问题描述
我有一个满足我需要的损失函数,但仅在PyTorch中可用.我需要将其实施到我的TensorFlow代码中,但是尽管其中的大多数可以平凡地翻译"为我被困在特定的行中:
I've got a loss function that fulfills my needs, but is only in PyTorch. I need to implement it into my TensorFlow code, but while most of it can trivially be "translated" I am stuck with a particular line:
y_hat[:, torch.arange(N), torch.arange(N)] = torch.finfo(y_hat.dtype).max # to be "1" after sigmoid
您可以在下面看到整个代码,除了那一行外,它确实很简单:
You can see the whole code in following and it is indeed pretty straight forward except for that line:
def get_loss(y_hat, y):
# No loss on diagonal
B, N, _ = y_hat.shape
y_hat[:, torch.arange(N), torch.arange(N)] = torch.finfo(y_hat.dtype).max # to be "1" after sigmoid
# calc loss
loss = F.binary_cross_entropy_with_logits(y_hat, y) # cross entropy
y_hat = torch.sigmoid(y_hat)
tp = (y_hat * y).sum(dim=(1, 2))
fn = ((1. - y_hat) * y).sum(dim=(1, 2))
fp = (y_hat * (1. - y)).sum(dim=(1, 2))
loss = loss - ((2 * tp) / (2 * tp + fp + fn + 1e-10)).sum() # fscore
return loss
到目前为止,我提出了以下建议:
So far I came up with following:
def get_loss(y_hat, y):
loss = tf.keras.losses.BinaryCrossentropy()(y_hat,y) # cross entropy (but no logits)
y_hat = tf.math.sigmoid(y_hat)
tp = tf.math.reduce_sum(tf.multiply(y_hat, y),[1,2])
fn = tf.math.reduce_sum((y - tf.multiply(y_hat, y)),[1,2])
fp = tf.math.reduce_sum((y_hat -tf.multiply(y_hat,y)),[1,2])
loss = loss - ((2 * tp) / tf.math.reduce_sum((2 * tp + fp + fn + 1e-10))) # fscore
return loss
所以我的问题归结为:
-
torch.finfo()
的作用以及如何在TensorFlow中表达它? -
y_hat.dtype
是否只返回数据类型?
- What does
torch.finfo()
do and how to express it in TensorFlow? - Does
y_hat.dtype
just return the data type?
推荐答案
1. torch.finfo()会做什么,以及如何在TensorFlow中表达它?
.finfo()
提供了一种巧妙的方法来获取浮点类型的机器限制. Numpy ,火炬以及 Tensorflow实验性.
1. What does torch.finfo() do and how to express it in TensorFlow?
.finfo()
provides a neat way to get machine limits for floating-point types. This function is available in Numpy, Torch as well as Tensorflow experimental.
.finfo().max
返回表示为dtype的最大可能数字.
.finfo().max
returns the largest possible number representable as that dtype.
注意:整数类型也有一个.iinfo()
.
NOTE: There is also a .iinfo()
for integer types.
以下是finfo
和iinfo
实际使用的一些示例.
Here are a few examples of finfo
and iinfo
in action.
print('FLOATS')
print('float16',torch.finfo(torch.float16).max)
print('float32',torch.finfo(torch.float32).max)
print('float64',torch.finfo(torch.float64).max)
print('')
print('INTEGERS')
print('int16',torch.iinfo(torch.int16).max)
print('int32',torch.iinfo(torch.int32).max)
print('int64',torch.iinfo(torch.int64).max)
FLOATS
float16 65504.0
float32 3.4028234663852886e+38
float64 1.7976931348623157e+308
INTEGERS
int16 32767
int32 2147483647
int64 9223372036854775807
如果要在tensorflow中实现此功能,可以使用tf.experimental.numpy.finfo
来解决.
If you want to implement this in tensorflow, you can use tf.experimental.numpy.finfo
to solve this.
print(tf.experimental.numpy.finfo(tf.float32))
print('Max ->',tf.experimental.numpy.finfo(tf.float32).max) #<---- THIS IS WHAT YOU WANT
Machine parameters for float32
---------------------------------------------------------------
precision = 6 resolution = 1.0000000e-06
machep = -23 eps = 1.1920929e-07
negep = -24 epsneg = 5.9604645e-08
minexp = -126 tiny = 1.1754944e-38
maxexp = 128 max = 3.4028235e+38
nexp = 8 min = -max
---------------------------------------------------------------
Max -> 3.4028235e+38
2. y_hat.dtype是否只返回数据类型?
是.
在割炬中,它将返回torch.float32
或类似的内容.在Tensorflow中,它将返回tf.float32
或类似的内容.
In torch, it would return torch.float32
or something like that. In Tensorflow it would return tf.float32
or something like that.
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