Question

我试图建立一个用于图像分割的Unet卷积神经网络，但是当我尝试使用输入数据编译该模型时，出现了形状不兼容的错误消息。


int main()
{
    int num1, num2, i, GCD;
   
    printf("Please enter a positive integer: \n");
    scanf("%d" ,&num1);
    printf("Please enter a positive integer:\n");
    scanf("%d", &num2);
if (num1 >0 && num2 > 0) {
    for(i = 1; i <= num1 && i <= num2; i++)
    {
        if(num1 % i == 0 && num2 % i == 0)
            GCD = i;
    }
}
else {
   printf("I'm sorry that number is unrecognized or negative.\n");

}
printf("The largest integer that divides both %d and %d is: %d\n",num1,num2, GCD);

    return 0;
}

当我已经检查了所有输入形状是否匹配时，问题出在什么地方？被忽略的是什么以及如何解决？

我已经尝试过

print(x_data.shape)
print(x_test.shape)
print(y_data.shape)
print(y_test.shape)

>>
(4, 767, 1022, 3)
(4, 767, 1022, 3)
(4, 767, 1022, 3)
(4, 767, 1022, 3)

>>>>
model = sm.Unet('resnet34', classes=1, activation='sigmoid')

model.compile(
    'Adam',
    loss=sm.losses.bce_jaccard_loss,
    metrics=[sm.metrics.iou_score],
)

>>>>
model.fit(
   x=x_data,
   y=y_data,
   batch_size=16,
   epochs=100,
   validation_data=(x_test, y_test),
)

>>
Epoch 1/100
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-27-6cf659e4ef4f> in <module>()
      4    batch_size=16,
      5    epochs=100,
----> 6    validation_data=(x_test, y_test),
      7 )

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 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:2585 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:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py:183 call
        return self._merge_function(inputs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py:522 _merge_function
        return K.concatenate(inputs, axis=self.axis)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:2881 concatenate
        return array_ops.concat([to_dense(x) for x in tensors], axis)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py:1654 concat
        return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py:1222 concat_v2
        "ConcatV2", values=values, axis=axis, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:744 _apply_op_helper
        attrs=attr_protos, op_def=op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:593 _create_op_internal
        compute_device)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:3485 _create_op_internal
        op_def=op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1975 __init__
        control_input_ops, op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1815 _create_c_op
        raise ValueError(str(e))

    ValueError: Dimension 2 in both shapes must be equal, but are 512 and 511. Shapes are [?,384,512] and [?,384,511]. for '{{node functional_3/decoder_stage3_concat/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](functional_3/decoder_stage3_upsampling/resize/ResizeNearestNeighbor, functional_3/relu0/Relu, functional_3/decoder_stage3_concat/concat/axis)' with input shapes: [?,384,512,64], [?,384,511,64], [] and with computed input tensors: input[2] = <3>.

如此处https://github.com/titu1994/Image-Super-Resolution/issues/27所示，但问题仍然存在。

使用Google Colab。

Answer 1

聚会有点晚了，但是您的问题来自输入宽度和高度不能被 32 整除的事实；确保您对 UNet 使用可被 32 整除的值，您的问题将得到解决。

您无需更改 Colab 环境或将频道顺序设置为 <ul> <li>School</li> <li>University</li> <li>Kindergarden</li> </ul>。

ValueError：两个形状中的尺寸2必须相等，但分别为512和511。形状分别为[？，384,512]和[？，384,511]

1 个答案: