MaxPooling1D是一种在1D卷积神经网络中常用的池化技术。池化层的作用是减少数据的维度,同时保留最重要的特征。 在MaxPooling1D中,pool_size参数决定了池化窗口的大小。当pool_size=2时,池化窗口会在1D输入张量的时间维度上滑动,每次取窗口内的最大值作为输出的一部分。 具体来说,假设输入张量的大小为T,经
super(MaxPool3d_modify, self).__init__() self.kernel_size=kernel_size self.stride=stride self.padding=padding self.max_pool_2d= torch.nn.MaxPool2d(kernel_size[1:], self.stride[1:], padding[1:]) self.max_pool_1d= torch.nn.MaxPool1d(kernel_size=kernel_size[0], stride=self.stride...
pool_size: Integer, size of the max pooling windows. strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default topool_size. padding: One of"valid"or"same"(case-insensitive). ...
针对你提到的“max_pool1d() invalid computed output size: 0”错误,我们可以从以下几个方面进行分析和解决: 检查max_pool1d()函数的输入参数: 确保你传递给max_pool1d()函数的参数是合理的。主要参数包括kernel_size(池化窗口大小)、stride(步长)、padding(填充)和dilation(膨胀率)。这些参数必须为正整数,并且...
self.max_pool_2d = torch.nn.MaxPool2d(kernel_size[1:], self.stride[1:], padding[1:]) self.max_pool_1d = torch.nn.MaxPool1d(kernel_size=kernel_size[0], stride=self.stride[0], padding=self.padding[0]) # stride is kernal_size ...
我想在特征(最后一个维度)上做COnv1D和MaxPool1D，我指定输入有4个维度，这对Conv1D很好，但用MaxPool...
问从MaxPool1D层到Conv1D层的额外维数EN导语 | 伴随着Snowflake的成功，重新激活了数据分析市场，大大...
🐛 Describe the bug The doc of nn.MaxPool1d() says kernel_size, stride, padding and dilation argument are int or tuple of int) as shown below: Parameters kernel_size (Union[int, Tuple[int]]) – The size of the sliding window, must be > 0. ...
import torch m = torch.nn.MaxPool1d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=True) input = torch.randn(20, 16, 1) output = m(input) As for this reported case, the correct output shape should be (20, 16, 1) if we calculate it by hand under ceil_mode=True...
185 CHECK_LE(input_index + kernel_size_, inputs[0].cols());186 for (int i = 0; i < output_num_row; ++i) {187 float output_i_j = -std::numeric_limits<float>::infinity();188 for (int k = input_index; k < input_index + kernel_size_; ++k) {189 output_i_j = std::...