问题描述
如何将调整大小图层添加到
How can I add a resizing layer to
model = Sequential()
使用
model.add(...)
要将图像的形状从形状(160、320、3)调整为(224,224,3)吗?
To resize an image from shape (160, 320, 3) to (224,224,3) ?
推荐答案
通常，您会使用> c0> 层:
model.add(Reshape((224,224,3), input_shape=(160,320,3))
，但是由于您的目标维度不允许保存来自输入维度(224*224 != 160*320)的所有数据，因此无法使用.如果元素数不变，则只能使用Reshape.
but since your target dimensions don't allow to hold all the data from the input dimensions (224*224 != 160*320), this won't work. You can only use Reshape if the number of elements does not change.
如果可以丢失图像中的某些数据，则可以指定自己的有损重塑:
If you are fine with losing some data in your image, you can specify your own lossy reshape:
model.add(Reshape(-1,3), input_shape=(160,320,3))
model.add(Lambda(lambda x: x[:50176])) # throw away some, so that #data = 224^2
model.add(Reshape(224,224,3))
也就是说，通常这些变换是在将数据应用于模型之前完成的，因为如果在每个训练步骤中进行操作，这实际上是在浪费计算时间.
That said, often these transforms are done before applying the data to the model because this is essentially wasted computation time if done in every training step.
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