Input 0 of layer "resnet50" is incompatible with the layer: expected shape=(None, 224, 224, 3), found shape=(None, 244, 244, 3)

引用\[1\]:ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: \[None, 784\]。引用\[2\]:卷积神经网络训练三个概念（epoch，迭代次数，batchsize）3 网络问题集 3.1 incompatible with the layer 这个是我在搭建Resnet+FPN的时候，抛出的异常self.latlayer = Sequential(\[ layers.Conv2D(filters=256, kernel_size=1, strides=1, padding='valid'), layers.BatchNormalization(), layers.Activation('relu') \]) File "D:\Python36\lib\site-packages\tensorflow_core\python\keras\engine\input_spec.py", line 213, in assert_input_compatibility ' but received input with shape ' + str(shape)) ValueError: Input 0 of layer sequential_10 is incompatible with the layer: expected axis -1 of input shape to have value 256 but received input with shape \[None, 28, 28, 128\]。引用\[3\]:迁移学习 ValueError: Input 0 of layer global_average_pooling2d is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: \[None, 1000\]。

这些错误信息表明在神经网络的搭建和训练过程中出现了输入维度不匹配的问题。在引用\[1\]中，卷积层的输入维度应该是4维的，但实际输入的维度是2维的。在引用\[2\]中，期望的输入维度的最后一个轴应该是256，但实际输入的维度是\[None, 28, 28, 128\]。在引用\[3\]中，全局平均池化层的期望输入维度是4维的，但实际输入的维度是2维的。

要解决这些问题，你需要检查网络的输入和各层的参数设置是否匹配。确保输入的维度与网络模型的期望输入维度一致。如果需要调整输入的维度，可以使用相应的预处理方法进行调整。此外，还需要检查各层的参数设置是否正确，包括卷积核大小、步长、填充方式等。

另外，还需要注意在迁移学习中，要根据具体的模型和任务设置合适的参数，确保输入和输出的维度匹配。在引用\[3\]中的问题中，期望的输入维度是(None, 224, 224, 3)，但实际输入的维度是(None, 244, 244, 3)，因此需要调整输入的尺寸以匹配模型的期望输入维度。

总之，要解决这些错误，需要仔细检查网络的输入和各层的参数设置，确保它们的维度匹配。
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