globalaveragepooling1d 参数globalaveragepooling1d 参数 GlobalAveragePooling1d 是 PyTorch 中用于一维全局平均池化的层。它没有可学习的参数,因此没有需要用户输入的参数。该层的作用是在一维输入张量的每个通道上进行全局平均池化。 以下是使用 GlobalAveragePooling1d 的基本示例: python import torch.nn as nn # ...
The Average Pooling 1D Layer block performs downsampling by dividing the input into 1-D pooling regions, then computing the average of each region.
Create a AveragePooling1D Layer. Parameters: pool_size (Union[int, Sequence[int]], optional, default=2) – The size of pooling window. strides (Sequence[int], optional) – The stride of pooling window. padding (Union[int, Sequence[int], str], optional, default='valid') – The padding...
Deep Learning Toolbox / Deep Learning Layers / Pooling Layers Description TheGlobal Average Pooling 1D Layerblock performs downsampling by outputting the average of the time or spatial dimensions of the input. The dimension that the layer pools over depends on the layer input: ...
我自己也在学习ML，所以这只是我对GlobalAveragePooling1D的理解。理解这个例子的关键是你引用的段落上方的...
layer_test(convolutional.AveragePooling1D, kwargs={'stride': stride,'border_mode':'valid'}, input_shape=(3,5,4)) 开发者ID:GeekLiB,项目名称:keras,代码行数:8,代码来源:test_convolutional.py 示例3: build_hcnn_model ▲点赞 2▼ # 需要导入模块: from keras.layers import convolutional [as 别...
(keras.layers.Embedding(vocab_size, 16)) #GlobalAveragePooling1D层通过对序列维度求平均值,针对每个样本返回一个长度固定的输出向量。这样,模型便能够以尽可能简单的方式处理各种长度的输入。 model.add(keras.layers.GlobalAveragePooling1D()) model.add ...
tf.keras.layers.GlobalAveragePooling1D.apply apply( inputs, *args, **kwargs ) Apply the layer on a input. This is an alias of self.__call__. Arguments: inputs: Input tensor(s). *args: additional positional arguments to be passed to self.call. **kwargs: additional ke...
设 F=R F = R \mathbb F=\mathbb R 或 C, C , \mathbb C, 对于任意两个 Fn×n F...
获取shape import tensorflow as tf tensor = tf.placeholder(dtype=tf.float32, shape=[200, 200, ...