1,gather是不规则的切片提取算子(Gathers values along an axis specified by dim. 在指定维度上根据索引 index 来选取数据)。函数定义如下: torch.gather(input,dim,index,*,sparse_grad=False,out=None)→Tensor 参数解释: +input(Tensor) – the source tensor. +dim(int) – the axis along which to ind...
apply_(callable)→ Tensor Applies the function callable to each element in the tensor, replacing each element with the value returned by callable. Note This function only works with CPU tensors and should not be used in code sections that require high performance. argmax(dim=None, keepdim=Fal...
transpose(axis, -1) return specgrams [docs]def mask_along_axis( specgram: Tensor, mask_param: int, mask_value: float, axis: int ) -> Tensor: r""" Apply a mask along ``axis``. Mask will be applied from indices ``[v_0, v_0 + v)``, where ``v`` is sampled from ``...
mask = tf.greater_equal(score, tf.constant(score_thresh)) # Step 2: Do non_max_suppression for each class for i in range(num_classes): # Step 3: Apply the mask to scores, boxes and pick them out filter_boxes = tf.boolean_mask(boxes, mask[:, i]) filter_score = tf.boolean_mask...
LSTM(Long Short Tem Memory)特殊递归神经网络,神经元保存历史记忆,解决自然语言处理统计方法只能考虑最近n个词语而忽略更久前词语的问题。用途:word representation(embedding)(词语向量)、sequence to sequence learning(输入句子预测句子)、机器翻译、语音识别等。 100多行原始python代码实现基于LSTM二进制加法器。https:...
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Tensor.apply_: lambda self, callable: -1, Tensor.as_strided: lambda self, size, stride: -1, Tensor.as_strided_: lambda self, size, stride: -1, Tensor.backward: lambda self, gradient=None, retain_graph=None, create_graph=False, inputs=None: -1, Tensor.bfloat16: lambda self,...
std(sample_s, axis=0) Example #21Source File: fast_text.py From TaskBot with GNU General Public License v3.0 5 votes def forward(self, x): x = self.embed(x) x = torch.mean(x, dim=1, keepdim=False) x = self.dropout(x) output = self.fc(x) output = F.log_softmax(output...
Currently, however, most commercially available systems make use of an emission observation along the axis of the torch (so called “axial viewing”). Recently commercial instruments tend to combine the “axial” view and “radial” view configurations into a single unit, known as dual view. ...
np.array([range(largest_number)],dtype=np.uint8).T,axis=1) for i in range(largest_number): int2binary[i] = binary[i] 预先把整数到二进制转换词典存起来。 alpha = 0.1 input_dim = 2 hidden_dim = 16 output_dim = 1 设置参数,alpha是学习速度,input_dim是输入层向量维度,输入a、b两个数...