shape of t: <built-in method size of Tensor object at 0x0000015C0D9AFD60>什么问题
时间: 2024-03-07 17:52:10 浏览: 41
这个问题的原因是你在使用 PyTorch 的 Tensor 对象时,使用了错误的方法。`size()` 方法可以返回 Tensor 对象的形状,而 `size` 属性可以返回 Tensor 对象中包含的元素数目。因此,正确的方法应该是 `t.size()` 而不是 `<built-in method size of Tensor object at 0x0000015C0D9AFD60>`。
相关问题
Inputs to a layer should be tensors. Got: <keras.engine.input_layer.InputLayer object at 0x00000237B0D43FD0>
This error occurs because you are trying to pass an InputLayer object to a layer that expects a tensor as input.
To fix this error, you need to make sure that you are passing tensors as inputs to the layers in your model. This can be done by calling the `output` attribute of the InputLayer object, which returns a tensor that can be passed to the next layer in the model.
For example, instead of passing the InputLayer object directly to the next layer, you would do:
```
input_layer = Input(shape=(input_shape,))
x = input_layer.output
x = Dense(units=64, activation='relu')(x)
...
```
Here, `input_layer.output` returns a tensor that is then passed to the Dense layer.
Inputs to a layer should be tensors. Got: <keras.layers.convolutional.conv1d.Conv1D object at 0x0000025F1DA0D340>
This error occurs when you try to pass a Keras layer object (in this case, a Conv1D layer) as an input to a function or another layer that expects a tensor as input.
To resolve this error, you need to pass a tensor as input to the function or layer instead of the Keras layer object. You can either define an input layer using the Input() function in Keras or use a previously defined layer that outputs a tensor as input to the current layer.
For example, if you have a Conv1D layer and want to pass its output as input to a Dense layer, you can do the following:
```
input_layer = Input(shape=(input_shape))
conv_layer = Conv1D(filters=32, kernel_size=3)(input_layer)
dense_layer = Dense(units=64)(conv_layer)
```
Here, we first define an input layer using the Input() function, then pass it to the Conv1D layer. We then pass the output tensor of the Conv1D layer as input to the Dense layer.
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