混合使用。谢谢您的答复。

ValueError：形状（无，50）和（无，1）在Tensorflow和Colab中不兼容

2 个答案: