Tensors and Dynamic neural networks in Python with strong GPU acceleration - torch.argmax() returning different indices to numpy.argmax() when the element values are the same · pytorch/pytorch@103b6cc
最大数是4 t.argmax() ##tensor(11) 最大数值的index是11 t.flatten() ##tensor([1., 0., 0., 2., 0., 3., 3., 0., 4., 0., 0., 5.]) argmax就是平铺之后的数值 t.max(dim=0) #torch.return_types.max( #values=tensor([4., 3., 3., 5.]), #indices=tensor([2, 1,...
🐛 Bug torch.argmax() returning different indices to numpy.argmax() when the element values are the same. To Reproduce Code: import torch import numpy as np arr1 = np.array([1,2,3]) arr2 = np.array([5,10,3]) my_list_arr = [arr1, arr2] A =...
valuesis the maximum value ofeach rowof theinputtensorin the given dimensiondim. Andindicesis the index location of each maximum value found (argmax). IfkeepdimisTrue, the output tensors are of the same size asinputexcept in the dimensiondimwhere they are of size 1. Otherwise,dimis squeeze...
This is equivalent to self.log_pob(input).argmax(dim=1), but is more efficient in some cases. Parameters input (Tensor)– a minibatch of examples Returns a class with the highest probability for each example Return type output (Tensor) Shape: Normalization layers BatchNorm1d class torch.nn...
And indices is the index location of each maximum value found (argmax). If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors ...
y_pred = tf.argmax(y_pred) values = tf.equal(tf.cast(y_true, 'int32'), tf.cast(y_pred, 'int32')) values = tf.cast(values, 'float32') if sample_weight is not None: sample_weight = tf.cast(sample_weight, 'float32') ...
argmax(probabilities, dim=1) #将predicted_classes转换为one-hot编码格式 one_hot_predictions = F.one_hot(predicted_classes, num_classes=probabilities.size(1)) # 使用torch.gather提取每行的正确预测结果的概率 correct_probabilities = torch.gather(probabilities, 1, one_hot_predictions.nonzero(as_tuple...
(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor #线性内插相当于将位置序号等比例缩小 freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same ...
(model=quantized_model, device_name="CPU")output_layer = compiled_quantized_model.outputs[0]result = compiled_quantized_model(inputs)[output_layer]result = np.argmax(result)print(f"Text 1:{sample['sentence1']}")print(f"Text 2:{sample['sentence2']}")print(f"The same meaning:{'yes'...