解读MaxPooling1D和GlobalMaxPooling1D的区别

MaxPooling1D和GlobalMaxPooling1D区别

import tensorflow as tf

from tensorflow import keras
input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
print(x)

y=keras.layers.GlobalMaxPool1D()(x)
print("*"*20)

print(y)
'''
 """Global average pooling operation for temporal data.

Examples:

>>> input_shape = (2, 3, 4)
 >>> x = tf.random.normal(input_shape)
 >>> y = tf.keras.layers.GlobalAveragePooling1D()(x)
 >>> print(y.shape)
 (2, 4)

Arguments:
   data_format: A string,
     one of `channels_last` (default) or `channels_first`.
     The ordering of the dimensions in the inputs.
     `channels_last` corresponds to inputs with shape
     `(batch, steps, features)` while `channels_first`
     corresponds to inputs with shape
     `(batch, features, steps)`.

Call arguments:
   inputs: A 3D tensor.
   mask: Binary tensor of shape `(batch_size, steps)` indicating whether
     a given step should be masked (excluded from the average).

Input shape:
   - If `data_format='channels_last'`:
     3D tensor with shape:
     `(batch_size, steps, features)`
   - If `data_format='channels_first'`:
     3D tensor with shape:
     `(batch_size, features, steps)`

Output shape:
   2D tensor with shape `(batch_size, features)`.
 """
'''

print("--"*20)

input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
print(x)

y=keras.layers.MaxPool1D(pool_size=2,strides=1)(x)  # strides 不指定 默认等于 pool_size
print("*"*20)

print(y)

输出如下图

上图GlobalMaxPool1D 相当于给每一个样本每列的最大值

而MaxPool1D就是普通的对每一个样本进行一个窗口（1D是一维列窗口）滑动取最大值。

tf.keras.layers.GlobalMaxPool1D()

与tf.keras.layers.Conv1D的输入一样，输入一个三维数据(batch_size,feature_size,output_dimension)

x = tf.constant([[1., 2., 3.], [4., 5., 6.]])
x = tf.reshape(x, [2, 3, 1])
max_pool_1d=tf.keras.layers.GlobalMaxPooling1D()
max_pool_1d(x)

其中max_pool_1d(x)和tf.math.reduce_max(x,axis=-2,keepdims=False)作用相同

来源：https://blog.csdn.net/qq_38574975/article/details/111468756