InvalidArgumentError:输出形状的内部尺寸必须与更新形状的内部尺寸相匹配
InvalidArgumentError:输出形状的内部尺寸必须与更新形状的内部尺寸相匹配
提问于 2021-05-22 18:22:59
我正试图在keras中实现SPL损失。我所需要做的就是非常简单，我将用numpy来解释我需要什么：
def spl_loss(y_true, y_pred, lmda):
# compute any arbitrary loss function
L = categorical_cross_entropy(y_true, y_pred)
# set to zero those values with an error greater than lambda
L[L>lmda] = 0
return L我正在尝试实现它遵循本教程，但是我在将值设置为零所需的步骤上遇到了麻烦。
目前，我有以下代码：
def spl_loss(lmda, loss_fn):
def loss(y_true, y_pred):
# compute an arbitrary loss function, L
loss_value = loss_fn(y_true, y_pred) # tensor of shape (64,)
# get the mask of L greater than lmda
mask = tf.greater( loss_value, tf.constant( float(lmda) ) ) # tensor of shape (64,)
# compute indexes for the mask
indexes = tf.reshape(tf.where(mask), [-1]) # tensor of shape (n,); where n<=64
# set to zero values on indexes
spl_loss_value = tf.tensor_scatter_nd_update(loss_value, indexes, tf.zeros_like(loss_value, dtype=loss_value.dtype) ) # this line gives the error
return spl_loss_value
return loss根据文档，tensor_scatter_nd_update操作应该执行赋值操作，但如果出现以下错误，则会失败：
spl_loss_value = tf.tensor_scatter_nd_update(loss_value, indexes, tf.zeros_like(loss_value, dtype=loss_value.dtype) )
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper **
return target(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:5512 tensor_scatter_nd_update
tensor=tensor, indices=indices, updates=updates, name=name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_array_ops.py:11236 tensor_scatter_update
_ops.raise_from_not_ok_status(e, name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py:6862 raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
<string>:3 raise_from
InvalidArgumentError: Inner dimensions of output shape must match inner dimensions of updates shape. Output: [64] updates: [64] [Op:TensorScatterUpdate]我正在colab运行，这里你可以试试。
我尝试了几次重新塑造，因为我知道这是一个期待的形状与获得的问题，但我找不到方法。这里发生了什么事？
提前感谢
回答 1
Stack Overflow用户
回答已采纳
发布于 2021-05-22 21:00:28
出现此错误的原因是，indices in tf.tensor_scatter_nd_update至少需要两轴，或者tf.rank(indices) > = 2需要完全填充。indices在2D中(在标量更新中)的原因是保存两个信息，一个是更新 (num_updates)的长度和索引向量的长度。有关此问题的详细概述，请参阅以下有关此问题的答案：最新消息？。
以下是SPL丢失在tensorflow中的正确实现。
def spl_loss(lmda):
def loss(y_true, y_pred):
# compute an arbitrary loss function, L
loss_value = keras.losses.sparse_categorical_crossentropy(y_true, y_pred)
# get the mask of L greater than lmda
mask = tf.greater( loss_value, tf.constant(float(lmda) ) )
# compute indexes for the mask
indexes = tf.where(mask) # tensor of shape (n,); where n<=64
updates = tf.reshape(tf.zeros_like(indexes, dtype=tf.float32), [-1])
# scaler update check
num_updates, index_depth = indexes.shape.as_list()
assert updates.shape == [num_updates]
assert index_depth == tf.rank(loss_value)
# print()
# print('A', tf.reshape(tf.where(mask), [-1])[:10].numpy())
# print('B', tf.where(mask).numpy()[:10])
# print('Ranks: ', tf.rank(loss_value).numpy(),
# tf.rank(indices).numpy(),
# tf.rank(updates).numpy())
# print('Shape: ', loss_value.shape, indexes.shape, updates.shape)
# set to zero values on indexes
spl_loss_value = tf.tensor_scatter_nd_update(loss_value, indexes, updates )
return spl_loss_value
return loss...
model.compile(optimizer="adam", loss=spl_loss(lmda=2.), run_eagerly=True)
...参考文献：更新
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原文链接：
https://stackoverflow.com/questions/67652872
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