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"Runtimeerror: bool value of tensor with more than one value is ambiguous" fastai


"Runtimeerror: bool value of tensor with more than one value is ambiguous" fastai

By : Belkacem Benfares
Date : October 01 2020, 02:00 AM
I wish this helpful for you The problem is probably because your tensor contains more than 1 bool values, which will lead to an error when doing logical operation (and, or). For example,
code :
>>> import torch
>>> a = torch.zeros(2)
>>> b = torch.ones(2)
>>> a == b
tensor([False, False])
>>> a == 0
tensor([True, True])
>>> a == 0 and True
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: bool value of Tensor with more than one value is ambiguous
>>> if a == b:
...     print (a)
... 
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: bool value of Tensor with more than one value is ambiguous
>>> (a != b) & (a == b)
tensor([False, False])

>>> mask = (a != b) & (a == b)
>>> c = torch.rand(2)
>>> c[mask]
tensor([])


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Ambiguous behavior of using "not" and "bool" together on an empty list in Python

Ambiguous behavior of using "not" and "bool" together on an empty list in Python


By : mianmian
Date : March 29 2020, 07:55 AM
Any of those help In this simple implementation of Stack using Python lists. I have a small method to check if the stack is empty (list empty). I am using this method as a check condition for pop method. The pop method should raise an exception when trying to pop an empty stack. For some reason, this doesn't seem to work. Am I missing something here? Please help!
code :
if self.is_empty:
if self.is_empty():
if not self.is_empty():
dynamic_rnn gives "`tf.Tensor` as a Python `bool`" error when layer_norm (in LayerNormBasicLSTMCell) is a Tens

dynamic_rnn gives "`tf.Tensor` as a Python `bool`" error when layer_norm (in LayerNormBasicLSTMCell) is a Tens


By : Ginosval
Date : March 29 2020, 07:55 AM
To fix this issue The tf.contrib.rnn.LayerNormBasicLSTMCell initializer expects a Python boolean and not a tf.Tensor as the layer_norm argument. The reason for this is that the value of this argument needs to be known at graph construction time, in order to create the appropriate variables for layer normalization (e.g. the "gamma" and "beta" variables created here).
PyTorch - RuntimeError: bool value of Tensor with more than one value is ambiguous

PyTorch - RuntimeError: bool value of Tensor with more than one value is ambiguous


By : Braveatom
Date : March 29 2020, 07:55 AM
it helps some times I think I got the problem.
Variable is a name reserved in torch and tkinter. If you are doing from ... import * you may get Variable from tkinter. Since the error is comming from this line, the Variable in your code is from tkinter. However, since you are calling it with a Tensor inside, I'm guessing that you wanted the deprecated version of torch's Variable.
code :
def create_noise(b):
    return torch.zeros(b, feature_space, 1, 1).normal_(0, 1)
Error when training CNN: "RuntimeError: The size of tensor a (10) must match the size of tensor b (64) at non-singl

Error when training CNN: "RuntimeError: The size of tensor a (10) must match the size of tensor b (64) at non-singl


By : Caleb Uran
Date : March 29 2020, 07:55 AM
seems to work fine The loss you're using (nn.MSELoss) is incorrect for this problem. You should use use nn.CrossEntropyLoss.
Mean Squared Loss measures the mean squared error between input x and target y. Here the input and target naturally should be of the same shape.
pytorch: RuntimeError: bool value of Tensor with more than one value is ambiguous

pytorch: RuntimeError: bool value of Tensor with more than one value is ambiguous


By : rebecca
Date : September 22 2020, 06:00 AM
will be helpful for those in need it works with , Just a syntax thing.
code :
x = torch.randn((1,3,20,20))
x[(x > 0) & (x < 1)] = 1
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